How Important Was Silver?
Some Evidence on Exchange Rate Fluctuations and
Stock Returns in Colonial-Era Asia
Warren Bailey and Kirida Bhaopichitr *
First Draft: 14th May 1997
Current Revision: 12th April 1999
* Johnson Graduate School of Management, Cornell University, Sage Hall, Ithaca, NY 148536201, (607) 255-4627 and Department of Economics, Cornell University, Uris Hall, Ithaca, NY
14853. We thank Kevin Chang, Eric Jacquier, Swee-Sum Lam, Jim Lothian, Mike Montesano,
Allen Reidy, Lan Truong, Ingrid Werner, and seminar participants at University of Southern
California, University of California at Riverside, Hong Kong University of Science and
Technology, 1997 Pacific Basin Finance Conference in Shanghai, City University of Hong Kong,
and Ohio State University for helpful discussions, comments on earlier drafts, and other
assistance. We thank Serena Agora-Menyang, James Chan, Julie Goking, Gustavo Grullon, Blair
Kanbar, Ayu Listiowati, Greg Loukedes, Hodaka Morita, Lillibeth Ortiz, Jane P. Wu, and Gul
Yanmaz for research assistance. Special thanks to Peter Chung, Andrew Karolyi, Rene Stulz (the
editor), and an anonymous referee for extensive comments.
1998, 1999 Warren Bailey and Kirida Bhaopichitr.
How Important Was Silver?
Some Evidence on Exchange Rate Fluctuations and
Stock Returns in Colonial-Era Asia
Abstract
We study the impact of exchange rate fluctuations and exchange rate regime shifts on Asian stock
prices from 1873 to the eve of the Second World War. The seven small open economies in the
sample are particularly interesting as they are comparable to modern emerging markets and
because they abandoned a common currency, silver, at differing times. Some significant
associations between stock returns, exchange rate changes, and silver-related legislative and
regulatory events are consistent with an “export competitiveness” interpretation, and silver also
appears to have been both a conditioning variable and a priced risk factor in the global risk-return
equilibrium. Overall, however, silver shocks explain only a small fraction of stock returns. When
combined with low correlation between the seven markets, the evidence suggests that local
factors, home bias, or purchasing power hedging, rather than common factors, were predominant
determinants of stock returns during this era. This echoes what has been found for modern
emerging markets.
The consequences of changes in exchange rates and exchange rate regimes are a principal
preoccupation of international finance and economics. If consumers, corporations, and other
economic entities cannot easily hedge against currency fluctuations, those fluctuations can affect
aggregate consumption and wealth, export competitiveness and corporate profitability, and the
overall course of economics and politics. If, on the other hand, currency fluctuations are modest,
can be hedged easily, or largely track inflation differentials, they may be of little consequence.
Stock prices reflect the consensus estimate of the impact of exchange rate changes and related
events on subsequent corporate earnings. Furthermore, stock prices represent a variety of
industries that may respond to exchange rate changes differently and, thus, illustrate the impact of
exchange rate fluctuations on different sectors of an economy.1
This study measures the impact of exchange rate fluctuations and exchange rate regime
shifts on equity values from China and other Asian economies during the period prior to the
Second World War. This time and place is interesting for several reasons. First, the period from
the 1870s into the early twentieth century was a time of considerable international trade, free
capital movements, and active global securities trading facilitated by overland and underwater
telegraph lines.2 Therefore, the data can yield fresh evidence on the significance of exchange rate
fluctuations from a period that is comparable to the modern era in many ways.
Furthermore, the countries and territories we study abandoned a common currency, silver,
at different times. This can be compared and contrasted to the decline of the Latin Monetary
Union in nineteenth-century Europe [Willis (1901)], the collapse of the Bretton Woods system in
the early 1970s [Bartov, Bodnar, and Kaul (1996)], and the possible future evolution of current
European monetary arrangements. Third, the seven economies we study are analogous to the
small, open “emerging markets” which have generated much recent interest. Many sectors of the
economy of China were strongly tied to the export trade while the European and American
colonies in Asia were specifically dedicated to the production and export of commodities and light
manufactured goods. We predict a strong sensitivity to exchange rate fluctuations and changes in
exchange rate arrangements, perhaps even larger than what is observed for emerging economies
today. 3
Finally, the performance of stock markets in the face of dramatic shifts in exchange rate
regimes and related policies illustrates the impact of political and regulatory change on asset
1
markets and the economy generally. In the case of silver, the impact of U.S. silver price-support
policy changes on Asian stock returns is particularly interesting as it highlights the unintended
global consequences of domestic-oriented government policies. Furthermore, the significance of
currency and monetary policies in colonial-era Asia is of continuing interest: the economic and
political legacy of colonialism continues to color trade relations with China, the return of Hong
Kong to China, and relations between China and Taiwan.
The paper is organized as follows. Section I describes the historical setting and presents
several testable hypotheses. Section II outlines the data set and empirical tests we conduct.
Section III presents results while Section IV is a summary and discussion of implications.
I. Historical Background and Testable Propositions
A. Currency Regimes in Colonial-Era Asia
As China opened to trade with the West several hundred years ago, certain patterns of
commerce emerged. Initially, the Chinese had little interest in Western products, so it was
necessary to bring hard currency to Chinese ports to exchange for tea, silks, porcelain, and other
commodities in high demand in the West. China’s traditional monetary system relied on silver
bullion so foreign traders typically paid for Chinese products with silver coins which were minted
in great quantity from Mexican and Peruvian silver. As trade grew, foreign silver coins gained
wide acceptance in China’s domestic commerce as well.4 Retail prices and legal contracts were
denominated in silver measured in terms of the Chinese ounce known as the tael or liang, and
equal to about one and one-third English ounces. Spanish and Mexican silver dollars were
accepted as payment by weight or, in some cities, directly as prices were quoted in Mexican
dollars. Silver “pieces of eight” produced in Spain and Spanish America circulated extensively in
China for hundreds of years as a result of foreign trade transactions. Silver trade coins were
circulated by Imperial and Republican China, her provinces, Japan, and principal colonial powers
into the twentieth century.5 China remained on silver continuously until replacing all silver coin
and bullion with paper money in November 1935. [Shen (1941)].
Other Asian countries and territories switched from silver to gold or paper at various
times in the nineteenth and twentieth centuries.6 Hong Kong, a British colony on the Guangdong
coast, mirrored China: Silver dollars were the standard until December 1935 when the currency
was pegged at 16 Hong Kong dollars per pound sterling. British Malaya7 adopted the Mexican
2
silver dollar in 1867 but in August 1904 silver coins were withdrawn from circulation and
replaced by paper money convertible into gold. The Straits dollar was formally put on the gold
standard by pegging to the British pound at the rate of two shillings four pence in January 1906.
The pound departed from gold with the outbreak of war in June 1914, returned to gold in April
1925, and permanently abandoned gold for paper in September 1931. French Indochina8
introduced a silver trade piastre similar in weight to the Mexican dollar in 1885. In May 1930,
the piastre was pegged to gold at ten francs per piastre. The piastre followed the franc off gold
and onto a paper standard in October 1936. Netherlands Indies 9 formally adopted the silver
standard in 1854 but in March 1877 switched to the gold-backed Netherlands guilder. The
Netherlands went off gold in 1914, returned to gold in 1925, and finally abandoned gold in
September 1936. Japan recognized the Mexican silver dollar in February 1868, switched to gold
in March 1897, to paper in September 1917, and back to gold in January 1930. In December
1931, the yen was devalued and permanently detached from gold. The Philippine Islands found
both Mexican silver and U.S. gold currencies in circulation after the Spanish-American War of
1898. The Philippine peso was put on gold by pegging at the rate of two pesos per U.S. dollar in
March 1903. The U.S. dollar was put on the gold standard in 1879, switched to paper in August
1917, switched back to gold in July 1919, and remained on gold until April 1933. Among other
countries that we do not study, India switched to gold in June 1893 and Siam in November
1902. Mexico supplied the metal for billions of “pieces of eight” but abandoned silver for gold in
1905.
The workings of a silver standard were often chaotic. In the case of China, there was no
legal currency until the standard silver dollar of exact weight and fineness was declared in April
1933. Silver bullion, various coins, and banknotes circulated side-by-side while the system of
weights and measures for silver differed from city to city. The currency market was also complex.
A typical Shanghai newspaper, for example, listed different exchange rates for sight drafts,
forward drafts, credits, bills, and telegraph transfers to London, Paris, and other centers. There
were also prices for silver bullion, Mexican dollars, and older Spanish dollars which were well
known and therefore particularly acceptable in transactions with more traditional local customers
and business partners. Furthermore, there were different prices for “clean” dollars versus coins
that had been defaced, tested, or “chopped” by local merchants. In Hong Kong, coins competed
3
with well-accepted banknotes of the Hong Kong and Shanghai Bank and other private issuers.
The convenience of banknotes sometimes earned a premium above silver [King (1988a)] which
was sometimes significant prior to the 1880s [King (1965, 1988b)]. In British Malaya, the
government of the Straits Settlements sanctioned various foreign silver coins as legal tender into
the early twentieth century. In French Indochina, silver export controls were initiated in 1904 but
rice traders arbitraging currencies and commodities between Saigon and Hong Kong tended to
enforce the silver parity between the piastre and the Hong Kong dollar.
Silver was abandoned for a variety of reasons. The Japanese followed Europeans who had
abandoned silver after many chaotic decades in which both gold and silver were legal tender but
fluctuated widely in value. In China, the government claimed that expensive silver in the early
1930s was throttling export competitiveness and causing a drain of money from the country. In
British Malaya and French Indochina earlier in the century, exporters enjoyed competitiveness
from cheap silver. However, government officials and other expatriates objected to the declining
gold value of their salaries and assets while fluctuations in the value of silver were a source of
general discontent in the business community. These constituencies prevailed in driving their
colonial economies off silver.
The value of silver was often influenced by events far from Asian shores. New
discoveries of silver in Mexico, Peru, and the United States along with sales of silver by countries
adopting the gold standard decreased the price of the white metal.10 Political pressure by senators
from mining areas of the U.S. had, at times, led the U.S. government to adopt massive silver
purchase programs that increased the price of silver. The resultant export of silver from China to
the U.S. was believed to have severely contracted China’s money supply and lowered prices.
Indeed, Friedman (1992) discusses the possibility that the U.S. silver purchase program initiated
in 1934 overvalued silver, crippled Chinese export competitiveness, and caused such damage to
the economy that the fall of China to communism may have been hastened. Thus, the extent to
which silver price fluctuations affected the value of Asian companies highlights the impact of
domestic U.S. policies on the fragile political economy of colonial-era Asia.
B. Exchange Rate Fluctuations, Competitiveness, and Business Activity
There are many references to the implications of silver fluctuations for colonial-era Asian
businesses. Chiang (1966) notes that cheap silver enhanced Malaya’s export competitiveness
4
during that colony’s silver period. Exports of Indochina’s rubber increased with decreases in the
value of the silver piastre [Murray (1980)] while rubber production in gold standard Netherlands
Indies was adversely affected by cheap silver. Netherlands Indies manufacturers suffered an influx
of imported textiles and other consumer products when Britain and Japan devalued their
currencies and detached them from gold in 1931 [Furnivall (1944)]. Friedman and Schwartz
(1960) suggest low silver prices in the late 1920s and early 1930s helped boost China’s exports
and shielded her economy from the beginnings of the Great Depression.
A principal financial institution, the Hongkong and Shanghai Bank, illustrates the
challenges regional firms faced as the price of silver began to gyrate in the 1870s. Silver
fluctuations affected the Bank’s relationship with its shareholders. Although the bank kept its
books in silver, half of the shareholders were in gold-standard England and the bank struggled to
maintain the gold value of its dividend on their behalf. [King (1987)]. Commercial banking was
also affected by silver fluctuations. Currency transactions were risky because it could take up to
two months to move silver between Europe and Asia, and mistakes in pricing contracts for future
deliveries of silver could be costly. [Collis (1965)]. At the same time, the volume of currency
transactions between Europe and Asia was expanding rapidly along with trade between the two
continents.11 The bank attempted to hedge against silver fluctuations by matching sources and
uses of funds by metal. However, the demand for silver loans in the East typically exceeded the
supply of silver deposits, necessitating funding in gold and risking that the silver earned by the
Bank on its loan portfolio would, when converted to gold, be insufficient to pay interest to gold
currency depositors. [King (1987)].12
Table I lists the companies in the sample. They are fairly representative of the economies
of the countries and territories covered. For French Indochina, for example, the Portland Cement
factory and the major coal producer, Charbonnages du Tonkin, represent the economy of Tonkin
in the north while the rubber estates represent the economy of Cochinchina in the south
[Lancaster (1961), Murray (1980)]. The absence of plantation shares from the Philippines is
consistent with the concentration of corporate capital in agricultural processing rather than
agricultural production [Robequain (1954)]. Cooperative and competitive relationships between
firms are also reflected in the sample. Straits Trading, for example, was formed in 1886, leased
facilities from Tanjong Pagar Dock in 1887, and employed Straits Steamship to transport tin ore,
5
coal, and tin ingots. Jelebu Mining, Pahang Consolidated, and the other British Malayan tin
mines in our sample were major suppliers of tin ore to Straits Trading and its rival, Eastern
Smelting [Tregonning (1963)]. Koninklijke Paketvart was founded in 1888 to compete with
Straits Steamship and other British firms which dominated the shipping business in the
Netherlands Indies [Furnivall (1944)].
The large banks and agricultural conglomerates in the sample were important to the
design and execution of government economic policies. Banque de l’Indochine, Hongkong and
Shanghai Bank, and Javasche Bank issued paper money and acted as government banker in
addition to ordinary commercial banking activities. In the Dutch and French colonies, agricultural
production and processing was dominated by a handful of large “cultuurbanken” and “banques
des affairs” which combined the functions of banks, holding companies, and management
companies. Nederlandse Handel Maatschappij, for example, was founded as an import-export
agency for the colonial government but evolved into financing agricultural estates and sugar
factories from its own capital and deposit base [Allen and Donnithorne (1957)]. Monetary
policies were often changed with the advice and consent of these institutions, and they were often
instruments for developing targeted industries and implementing government policies. It was also
believed that these institutions were able to exploit their power, privileges, and information at the
expense of other players in the colonial economies. Banque de l’Indochine, for example, was the
target of complaints that its devotion to speculative currency trading superseded its agricultural
lending and other development-oriented activities [Murray (1980)]. Increased currency volatility
could lead to increased currency trading profits, commissions on sales of forward contracts, and
fees from other transactions.
C. Testable Hypotheses
The variety of currency regimes and industries in our sample allows us to examine the
impact of currency fluctuations in several ways. We hypothesize that stock returns can be
sensitive to changes in the price of silver in very straightforward ways and that this sensitivity will
change when the currency regime changes. We also hypothesize that stock returns can be
affected by the conditional volatility of silver prices based on an industry’s flexibility in adapting
to currency fluctuations. We also predict that currency regime shifts and other government
6
actions have a significant impact on stock returns. Finally, we hypothesize that, as a global
economic factor, silver will enter the global asset pricing equilibrium significantly.
Our simplest propositions revolve around the relationship between changes in stock prices
and changes in the price of silver:
H1. Returns on shares of export (import) oriented industries in silver (gold)
standard areas are negatively (positively) correlated with changes in the value of
silver.
For example, the share price for Koninklijke Paketvart, a Netherlands Indies steamship line,
should increase as silver increases in price relative to gold: As gold cheapens, the company
benefits from relatively cheaper operating costs, from greater revenues due to increased exports
from the Netherlands Indies, and from greater cost pressures on its competitors from silverstandard countries. The hypothesis assumes that the market for a firm’s products or services is
significantly price elastic.
H1 also assumes that changes in the price of silver result in shifts in the real exchange rate
between silver and gold regions. If, for example, the price of silver drops by ten percent, there is
no perfectly offsetting increase in domestic prices and production costs in silver-standard
economies. Put another way, purchasing power parity must be violated. Figure 2 offers some
evidence on the degree to which changes in the value of silver tracked changes in the difference
between Chinese inflation and inflation in two gold-standard economies, the Netherlands Indies
and England. It is evident that purchasing power parity did not hold and that large real
movements in the value of silver were common.13
H2. Exposure to the value of silver changes as a country or territory switches
from silver to gold.
This proposition is a variation on H1. For example, the slope coefficient from regressing stock
price changes of Straits Steamship, a Malayan shipping firm, on silver price changes should be
significantly different comparing the period when Malaya was on the silver standard to the period
when Malaya was on the gold standard.
7
In addition to looking at associations between stock returns and silver price changes, we
also consider the impact of higher moments of silver price changes:
H3. Returns on shares of globally-competing industries with little ability to switch
sources of inputs or destinations of outputs are negatively correlated with
conditional silver price volatility.
Dixit (1989) models the situation where exchange rate volatility combined with inflexibility in a
corporation’s operating environment may affect corporate decision-making and valuation. If, for
example, Jelebu Mining from Malaya is unable to increase production at times when silver is
cheap and is unable to cut back production at times when silver is costly, its stock price changes
may be negatively correlated with the volatility of silver price changes. In contrast, firms with the
ability to modify their activities or otherwise hedge against changes in exchange rate regime may
show a different association with changes in the volatility of silver prices:14
H4. Returns on shares of firms with special access to markets or information are
positively correlated with conditional silver price volatility.
If, for example, Hongkong and Shanghai Bank enjoys higher income from currency-changing fees,
sales of forward contracts and other hedges, and speculative or manipulative trading activities
during times of high silver price volatility, its stock price changes can exhibit a positive slope
coefficient when regressed on silver price volatility changes.
We can also expect specific stock price reactions at times when information about changes
in government policies concerning silver arrives:
H5. Announcements of specific monetary or currency policy changes cause an
event-time reaction in stock values.
For example, the announcement of a U.S. government silver purchase program designed to
support the silver-mining industry should decrease share prices of textile exporters in silverstandard countries as it represents new, upward pressure on the price of silver and, thus, threatens
the export competitiveness of silver-standard countries. We examine this proposition with
announced changes in both U.S. silver purchase plans and Asian currency regimes.
8
Finally, we examine the possibility that silver was so important that it can be detected in
cross-market asset pricing tests:
H6. Changes in the gold-silver exchange rate were a priced factor in the global risk-return
equilibrium.
If, for example, global investors could not perfectly hedge the exposures of their portfolios to
changes in the silver exchange rate, it could earn a risk premium in the expected returns of stocks
that are significantly exposed to silver. Note that H3 through H6 also rest on the notion that
silver price movements are not neutral in real terms: Changes in silver prices and silver price
volatility are not offset by changes in price levels.
II. Data
There are, to our knowledge, no Asian stock market databases beyond the Emerging
Stock Markets database of the World Bank’s International Finance Corporation and the PACAP
tapes maintained by the Pacific Basin Capital Markets Research Center at the University of Rhode
Island.15 Both of these sources extend back to the 1970s at the earliest. Thus, our initial task was
to build a database from scratch.
Stock Markets. End-of-month stock prices were collected from principal national,
colonial, or metropolitan newspapers such as The South China Morning Post, The Straits Times,
and The Manila Bulletin. Quotes for Netherlands Indies from prior to 1912 are from the
Amsterdam stock market, all French Indochina quotes are from the Paris market,16 and all other
quotes come from Asia. See the Appendix for a detailed description of the newspapers from
which data were extracted. Stock prices are either closing transactions or bid-offer midpoints
from organized or over-the-counter markets. The firms selected are quoted regularly, have
relatively large capitalization, and represent leading industries. While only a handful of companies
are collected from each market, they are typically the largest firms which represent much of the
total market capitalization and, in many cases, explicitly owned partial stakes in many other listed
firms. Data on dividends, ex dividend dates, and shares outstanding was published only irregularly
and, therefore, was not collected.17
9
We obtained data from December 1872 through December 1935. Stock data are almost
entirely absent prior to that time while silver was demonetized after that period. Table I does not
attempt to describe the frequency of missing observations or the many events leading to the
disappearance of individual companies or closure of entire markets. For example, the Tanjong
Pagar Dock Company was nationalized in 1905 by the colonial government. [Boors (1956)] while
Malayan tin and rubber companies blossomed with commodity price booms, then vanished in
liquidation or reorganization when commodity prices fell. [Pahang Consolidated Company, Ltd.,
(1966)]. Many markets closed during the First World War (1914 to 1918), the 1925 general
strike in China, and other disturbances. Our data for French Indochina and the Philippine Islands
begins relatively late in the sample period because there was no active stock trading earlier.
Additionally, Table I does not indicate the extent of cross-listing of securities across the
markets in the sample or with the London stock market. We surveyed the newspapers that were
the source of the data and found evidence of cross-listing as follows. Hong Kong and Shanghai
Bank was listed continuously in Hong Kong, Shanghai, and London. Spot checks indicate that
stock prices are very similar across the markets after accounting for different currencies of
quotation. This is not surprising given the absence of barriers to arbitrage, particularly the
availability of telegraph communications throughout our sample period. In Hong Kong, some of
the larger Shanghai stocks (EWO Cotton, Shanghai Dock, Shanghai Land) were often quoted, as
was one of our Malayan tin mines, Tronoh. Unsurprisingly, a few of the larger Hong Kong
companies (Hong Kong Land, Whampoa, Wharf) were usually quoted in Shanghai. Hong Kong
and China Gas was sometimes quoted in London and, at points during commodity booms, some
Malayan tin and rubber shares appeared there as well. Again, we could find no evidence of
significant gaps in pricing across markets.
Exchange Rates. End-of-month exchange rates were also collected from the same
newspapers. Exchange rates are closing or midpoint quotes for bills on London or telegraphic
transfers to London supplied to the newspapers by leading banks.
The exchange rates for French Indochina and Netherlands Indies posed some difficulty.
All French Indochina stock prices were collected from the Paris stock exchange listing and,
therefore, are quoted in francs rather than Indochina piastres. When available, we use the francs
per piastre exchange rate to translate the stock prices back into the Indochina numeraire for the
10
period through April 1930 when the colony was on the silver standard.18 When the francs per
piastre exchange rate is not published in the Paris newspaper, we use the exchange rate of French
Indochina’s nearest silver standard neighbor, China, to put stock returns into silver terms.
Netherlands Indies stock prices through 1912 are collected from the Amsterdam stock exchange
which implies that, for the period from 1872 to 1877, they are in gold, rather than the colonial
numeraire, silver. We use the exchange rate for Netherlands Indies nearest silver standard
neighbor, British Malaya, to translate stock returns into silver for the period when Netherlands
Indies was on silver.19
There were a variety of bullion, coin, banknote, bank bill, and wire exchange rates quoted
across our sample countries during our sample period. For the purpose of measuring the impact
of silver on stock returns, we use changes in the price of silver in London as the silver factor in
our regressions. The price of silver in London20 is collected from The Times of London. It was
the benchmark for determining exchange rates throughout the period we study and telegraph lines
connecting London to major Asian cities permitted the current London silver quote to be
disseminated immediately throughout Asia. Figure 1 plots its monthly evolution.
Global Stock Index. As a measure of global trends in asset values and general economic
conditions, we also collected monthly returns on British and U.S. stock indexes. For Britain, we
use a stock index series spliced from The London and Cambridge Economic Service’s The British
Economy Key Statistics (through 1921) and Banker’s Monthly (1922 on).21 For the U.S., we
combine the series constructed by Schwert (1990) through 1925 with the CRSP value-weighted
index. We translate the U.S. index returns to pounds and construct an equally weighted index of
British and U.S. index returns to serve as our global stock index return series. Ideally, we should
like to have the equivalent of the Morgan Stanley Capital International world stock index but such
a series is not available to our knowledge.
III. Results
To understand the impact of exchange rate fluctuations and exchange rate regime changes
on economic agents within each of the seven economies we study, we adopt the local currency as
numeraire in each economy. We compute stock returns or, to be more precise, log-differences of
stock prices expressed in local currency. Individual stock price return series are then aggregated
into equally weighted industry portfolios following the classifications in Table I. Not all industries
11
exist for all countries or territories. Furthermore, there are some cases where the industry
portfolio consists of a single prominent firm.22 We also construct equally weighted national
indexes of sample company returns for each country and territory.
A. Summary Statistics
Table II presents univariate summary statistics on the log-differences of silver, our global
stock index, 7 national index portfolios, and 31 industry portfolios. The variety of lengths of data
across our series is evident, with Shanghai Ports being the longest industry series. The volatility
of returns varies widely, with large Netherlands Indies agricultural and financial conglomerates
often having volatility only a half or a third that of natural resource industries like mining and
rubber. Based on the summary statistics in Bekaert and Harvey (1997), the volatilities of Asian
colonial stock returns are comparable to those of typical modern emerging markets but are much
smaller than those for the most volatile modern cases, Argentina and Brazil. There are three
economies, Indonesia, Malaysia, and Philippines, which can be compared directly, and it seems
that their volatilities were, interestingly, lower in the colonial era. The global portfolio of U.S.
and U.K. equities displays positive serial correlation at the first lag, as is commonly found in
modern stock index returns including the emerging markets covered by Bekaert and Harvey
(1997). However, serial correlation ranges from positive to negative across the Asian industry
portfolios and national indexes. For example, the Hong Kong Banking and Insurance portfolio,
consisting solely of Hong Kong and Shanghai Bank, displays large negative serial correlation.
Skewness is comparable across the Asian colonial markets and the modern emerging markets
while kurtosis is sometimes much larger in colonial times.
There are two particularly notable aspects to the average returns earned by the portfolios.
First, the global portfolio of U.S and U.K. equities has one of the highest average returns, about
37 basis points per month, along with the one of the lowest standard deviations of returns.
Second, many of the firms in the high-performing industry portfolios (Cold Storage, Fraser and
Neave, Dairy Farm, Benguet, China Light and Power, Hong Kong Electric, and Hong Kong and
China Gas) exist to this day, and continue to trade actively on local and overseas stock markets
under their original names. Both facts are suggestive of the survivorship issues discussed by
Goetzmann and Jorion (1999). This is also evident in Figure 3, a plot of cumulative returns for
the global index and equally-weighted national indexes of each Asian country or territory in our
12
sample. Clearly, colonial Asian stocks typically offered investors a bumpy and unsatisfying ride
relative to U.S. and U.K. stocks.
Table III presents correlations between silver, the global stock index, and the seven
national stock index portfolios. The positive correlation between the global index and silver
indicates that, on average, increases in the value of silver are associated with stock price increases
in the U.S. and U.K. Increases in the gold value of silver are also associated with stock price
increases in the gold-based economies of Japan, Netherlands Indies, and the Philippine Islands,
silver-standard Shanghai, and mixed-standard British Malaya. Increases in silver are not
associated with significant stock price movements in the silver-based economies of Hong Kong
and French Indochina (on silver for all but five years of our sample period).
Table III also reports correlations between the national stock index returns and the return
on the global stock index. Interestingly, the largest correlations are for French Indochina shares,
which were listed exclusively in Paris, and Netherlands Indies shares, which were listed in
Amsterdam until 1912. The third-highest correlation is for British Malaya, and some of the
component tin and rubber shares were cross-listed in London. The other indexes have low
correlation with the global index return.
Elsewhere in Table III, there are also sensible associations among the national stock
indexes. The three commodity exporting economies, British Malaya, French Indochina, and
Netherlands Indies, show significant cross-correlation, perhaps due to their common exposure to
the prices of tin, rubber, and other crops and minerals. A large cross-market correlation, almost
20%, is displayed between China’s two markets, Hong Kong and Shanghai. The extensive trade
and investment relations between Japan and the Dutch colonies are evident in their correlation.
Overall, however, the correlations are much smaller than we would predict given the open capital
markets and trading orientation of these economies. In particular, the correlations are much
lower than what is typically observed between developed economies in the 1980s and 1990s,
although they parallel the results of Harvey (1995) and Bekaert and Harvey (1997) which show
that emerging market stock returns typically seem more sensitive to idiosyncratic, rather than
global, influences.
B. Silver Price Changes and Stock Returns
13
Tables IV and V present regressions of stock returns on silver price changes and the
global stock index return. Because of the significant correlation between log-differences of silver
and the global stock index, we orthogonalize the silver price changes over the global stock index
returns and use the resulting residual to represent silver in most subsequent empirical tests.23
Although we cannot determine the direction of causality between silver and global stock
movements, we prefer to use the residuals of silver to avoid exaggerating the explanatory power
of silver.
Table IV presents regressions of local currency portfolio returns on the value of silver and
the global stock index return. 8 of the 31 portfolios display a statistically significant slope
coefficient on silver. The significant coefficients appear for both gold and silver standard
economies. Some of the significant slope coefficients seem consistent with H1. For example, a
1% increase in the price of silver is associated with a .28% decrease in the value of Hong Kong
Banking shares, indicating that the value of this financial institution declines as the value of silver
increases. This is consistent with H1which implies that increases in the value of silver reduce the
competitiveness and prosperity of silver-standard economies dependent on exports. The Hong
Kong Banking portfolio consists solely of Hong Kong and Shanghai Bank which was exposed to
general economic conditions in China through its banking activities and through its holdings of
other corporations. The opposite result is observed for the bank portfolios of Netherlands Indies
and the Philippine Islands. Bank stock prices in these gold standard economies increased when
gold became cheaper relative to silver, perhaps because local economies became more competitive
and prosperous with a cheaper domestic currency. The negative slope for Hong Kong Banking is
especially ironic given the Hong Kong and Shanghai Bank’s historical practice [King (1987)] of
attempting to hedge against declines in the value of silver. Cheap silver was associated with
prosperity for the bank and increases in the value of its shares.
Some of the other results in Table IV are also consistent with a straightforward
interpretation of H1. Food and Mining in gold standard Philippines benefit from expensive silver,
as might be expected of gold-based companies competing with other regional firms selling sugar,
beer, and metals. Netherlands Indies Transport shows a positive association with silver, which is
consistent with cheap gold giving those firms and their underlying economy a competitive edge.
14
The lack of significance for the export-oriented rubber producers in British Malaya and French
Indochina and the Mining Portfolio in British Malaya is surprising.
At least two of the slope coefficients are problematic. The positive exposure of the
Shanghai Commercial/Industrial portfolio that consists entirely of textile producer EWO Cotton
seems counter-intuitive: expensive silver would be associated with more intense competition from
gold standard areas. However, the regressions of stock returns on silver price changes can be
thought of as a reduced form that obscures the underlying causal factors and direction of
causality. For example, a high value of silver could be caused by increased global demand for
silver with which to purchase Chinese textiles. Therefore, EWO Cotton’s earnings and stock
price are positively correlated with the price of silver.24 The positive association between silver
and stock prices of the “ports” sub-index of silver standard Shanghai is also not intuitive. If costly
silver chokes off exports, this would have an adverse impact on the warehousing, handling, shipbuilding, and repair industries dependent on export volume. Of course, the increased values could
be due to increased import volume due to the higher international purchasing power of silver.
The slope coefficients on the global stock index that are also reported in Table IV mirror
the correlation results presented in Table III. There are several cases, mostly from French
Indochina, where firms display large global “betas”. For the most part, however, the stock
sensitivities to this factor are small. Only about a third of the portfolios display a significant slope
for the global stock index factor.
Table V focuses on three special cases where an economy dropped the silver currency
standard. Netherlands Indies switched from silver to gold in 1877, British Malaya switched in
1904, and French Indochina switched in 1930. The table parallels the regressions of Table IV but
also includes slope and intercept dummies for the time period when the respective territory was
on silver. Of the twelve portfolios, only two, British Malaya Commercial/Industrial and Ports,
show a significant slope coefficient on the silver slope dummy variable. What this means is that
silver was important to stock values in these industries, but only during those times when the
economy was on the silver standard. Silver did not matter at other times, even if competitors or
the big Chinese economy were still using silver. Positive signs indicate that stock values for these
two British Malaya portfolios increased with the price of silver during the time when this territory
was on silver. This makes some sense for Commercial/Industrial as the portfolio consists of firms
15
that imported automobiles and parts or consumer pharmaceuticals. The positive sign for Ports
makes sense if the elasticity of demand for imports was larger than that for exports, which is
plausible given Malaya’s global dominance in the production of unique commodities like tin and
rubber.
The insignificant results for British Malaya Food and Mining portfolios in both Tables IV
and V may represent the peculiar characteristics of the firms in those portfolios. The Food
portfolio represents two firms that produced and retailed food products. Expensive silver would
have benefited these firms with decreased costs of purchasing foreign products for domestic resale
but would have dampened sales overall as the export sectors of the local economy suffered.
Consistent with the discussion of the Ports portfolio, the insignificant slope for Mining may in
some way be due to the global dominance of Malaya in supplying unique commodities with
inelastic global demand.
Another notable facet of Table V is that betas on our global stock index return are
significantly lower in the silver period in four of twelve cases. This may be easily explained for
British Malaya and French Indochina: Malaya’s “off silver” period spans both the First World War
and the beginning of the Great Depression while French Indochina’s spans the Great Depression
as well. Trade and capital regimes during these periods may have been less open, and
idiosyncratic events were likely to be more important.
C. The Hong Kong and Shanghai Bank
Much of the information about the operations and performance of the firms in our sample
is, quite literally, “lost in the sands of time”. As indicated earlier, records of dividends paid were
reported only irregularly in newspapers. Scant accounting information was produced by listed
firms, and modern libraries maintain only fragmentary holdings of publications like China Stock
and Share Handbook, an annual published by the North China Daily News which provided a page
of basic information on each company listed on the Shanghai stock market. Many library books,
public records, and company accounts were destroyed during the Second World War.
A notable exception is the most prominent firm in our sample, the Hong Kong and
Shanghai Bank. During the Second World War, the Bank’s records were preserved in London.
They form the basis for an extensive history of the Bank [King (1987), (1988a), (1988b)] from
which we collected some accounting data for a closer look at this firm.25 Figure 4 plots the
16
annual dividend yield and ratio of earnings-per-share to share price for the period from 1876 to
1935. The dividend yield was typically large, five percent or more, while earnings were quite
volatile and often equal to a large fraction of the market value of equity. Figure 5 displays the
interim, final, and bonus dividend per share for the same time period. Interim and final dividends
seem quite “sticky”, that is, change infrequently as has been documented for firms in more recent
times. The bonus dividend seems less regular.
Table VI presents some regression results that detail the behavior of the Bank’s dividends
and earnings, and their relation to silver. The first row presents a regression of the Bank’s silver
currency earnings per share on the value of silver. The resulting significantly negative slope
coefficient and the R-squared of about 7% are consistent with H1 and the results on monthly
stock returns in Table IV. Expensive silver had an adverse affect on the value of the Bank,
perhaps due to the impact on the export competitiveness of the Chinese economy that supported
the Bank’s profitability. The second row shows that current year’s dividend is very strongly
correlated with current and previous years’ earnings per share. The R-squared of almost 70%
indicates that current and previous earnings were the predominant factors in determining
dividends. Finally, a probit regression indicates that the probability that a bonus dividend is paid
was, again, strongly positively related to current and previous years’ earnings. Overall, we
confirm the importance of silver to this company and, in particular, observe how fluctuations in
the value of silver fed into earnings and dividend policy.
D. Conditional Silver Volatility and Stock Returns
Propositions H3 and H4 require us to measure the association between stock price returns
and changes in the volatility of silver price changes. We use a “GARCH-in-mean” econometric
specification that extracts a conditional volatility process for silver price changes and permits that
conditional volatility to influence stock price changes:
εs,t = rs,t - β0,s - β1,s hss,t
(1a)
εp,t = rp,t - β0,p - β1,p hss,t
(1b)
hss,t = γss,0 + γss,1 hss,t-1 + γss,2(εs,t-1)2
(1c)
hpp,t = γpp,0 + γpp,1 hpp,t-1 + γpp,2(εp,t-1)2
(1d)
hsp,t = γsp(hss,thpp,t)1/2
(1e)
17
The beta and gamma symbols are parameters while “s” and “p” subscripts refer to the silver price
change and portfolio return series respectively. Equation (1a) defines the residuals from an
equation that allows silver price changes to depend on silver price volatility while equation (1b)
allows portfolio returns to depend on silver price volatility. The system of equations is estimated
simultaneously and the size and sign of the coefficient β1,p measures the associations described by
propositions H4 and H5. Equations (1c) and (1d) define the dynamics of the volatility of the
silver price change and the portfolio return respectively while equation (1e) specifies a constant
correlation between the silver and portfolio processes. See Bollerslev, Chou, and Kroner (1992),
Chan, Chan, and Karolyi (1991), and Chan, Karolyi, and Stulz (1992).
Table VII summarizes estimates of the bivariate GARCH system. The purpose is to see if
the portfolio returns are sensitive to changes in the volatility of silver as hypothesized in H3 and
H4. For this set of results, we use the raw silver price change series, rather than the series
orthogonalized over the global index return. We take two steps to save space in reporting these
results. First, we report only the slope coefficient that relates stock returns to conditional silver
volatility. Second, we present the slope coefficient only for the seven national index portfolios
and summarize the results for industry portfolios even more briefly.
H3 rests on the notion that inflexible or disadvantaged firms suffer from exchange rate
volatility. In contrast, H4 predicts that some firms are flexible or enjoy other advantages that
allow them to profit from any movement in exchange rates regardless of sign. We gave the
example of a bank which enjoys increased income from exchanging currencies, selling hedges, and
perhaps even trading on inside knowledge of any impending exchange rate movement positive or
negative. There is no evidence that, on average, the aggregate economies of these colonies and
countries suffered or benefited from increases in the volatility of silver while the economy of
French Indochina benefited. Furthermore, we see no such reaction for the powerful quasi-central
banks such as the Banque de l’Indochine, Hong Kong and Shanghai Bank, and Javasche Bank
that dominate the Banking portfolios.26 Furthermore, the summary of individual industry portfolio
results in the right-hand column of the table indicates few significant results. Only some exportrelated industries from British Malaya and the Utilities portfolio from Shanghai clearly reinforce
the negative sensitivity to silver volatility that the national index displays. Only the French
Indochina Mining portfolio consisting of a single coal mining firm show evidence that its value is
18
enhanced by silver volatility. A panel of additional results inserts a slope dummy term to test if
the sensitivity of stock returns to conditional silver volatility depends on whether or not the
underlying economy was on silver, but these results are insignificant.
E. U.S. Silver Purchase Legislation and Asian Currency Regime Events
We have identified several U.S. government actions that were believed to have a strong
impact on the subsequent course of silver prices.27 The Bland-Allison Act authorizing large
purchases of silver by the U.S. government was passed on February 28th 1878. The Sherman Act
of July 14th 1890 virtually doubled the size of silver purchases by the U.S. government. The
Senate passed a bill authorizing “free coinage” of silver in July 1892, although the bill was never
signed into law. Repeal of the silver purchase clause of the Sherman Act was proposed in June
1893 and passed on November 1st 1893. The Silver Purchase Act of 1934 was passed on June
19th of that year.
We search for significant reactions to these events using the following procedure. For
each portfolio and event, we first determine whether there are at least 30 of a possible 36
observations of returns data in the three-year period from four to forty months prior to the event
and four complete observations for a window spanning the event month and three previous
months.28 If this condition is satisfied, we regress the portfolio return series on the silver residual
and global stock index return from t-40 to t-4. The regression residuals are used to compute an
estimate of the standard deviation of residuals while the regression coefficients are used to
generate prediction errors for the event window. The sum of event window prediction errors
divided by the estimated standard deviation times the square root of 4 is a z-statistic with four
degrees of freedom.29 It is a measure of whether there was significantly unusual stock return
behavior around the time of the event.30
Table VIII presents evidence on the prediction errors and their z-statistics. To save space,
we report full results only for the seven national index portfolios and mention results for
individual industries only if they are statistically significant. The limited amount of data in the
earlier part of our sample gives us only a few cases with which to study the Bland-Allison Act of
1878 and the Sherman Act of 1890. There is no indication of unusual returns behavior around
these two earliest events. U.S. Senate passage of a “free coinage” silver bill in 1892 has a strong
negative impact on bank values in silver-standard Hong Kong and a strong positive impact on
19
bank values in gold-standard Netherlands Indies. This echoes the results in Table IV. “Free
coinage” implies that large amounts of silver would be coined into U.S. dollars. This could imply
either a heightened value of silver due to a strict peg to the dollar, or it could imply silver, and
dollar, inflation. The stock markets seem to have interpreted the event as potentially increasing
the value of silver. The size of the cumulative prediction errors, about minus 20% for Hong Kong
banks and plus 20% for Netherlands Indies banks, indicates very large stock return surprises at
that time.
We find more complex evidence for the repeal of the silver purchase clause in 1893. The
repeal implied less U.S. government support for silver and, therefore, cheaper silver. However,
the stock results indicate a strong negative impact in several portfolios from both silver and gold
areas. For example, cheaper silver should imply greater prosperity in China but is associated with
drops in the Shanghai national, port, and utility indexes. The Netherlands Indies Bank portfolio
also declines with this event. We cannot interpret this evidence using our competitiveness story.
There is almost no evidence of significant reaction to the Silver Purchase Act of 1934,
even though we have a more complete array of industry portfolios with which to interpret that
event. This Act again implied much higher silver prices and there is indeed a massive 50% return
for the rubber exporters of gold-standard British Malaya. This is also evident in the national index
returns for Malaya. There is consistent evidence in that there is a large drop in prices for Real
Estate stocks in silver-standard Hong Kong. However, no other significant reactions are evident.
This contrasts with the importance of U.S. silver legislation to the Chinese economy which
Friedman (1992) suggests. Collectively, the evidence in Table VIII indicates that large reactions
to changes in U.S. silver policies were limited only to selected industries and were otherwise
ignored by the stock markets.
Table IX conducts similar tests for five announcements of changes to currency standards
that affect our seven countries and territories. British Malaya switched from silver to gold in
August 1904. Five industry portfolios show significant positive reactions. The positive reactions
for Netherlands Indies Agriculture and Transport are consistent with the elimination of cheap
silver as a benefit to competing firms in British Malaya. The resumption in 1925 of convertibility
into gold by Britain and the Netherlands was potentially relevant to the firms in British Malaya
and Netherlands Indies since their currencies were tied to the pound and the guilder. However,
20
there is no reaction to this event other than a drop in Philippine Bank share prices and the
Philippine national index, and an increase in Hong Kong Utilities share prices.
French Indochina dropped silver in favor of gold in May 1930. The negative reaction of
the French Indochina national index portfolios is consistent with the explicit or implicit export
exposures of local industries combined with the expectation that gold would be relatively
expensive in the future. The abandonment of gold by Britain and Japan in late 1931 is associated
with many significant reactions. In particular, the positive reaction by Japanese
Commercial/Industrial and Food portfolios to the cheapening of the yen in December 1931 is
consistent with enhanced export competitiveness.
F. Silver and the Global Risk-Return Equilibrium
Our final test explores the extent to which silver was important to equilibrium global asset
pricing as outlined in H6. For a global perspective, we translate all portfolio returns to pounds,
taking the point-of-view of a global investor rather than a local Asian investor. We express
returns in excess of a short maturity gold global interest rate31 and estimate the parameters of an
asset pricing model in which expected returns are generated by expected premiums for systematic
risks and the risk exposures of a particular asset to those factors. The expected risk premiums
can vary through time with the business cycle and other general economic conditions. [See, for
example, Ferson and Harvey (1991).] The application of an asset-pricing model to our data is
tentative for at least two reasons. First, our return series are capital gains only because we could
not obtain consistent records on dividends and ex dividend dates. This can affect results
significantly if the dividend component of total returns is large and varies significantly through
time.32 Second, our stock returns data display patterns of missing observations that vary across
industries. We cannot adopt simple one-pass econometric procedures under these circumstances.
We follow the three-pass procedure of Ferson and Harvey (1991). The first pass consists
of time-series regressions of stock portfolio excess returns on the silver price change residual, the
global stock index excess return, and an equally-weighted Asian index excess return residual.33 In
the second pass, monthly cross-sectional regressions of portfolio excess returns on estimated
betas produce time-series of coefficient estimates. These coefficients can be interpreted as the ex
post returns on four portfolios. One portfolio has unit risk exposure to silver, one has unit risk
exposure to the global index, one has unit risk exposure to the Asian index, and the final portfolio
21
has no exposure to any of the three factors. [See Fama and MacBeth (1973).] In the third pass,
we estimate time-series regressions of these “mimicking portfolio” return series on lagged values
of the residual silver price change, the global stock index return, the Asian stock index return, and
the global interest rate. Lags of these factors serve as proxies for the information investors use in
formulating ex ante expected stock returns and they may be able to detect the presence of timevarying risk premiums in the mimicking portfolio returns.34
Table X reports the results of the third-pass regressions for each of the three mimicking
portfolios. All four regressions show adjusted r-squared coefficients that are small, though not
radically different from what has been reported for portfolios of modern stocks across countries
and industries. The regression for the silver risk-mimicking portfolio shows that a positive lagged
silver return tends to be associated with a negative realized risk premium for silver exposure.
Silver price changes also have small but significant forecast power for the global stock index
factor and the Asian stock index factor. This suggests that the price of silver was a significant
state variable which global investors included in their forecasts of expected returns and the course
of the global economy. Interestingly, lagged silver enters the regressions for global versus silver
risk premiums with different sign, again suggesting something distinct about the silver exchange
rate factor. There is even stronger evidence that silver and the global stock index can forecast
changes in the portfolio with no exposures to any of our factors. This suggests that silver was
important in forecasting the behavior of company dividends or other factors missing from our
model. This is consistent with Table VI which shows that the value of silver was a significant
determinant of dividends for the Hong Kong and Shanghai Bank.
IV. Summary and Conclusions
Our database allows us to examine the impact of exchange rate volatility and exchange
rate regime changes using a very long history of previously-untouched data from an era with many
parallels to our own. We find evidence that exchange rate fluctuations had an impact on colonial
Asian business activity. First, several industries exhibit significant exposures to ups and downs in
the price of silver. The signs of estimated exchange rate exposures are sometimes intuitively
sensible and sometimes differ when comparing competing industries in different currency regions.
In a few cases, risk exposures change with changes in currency regimes. Second, flexibility or
inflexibility in adapting to silver uncertainty is indicated for a few industries with a significant
22
association between stock return and conditional exchange rate volatility. Third, some stock
returns fluctuate strongly at times of extraordinary political and legislative events affecting the
value of silver. Finally, silver appears to have affected the global economy beyond its explicit
impact on importing and exporting firms in Asia. There is evidence of a stock market risk
premium for silver exposure. It also appears that the price of silver served as one of the general
state variables for global investors.
Some aspects of the results are surprising. Although silver price fluctuations and silverrelated events are sometimes significant in explaining stock returns, their impact is not
overwhelming. Furthermore, there is hardly any evidence that the conditional volatility of silver is
an important determinant of stock returns. When combined with the small explanatory power of
the global stock returns and low cross-market correlation, the behavior of the stock returns
appears to parallel what has been documented for many modern emerging markets. Harvey (1995)
and Bekaert and Harvey (1997), for example, document that the IFC universe of emerging market
equity returns seem more sensitive to local rather than global factors. This is true even for
countries where explicit capital barriers have been lowered or largely eliminated. This suggests
information problems, home bias,35 or other more subtle barriers are still substantial in modern
emerging markets. These influences also seem likely for our sample of prewar Asian markets.
This is particularly anomalous given the openness of these economies to trade and investment. It
is unfortunate that we cannot measure the extent to which local investors in the colonial
economies concentrated their portfolios in their local market, or the degree to which European
and North American investors were willing to diversify into the colonies.
The evidence on slope dummies in Table V is particularly surprising. Recall that local
currency stock returns are regressed on silver plus a silver slope dummy term that separates
periods when the local economy was on silver versus periods on gold. A few coefficients on
slope dummies are significant, suggesting that the behavior of silver was important to stock
returns in those countries during the period that they were on the silver standard. However, the
coefficients on silver are almost always not significant, which suggests that the markets largely
ignored silver when their local economy was not on the silver standard. This is not consistent
with a “competitiveness” interpretation that would predict the sign of the exchange rate exposure
to switch when the exchange rate regime changes. Perhaps “purchasing power hedging” [see, for
23
example, Adler and Dumas (1983) or Cooper and Kaplanis (1994)] is a more appropriate
interpretation. That is, local investors updated stock prices at times of silver price changes to
reflect their concern about how the silver exchange rate affected their silver-denominated
consumption and wealth during times when their numeraire was silver. At other times, silver was
not relevant. This again suggests that local factors or home bias were much more important than
global equity and currency risks in these economies, in spite of their openness to trade and capital
flows.
Acceptance or abandonment of particular currency regimes or international currency
arrangements is usually thought to have a significant impact on economic activity. In the
nineteenth century, European states struggled to redesign their monetary practices in the face of
volatile relative values of gold and silver, large movements of coins and bullion across borders,
and other presumably disruptive monetary conditions. Modern European states have invested
enormous energy and resources into constructing a common currency area. Indeed, Hardouvelis,
Malliaropolus, and Priestly (1999) find that modern European economic convergence has been
significant to the pricing of European equities and, in particular, has lowered the cost of capital
across the continent. Notions of exchange rates and competitiveness, Friedman’s conjectures
about the impact of U.S. silver legislation on China, and the capital flight theory of Chang (1991)
are consistent with sharp stock price reactions and, thus, seem to be rejected by our evidence.36
Why did colonial Asian stock markets put only secondary weight on exchange rate fluctuations
and regime shifts? We can speculate that the openness of these economies, lack of demand for
protection by businesses or workers, and other aspects of these largely unregulated economies
allowed companies to adjust prices, wages, and other costs relatively easily in the face of
seemingly significant exchange rate developments.
This study of rather old stock price data is subject to some limitations. We have no
information on the liquidity or rationality of the markets from which the stock prices are drawn.
Perhaps more importantly, we lack detailed information on the firms that might allow us to
speculate with greater precision about the determinants of the measured exposures to silver
fluctuations. Nonetheless, we get a good deal of evidence about the importance of exchange rates
and exchange rate regimes for equity markets. Perhaps the strongest conclusion we can draw
from these results is that long histories of stock prices continue to prove their usefulness both in
24
understanding the forces that affect stock prices and in interpreting broader economic events and
policies.
25
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31
Endnotes
1
See, for example, Jorion (1990, 1991), Bailey and Chung (1995), and He and Ng (1998).
2
The first transatlantic cable was laid in 1866 while the first cable connecting London to Shanghai was completed
in 1871. See Garbade and Silber (1978) and Michie (1987).
3
See, for example, Bailey and Chung (1995).
4
Only small retail transactions were conducted with small copper coins known as cash. See King (1965).
5
From the first coins struck in 1537 to the last Mexican production in 1903, almost four billion Spanish and
Mexican dollars were struck. Hundreds of millions of additional coins were also produced by Japan, Britain,
France, and the U.S. to facilitate trade with China and other Asian countries. See Willem (1959), Chiang (1966),
and Sandrock (1995).
6
In the following discussion, we do not distinguish between a gold standard where gold coin is literally in
circulation and a gold exchange standard where the currency is maintained at par with a gold standard currency of
another country and, thus, is indirectly convertible into gold coin. See Wagel (1914). See Allen and Donnithorne
(1957), Cassel (1922), Chiang (1966), Friedman (1960, 1990), King (1988a), League of Nations (1937),
Robequain (1939), Shinjo (1962), Touzet (1939), and Vissering (1920) for details of the currency regimes.
7
The Crown Colony of Straits Settlements (Malacca, Penang, and Singapore), the Federated Malay States, and
several other sultanates less formally linked to Britain and known collectively as the Unfederated Malay States.
8
Vietnam, Cambodia, and Laos.
9
Essentially the archipelago now known as Indonesia.
10
Germany, the Netherlands, and the Scandinavian countries abandoned silver in 1871 while large silver deposits
were discovered in Nevada in 1873. U.S. silver purchase programs were initiated or modified by the Bland-Allison
Act (1878), the Sherman Silver Purchase Act (1890), and the Silver Purchase Act of 1934.
11
In 1886, for example, half of Britain’s exports were shipped to silver-standard countries. See MacKenzie
(1954).
12
Other banks such as the Chartered Bank of India, Australia, and China dealt with this risk in a similar fashion.
See MacKenzie (1954). The inability to manage this risk was the primary cause of the failure of the Oriental Bank
and reorganization of the Chartered Mercantile Bank. See King (1965).
13
Few price indexes exist for this era. Annual data on the gold value of silver and wholesale prices in China and
the Netherlands Indies were obtained from China Ministry of Industries (1935) and Korthals-Altes (1994). In
addition to constructing Figure 2, we also regressed percent changes in the pound value of silver on the spread
between Chinese and Netherlands Indies inflation implied by the two wholesale price indexes. An adjusted Rsquared of 39.1% and slope coefficient (standard error) of 0.8687 (0.1319) confirm that inflation differentials were
an important but not exclusive determinant of the relative values of silver and gold currencies in the region. See
Lothian (1991) and Lothian and Taylor (1996) for evidence that purchasing power parity for other currencies (the
dollar, pound, franc, and yen) was, at best, valid only in the long run during the period we study. Also see
evidence and discussions in Jamieson (1893).
32
14
See Sercu (1992) for a model incorporating this notion.
15
Recently, Goetzmann and Jorion (1997) have uncovered stock price indexes from 1921 to the second world war
for several of the countries in our sample. Source is League of Nations publications.
16
A stock exchange was proposed for Saigon in 1926 but never developed [Simoni (1929)].
17
Since our study focuses on estimating correlations and short-term return behavior within event-windows, the
absence of dividends is not likely to induce significant bias in our results. This would not be the case if we were
studying long-term cumulative stock returns, the fit of asset pricing models, or other issues related to total return
performance.
18
The piastre exchange rate is not reported for July 1914 to August 1992 and February 1929 to September 1929.
19
British Malaya data begins with August 1873 so the Chinese exchange rate is used from December 1872 to July
1873.
20
The London silver market was subject to price controls at points during the First World War. They were
removed in May 1919. See MacKenzie (1954).
21
This series was prepared by Professor Tim Opler of Ohio State University and can be downloaded at
www.cob.ohio-state.edu/~fin/osudown.htm.
22
While the number of firms is small, these firms often represented a broad cross-section of the local economy.
Nederlandse Indies Handelsbank, for example, held stakes in agricultural firms, tin mines, and the Netherlands
Indies Spoorweg (railway) in addition to functioning as a conventional bank. In the French territories, a 1900
decree allowed Banque de l’Indochine to invest in shares of other companies while Societe Financiere des
Caoutchoucs established and retained stakes in many rubber plantations including Terres Rouges [Murray (1980)].
In contrast, agriculture and mining in British Malaya did not evolve around large banks or holding companies:
British Malayan “agency houses” evolved from merchant firms to firms providing managerial expertise and other
assistance but did not function as banks or holding companies [Allen and Donnithorne (1957)].
23
This does not induce an errors-in-variables problem since the generated regressor is an unlagged residual. See
Pagan (1984, 1986).
24
Leavens (1939) argues that silver prices were driven by worldwide demand for Chinese exports. A positive
correlation between earnings, share price, and local currency has been documented for some industries in more
modern times, and the relationships may represent export demand “causing” exchange rate movements. See, for
example, Maloney (1990). We collected the annual value of Chinese cotton and yarn exports from Yang (1931)
and deflated them by a Chinese traded goods price index from China Ministry of Industries (1935) to produce a
real index of cotton and yarn exports. A regression of the change in the gold value of silver on the growth of this
export index (for the 1873 to 1928 period for which we had data) produced a significantly positive slope
coefficient.
25
Other good company histories include Cameron (1982) on China Power and Light and Coates (1977) on Hong
Kong Electric. Note, however, that these books contain far fewer statistics than King’s work on the Bank.
26
This is consistent with the work of Highfield, O’Hara, and Wood (1991) who found no evidence that privatelyowned Second Bank of the United States exploited its position to maximize profits rather than fulfill the
responsibilities of a central bank.
27
See Leavens (1939) for details.
33
28
Because we reckon event time from the time of passage of legislation, we adopt a “t-3” to “t” event window of
four months to capture possible anticipation of the event earlier in the legislative process.
29
See Chen and Merville (1986).
30
Note that this differs from typical event studies in which the cross-sectional average residual is examined for
different firms that experience a similar event at differing times. We are examining the significance of individual
portfolio return residuals at times of single events. See Hill and Schneeweis (1983), Chen and Merville (1986),
and Bailey and Ng (1991) for examples of empirical work based on residuals for single security return series. See
Dann and DeAngelo (1988) and Ryngaert (1988) for examples of work which report individual firm residuals, in
additional to more conventional cross-sectional averages.
31
The three-month bank bill yield series from NBER.
32
See Stambaugh (1983).
33
The return on an equally-weighted average of all seven national index portfolios is orthogonalized over the
global stock index return and the silver return.
34
Although the mimicking portfolio returns are generated at the previous stage, they are used as dependent
variables rather than regressors and, thus, do not yield the classical errors-in-variables problem in the third pass
regression. To the extent that the mimicking portfolio returns measure risk premiums with noise, the regression
errors are enlarged and bias standard errors upwards.
35
See, for example, French and Poterba (1991), Cooper and Kaplanis (1994), Tesar and Werner (1995), and Kang
and Stulz (1997).
36
However, our results support Brandt and Sargent (1989) who suggest that silver appreciation constituted a one
shot capital gain for China with no lasting real impact.
34
Appendix: Sources of published stock and exchange rate data
The following publications were consulted in the British Library, Cornell University Library, Hong Kong Public
Records Office, Library of Congress, National Library of Singapore, National University of Singapore Library,
and New York Public Library:
The price of silver was obtained from The Times, London.
British Malaya stock prices were obtained from the Straits Times or its precursor, the Singapore Daily Times,
prior to 1882.
French Indochina stock prices were obtained from Le Temps, Paris.
Hong Kong stock prices were obtained from the China Mail and China Overland Mail (prior to 1881), the
Hong Kong Telegraph (1881 to 1904), and the South China Morning Post (1904 on).
Japan stock prices were obtained from the Japan Times.
Netherlands Indies stock prices were obtained from Nieuwe Amsterdamsche Courant (through November
1884) and Die Indische Mercuur thereafter.
Philippine Islands stock prices were obtained from the Manila Bulletin, also titled the Manila Daily Bulletin at
points.
Shanghai stock prices were obtained from the North China Herald and North China Daily News.
35
Table I. Overview of the Sample of Equities
End-of-month stock prices were collected from colonial or metropolitan newspapers. Pre 1912 quotes for Netherlands Indies are from Amsterdam while all French Indochina
quotes are from Paris. All other quotes come from organized or over-the-counter markets in Asia. The firms selected are quoted regularly, have relatively large capitalization,
and represent leading industries. Except where noted, each company has data spanning the years indicated in the column heading. The table does not indicate the frequency of
missing observations due to market closures during the first World War or other disturbances, corporate reorganization, infrequent trading, or missing newspapers. Missing
observations are especially severe for Japan.
Industry
British Malaya
French
Indochina
1873 to 1935
Hong Kong
Japan
Netherlands Indies
Philippine Islands
Shanghai
1873 to 1935
1897 to 1935
1873 to 1935
1914 to 1935
1873 to 1935
1911 to 1935
Agricultural
Groups
Societe
Financieur des
Caotchoucs
Banking and
Insurance
Straits Insurance (1884-1902)
Banque de
l’Indochine
Commercial
and Industrial
Wearne Brothers (1913 on),
Singapore Dispensary (18931918)
Ciments
Portland
Indochine (1926
on)
Food
(processing
and retailing)
Cold Storage (1904 on),
Fraser and Neave (1898 on)
Mining
(mines,
collieries, and
smelters)
Tongkah Harbour Tin
Dredging (1909 on), Malay
Peninsula Prospecting (18871898), Jelebu Mining (18891904), Pahang (1889-1933),
Tronoh (1901-1933), Eastern
Smelting (1908-1930), Straits
Trading (1894-1941)
Nederlandsche
Handel, N. I.
Handelsbank,
Koloniale Bank
H. K. and Shanghai Bank
Bank of the P. I.,
China Bank (1926 on)
Asano Cement
(1923 on), Nippon
Oil , Fuji Paper
(1907 on),
Kanegafuchi
Spinning
Dairy Farm (1899 on)
Charbonnage du
Tonkin (1921
on)
Javasche Bank, N. I.
Escompto (1895 on)
EWO Cotton (1895
on)
Nippon Sugar
(1904 on), Nichiro
Fishery (1925 on),
Nisshin Grain
(1917 on)
Bogo Medellin (1928
on), Central Azucarera
Tarlac (1928 on), San
Miguel
Kuhara Mining
(1916 on)
Benguet
36
Industry
British Malaya
French
Indochina
1873 to 1935
Hong Kong
Japan
Netherlands Indies
Philippine Islands
Shanghai
1873 to 1935
1897 to 1935
1873 to 1935
1914 to 1935
1873 to 1935
1911 to 1935
Ports (ship
building,
engineering,
docks,
wharves, and
warehouses)
Tanjong Pagar Dock (ends
1907), New Harbour Dock
(1876-1899) Howarth Erskine
(1901-1917), Riley
Hargreeves (1899-1917),
United Engineers (1917 on),
Maynard (1884-1933)
H. K.and Whampoa
Dock, H. K. and
Kowloon Wharf and
Godown (1887 on)
Shanghai Dock
Real Estate
Tanjong Pagar Land (18841893)
H. K. Land (1894 on)
Shanghai Land
(1889 on)
Rubber
Plantations
Bukit Rajah (1905-1917),
Pataling (1905-1917),
Selangor (1905-1917),
Highlands and Lowlands
(1906-1917), Ayer Molek
(1916 on), Glenealy (1916
on), New Serendah (1916
on), United Malacca (1916
on)
Transport
(Ship lines
and Railways)
Straits Steamship (1891 on),
Singapore Steamship (ends
1886)
Utilities
Caotchoucs de
l’Indochine
(1924 on),
Plantations des
Terres Rouges
(1926 on)
Koninklijke Paketvart
(1889 on), Java China
Japan Line (1902 on),
N. I. Spoorweg , Deli
Spoorweg (1888 on)
China Light and Power
(1901 on), H. K. Electric
(1894 on), H. K. and
China Gas (1899 on)
Shanghai Gas,
Shanghai Telephone
(1900 on), Shanghai
Water (1881 on)
37
Table II. Summary Statistics on Price Relatives of Silver and Industry Portfolios
Price changes for silver are measured with monthly log-differences of the price of silver. Industry portfolio returns are equally-weighted monthly log-differences of
the company share prices constructed following the classification scheme in Table 1. Time period is 1873 to 1935 except where noted in Table 1. All stock price
changes are expressed in local currency while silver and global stock index price changes are expressed in British pounds. The “Global Stock Index” is an equallyweighted average of returns on U.K. and U.S. indexes from a variety of sources detailed in the text. “Nobs” is the number of monthly observations for the series.
Price or Portfolio
Series
Silver
Global Stock
Index
National Stock
Index
Agricultural
Groups
Banking and
Insurance
Commercial
Industrial
Food
Price or Portfolio
Series
Region
Nobs
Mean
Median
Standard
Deviation
Skewness
Kurtosis
Lag 1
autocorrelation
Lag 2
autocorrelation
Lag 3
autocorrelation
London quote
U.K. and U.S.A.
756
753
-0.00132
0.003749
-0.00202
0.003618
0.040228
0.033628
-0.09368
0.324771
9.356039
7.989726
0.13375
0.168283
0.013234
-0.00095
0.013434
-0.13349
British Malaya
French Indochina
Hong Kong
Japan
Netherlands Indies
Philippine Islands
Shanghai
French Indochina
Netherlands Indies
British Malaya
French Indochina
Hong Kong
Netherlands Indies
Philippine Islands
British Malaya
French Indochina
Japan
Shanghai
British Malaya
Hong Kong
Japan
Philippine Islands
748
282
746
422
743
241
752
282
743
225
237
746
676
241
506
92
422
480
454
434
322
241
0.000452
-0.00185
0.000971
-0.00012
0.000979
0.000216
0.001808
-0.00631
0.000637
-0.01653
-0.00195
0.001839
-0.00026
-0.00705
0.002313
-0.00162
0.001411
0.00203
0.004315
0.005265
-0.00379
-0.00134
0.00006
-0.00355
0.001176
-0.00332
0.0014
0.0008
0.001282
-0.00974
-0.00087
-0.00285
-0.00338
0.003209
0.000835
0
0
-0.00592
-0.00455
-0.00041
0.003035
0.00303
0
0
0.050152
0.094184
0.054237
0.086237
0.036705
0.060967
0.039432
0.131152
0.051618
0.136291
0.098683
0.057324
0.030239
0.075503
0.073169
0.127555
0.089722
0.104341
0.065074
0.07519
0.117997
0.071941
0.495521
0.159294
-0.94226
0.370914
-0.13626
-0.10298
0.285046
0.043949
-0.17796
-7.23303
-0.69729
-0.7821
-0.57793
-1.51117
-0.09294
-0.31741
0.56482
0.869707
0.854276
-0.41319
0.379082
-1.2105
6.441743
1.165252
22.07041
5.680655
4.825777
6.319207
7.500608
1.944638
3.753583
83.89637
6.095678
24.16026
5.998269
7.436914
12.01656
4.317906
5.434859
8.803064
20.7101
6.732726
5.887934
15.15301
0.137165
0.02503
-0.03182
-0.10492
0.092839
-0.08982
0.060603
0.036224
0.049151
0.069111
-0.10263
-0.1246
-0.01489
-0.02918
0.031409
-0.09267
-0.09876
0.033177
-0.02635
0.060349
-0.00797
-0.0188
0.028491
-0.07306
0.015733
0.113313
-0.07419
-0.06808
0.006623
-0.08418
-0.1137
-0.02549
0.002063
-0.05437
-0.02149
0.048744
0.028512
-0.01834
0.128977
-0.02341
0.05357
-0.0237
0.112595
-0.14361
0.024586
-0.08439
-0.01407
-0.02491
-0.02628
0.048394
0.038844
-0.09043
-0.03497
-0.07126
-0.0382
0.082852
0.019765
0.071693
0.120726
0.000889
-0.00902
0.075813
-0.27568
-0.07205
-0.03524
-0.03844
Region
Nobs
Mean
Median
Standard
Deviation
38
Skewness
Kurtosis
Lag 1
autocorrelation
Lag 2
autocorrelation
Lag 3
autocorrelation
Mining
Ports
Real Estate
Rubber
Plantations
Transport
Utilities
British Malaya
French Indochina
Japan
Philippine Islands
British Malaya
Hong Kong
Shanghai
British Malaya
Hong Kong
Shanghai
British Malaya
French Indochina
British Malaya
Netherlands Indies
Hong Kong
Shanghai
582
176
53
241
748
745
751
103
558
545
367
134
675
740
555
726
0.002842
0.000915
0.001664
0.012812
-0.00018
-0.00111
-0.00063
-0.00104
-0.00157
0.002622
0.002467
-0.00236
0.000798
0.001594
0.003267
0.001802
0.00191
-0.00371
-0.00911
0.00242
0
-0.00042
0
-0.00447
-0.00242
0.000946
0
0.005226
0
0.00154
0.00224
0.00068
0.187687
0.102503
0.176516
0.14302
0.046077
0.068284
0.062633
0.201461
0.057304
0.071831
0.109433
0.146827
0.054217
0.042148
0.094663
0.042394
39
4.289596
0.865193
-0.23965
-1.70056
0.183637
-1.15605
-1.28696
2.276865
-0.56642
5.145755
0.698631
-0.18994
-0.1815
0.080643
-0.42046
0.434894
62.05853
3.466765
4.40299
15.20731
6.909588
15.59593
11.80344
24.78888
9.24827
73.86359
5.202513
1.139922
7.418486
6.889301
6.172975
12.3167
-0.07034
-0.02888
-0.42491
0.02797
0.094637
-0.0647
0.021773
-0.03979
0.08768
-0.12177
0.193845
0.03962
0.035537
0.085839
-0.08875
-0.00935
-0.05075
-0.05162
0.120833
0.036676
-0.00348
0.090403
-0.06285
-0.01133
0.007931
0.009361
0.072624
-0.05894
0.039696
0.032785
-0.05175
0.060709
-0.09927
-0.03167
-0.06195
0.042256
-0.01504
-0.03776
-0.01955
-0.40875
-0.00148
0.16472
0.053758
-0.26375
0.026785
0.02901
-0.02983
0.037581
Table III. Correlations between Price Changes of Silver, Global Stock Index, and National Aggregate Stock Portfolios
The table reports correlations between price changes of silver, our equally-weighted global index of U.K. and U.S. stocks, and equally-weighted returns indices of the
seven Asian countries and territories we study. Price changes are computed with log-differences expressed in local currency. Each cell includes the p-value in
parenthesis and the number of overlapping observations available for the pair of series in square brackets. See Table 1 for details of the individual stock series that
comprise the indices. Except where noted in Table 1, the monthly data series span the period from 1873 to 1935. “Silver Residual” refers to the series of residuals
from orthogonalizing the silver series over the global stock index series. China and Hong Kong were on silver for the entire sample period, Netherlands Indies
switched from silver to gold in March 1877, Japan switched on March 1897, Philippine Islands was formally pegged to the gold dollar in March 1903, British
Malaya switched from silver to gold in August 1904, French Indochina switched in May 1930.
Silver
Global
British
Malaya
French
Indochina
Hong Kong
Japan
Netherlands
Indies
Philippine
Islands
Shanghai
Silver
Residual
.1627
(<.001)
[752]
.1244
(<.001)
[748]
-.0039
(.948)
[282]
-.0058
(.873)
[746]
.1063
(.029)
[422]
.1297
(<.001)
[743]
.2663
(<.001)
[241]
.1109
(.002)
[752]
.9867
(<.001)
[752]
Global
.1680
(<.001)
[744]
.3327
(<.001)
[282]
.0272
(.459)
[742]
.1011
(.039)
[419]
.3534
(<.001)
[743]
.1205
(.062)
[241]
.0159
(.663)
[750]
0.000
(1.000)
[752]
British
Malaya
.2714
(<.001)
[282]
.0521
(.155)
[746]
.0998
(.040)
[422]
.1321
(<.001)
[735]
.0321
(.620)
[241]
.0622
(.090)
[744]
.0978
(.008)
[744]
French
Indochina
Hong Kong
-.0217
(.718)
[280]
.0024
(.969)
[255]
.3690
(<.001)
[279]
.0596
(.364)
[234]
-.0908
(.130)
[280]
-.0553
(.355)
[282]
.0339
(.489)
[420]
.0396
(.284)
[733]
-.0242
(.710)
[239]
.2081
(<.001)
[744]
-.0093
(.801)
[742]
40
Japan
.1440
(.003)
[413]
.0716
(.292)
[218]
.0146
(.767)
[419]
.0900
(.066)
[419]
Netherlands
Indies
.1232
(.059)
[235]
.0472
(.200)
[741]
.0742
(.043)
[743]
Philippine
Islands
-.0114
(.861)
[239]
.2522
(<.001)
[241]
Shanghai
.1120
(.002)
[750]
Table IV. Regressions of Industry Portfolio Price Changes on Silver Price Changes
Monthly industry portfolio price changes in local currency are regressed on silver price changes and a constant. Silver price changes
are orthogonalized over global stock returns prior to use in this table. Time period is ordinarily 1873 to 1935 but missing
observations may reduce this. China and Hong Kong were on silver for the entire sample period, Netherlands Indies switched from
silver to gold in March 1877, Japan switched on March 1897, Philippine Islands was formally pegged to the gold dollar in March
1903, British Malaya switched from silver to gold in August 1904, French Indochina switched in May 1930. Global stock index
returns are an equally-weighted average of U.K. and U.S. index returns and are included to control for general global economic
trends. T-statistics reported beneath each coefficient estimate are adjusted for serial correlation and heteroskedasticity following
Newey and West (1987) and White (1980).
Portfolio
Series
Region
Agricultural
Groups
French Indochina
282
Netherlands
Indies
743
British Malaya
225
French Indochina
237
Hong Kong
742
Netherlands
Indies
676
Philippine
Islands
241
British Malaya
502
Banking and
Insurance
Commercial
Industrial
French Indochina
Food
Mining
Number of
observations
92
Japan
419
Shanghai
478
British Malaya
450
Hong Kong
430
Japan
319
Philippine
Islands
241
British Malaya
578
French Indochina
176
Japan
Philippine
Islands
53
241
Constant
-0.01023
-1.39169
-0.00122
-0.67022
-0.01869
-1.80301
-0.00525
-0.909
0.001803
0.922492
-0.00068
-0.59376
-0.00774
-1.63253
0.001485
0.452119
-0.01
-0.90136
0.000729
0.176027
0.001799
0.374759
0.004214
1.411889
0.005429
1.445565
-0.00451
-0.67679
-0.00174
-0.39657
0.003067
0.407098
-0.00284
-0.40526
0.001856
0.092291
0.011432
1.311092
41
Slope
coefficient on
silver price
change
-0.14843
-1.01992
0.022718
0.404725
0.165306
1.221559
-0.24186
-1.75524
-0.28288
-3.92931
0.078101
2.580731
0.179971
2.148911
0.041204
0.603529
-0.07833
-0.65313
0.17546
1.785531
0.257481
2.111236
-0.00829
-0.11782
-0.11594
-1.2913
0.065645
0.571135
0.315005
3.562357
0.223754
1.795352
-0.18676
-1.48806
-0.54383
-0.97015
0.264171
2.213609
Slope coefficient
on global stock
index return
0.991784
6.106007
0.516503
7.355439
0.5948
1.461257
0.734403
3.323498
0.105605
1.772672
0.109742
2.114579
0.134926
1.490183
0.1635
1.734828
1.161387
5.501225
0.228031
2.513321
0.017327
0.130878
0.068881
1.135643
0.093604
1.132601
0.191953
1.622934
0.083384
1.110087
0.180785
0.884841
0.658271
4.165133
-0.13944
-0.19057
0.26945
1.341229
Adjusted
R2
0.102427
0.108672
0.00308
0.105333
0.040863
0.023973
0.018364
0.00387
0.140619
0.01462
0.008735
-0.00276
0.002853
-0.00028
0.058683
0.000656
0.092444
-0.03042
0.01153
Portfolio
Series
Region
Ports
British Malaya
744
Hong Kong
741
Shanghai
749
British Malaya
103
Hong Kong
554
Shanghai
543
British Malaya
363
French Indochina
134
British Malaya
671
Netherlands
Indies
740
Hong Kong
551
Shanghai
725
Real Estate
Rubber
Plantations
Transport
Utilities
Number of
observations
Constant
-0.00049
-0.2765
-0.00107
-0.44321
-0.00065
-0.27982
-0.00228
-0.12024
-0.00136
-0.54384
0.002341
0.822561
-9.9E-05
-0.01654
-0.00829
-0.69307
0.000315
0.147376
0.000114
0.074446
0.00391
0.99983
0.002172
1.43105
42
Slope
coefficient on
silver price
change
0.077102
1.790117
0.063091
1.216343
0.164881
2.546128
0.05194
0.11246
0.02572
0.46765
0.023167
0.513797
0.059514
0.587639
-0.11434
-0.40984
0.090258
1.310431
0.10372
2.065997
0.066274
0.961769
0.076901
1.631724
Slope coefficient
on global stock
index return
0.093411
1.559125
0.07207
0.888163
0.034901
0.445851
0.373847
0.581447
0.046005
0.740884
0.088504
1.131075
0.665022
3.461879
1.158546
5.098807
0.129117
1.782166
0.408697
4.591633
-0.02165
-0.21344
-0.03838
-0.61465
Adjusted
R2
0.00646
-4.8E-05
0.008683
-0.01847
-0.00231
-0.00139
0.059848
0.165455
0.008568
0.111791
-0.00255
0.003511
Table V. Regressions of Industry Portfolio Price Changes on Silver Price Changes and Currency Dummy Variables
Monthly industry portfolio price changes in local currency are regressed on orthogonalized silver price changes, a constant, and slope and intercept dummies
indicating the currency regime. The currency dummy variable is set to one for months on the silver standard and set to zero otherwise. British Malaya switched
from silver to gold in August 1904, French Indochina switched in May 1930, and Netherlands Indies in March 1877. Regressions are estimated only for those
countries and territories which abandoned the silver standard during our sample period and only for those portfolios with sufficient data to span both silver and nonsilver subperiods. Except where noted in the previous table, time period is 1873 to 1935. Global stock index returns are an equally-weighted average of U.K. and
U.S. index returns and are included to control for general global economic trends. T-statistics in parentheses beneath each coefficient estimate are adjusted for serial
correlation and heteroskedasticity following Newey and West (1987) and White (1980).
Portfolio Series
Region
Agricultural
Groups
French Indochina
Banking and
Insurance
Commercial
Industrial
Netherlands
Indies
French Indochina
British Malaya
French Indochina
Food
British Malaya
Mining
British Malaya
French Indochina
Ports
British Malaya
Rubber
Plantations
Transport
French Indochina
British Malaya
Netherlands
Indies
Constant
-0.00178
-0.93521
-0.00136
-0.08351
-0.01357
-1.10634
0.002005
0.558297
0.001464
0.08609
0.004046
1.204402
0.00006
0.011132
-0.0125
-0.943
-0.00281
-0.95367
-0.00312
-0.16609
0.00255
0.835578
-0.00107
-0.69096
Slope coefficient
on silver currency
dummy variable
0.008392
1.421464
-0.00918
-0.50357
0.015826
1.171339
-0.00385
-0.48768
-0.01471
-0.68302
0.000983
0.135321
0.009468
0.487783
0.018165
1.196761
0.00493
1.423337
-0.01569
-0.66586
-0.00455
-1.09802
0.018403
2.61323
Slope coefficient
on silver price
change
0.023013
0.406678
-0.22126
-1.44245
-0.09667
-0.49198
0.000669
0.009545
-0.09633
-0.95026
-0.02768
-0.38612
0.135427
0.913479
-0.08335
-0.57309
0.001579
0.030073
-0.08935
-0.27698
0.056055
0.708413
0.105004
2.074141
43
Slope coefficient
on silver price
change times
silver currency
dummy variable
-0.13256
-0.59153
0.157366
0.576101
-0.19177
-0.77099
0.470061
2.213416
0.118952
0.444929
0.242166
0.708906
0.385447
1.263049
-0.35447
-1.41894
0.31618
4.138897
-0.28625
-0.55057
0.116613
0.823223
-0.43262
-1.40585
Slope coefficient
on global stock
index return
0.521459
7.325007
1.309568
8.212642
1.073526
3.543423
0.118806
1.216972
1.584756
8.264109
0.098037
1.581345
0.26488
1.181544
0.779429
3.961097
0.150481
2.057779
1.107335
4.445655
0.198542
2.427213
0.423428
4.719576
Slope coefficient
on global stock
index return
times silver
currency dummy
variable
-0.17784
-0.55032
-0.77131
-2.71692
-1.15107
-3.09765
0.53699
1.529969
-1.23526
-3.23748
-0.31167
-1.43927
-0.38862
-0.63979
-0.58022
-1.86946
-0.15655
-1.58331
0.340929
0.718395
-0.29219
-2.05371
-0.70341
-2.12498
Adjusted R2
0.106911
0.110554
0.155924
0.010838
0.162661
-0.00451
-0.00174
0.102471
0.021422
0.150969
0.014859
0.124498
Table VI. The Behavior of Earnings and Dividends of the Hong Kong and Shanghai
Banking Corporation, 1876 to 1935
Yearly earnings per share and dividend per share and year-end stock price of the bank are expressed in silver currency. T-statistics
in parentheses beneath each coefficient estimate are adjusted for serial correlation and heteroskedasticity following Newey and West
(1987) and White (1980).
Dependent
variable
Regression
methodology
Logdifferences
of earnings
per share
OLS
Logdifferences
of dividends
per share
OLS
Bonus
dividend per
share
Probit
Intercept
Slope on logdifferences of
price of silver
.0186
-.5109
(.60)
(-4.21)
.0155
-
Slope on log-differences of earnings per
share at lag
(1.02)
.4502
-
(2.38)
44
Adjusted
R2
0
1
2
-
-
-
.070
.5967
.1367
.0200
.681
(4.91)
(2.17)
(.33)
2.478
1.693
.5608
(2.68)
(2.11)
(.81)
-
Table VII. Sign and Significance of Associations between National Portfolio Returns and
Changes in Silver Volatility (old)
A bivariate “garch-in-mean” specification detects the dependence of monthly national portfolio price changes on changes in
silver price volatility:
εs,t = rs,t - β0,s - β1,s hss,t
εp,t = rp,t - β0,p - β1,p hss,t
hss,t = γss,0 + γss,1 hss,t-1 + γss,2(εs,t-1)2
hpp,t = γpp,0 + γpp,1 hpp,t-1 + γpp,2(εp,t-1)2
hsp,t = γsp(hss,thpp,t)0.5
(1a)
(1b)
(1c)
(1d)
(1e)
Betas and gammas are parameters, “s” and “p” subscripts refer to silver price change and portfolio return series. (1a)
defines the residuals from an equation which allows (raw) silver price changes to depend on silver price volatility while (1b)
allows portfolio returns to depend on silver price volatility. Coefficient β1,p tests propositions H4 and H5. (1c) and (1d)
define the dynamics of the volatility of the silver price change and the portfolio return respectively while (1e) specifies a
constant correlation between the silver and portfolio processes. A second specification includes a slope dummy term to test
whether the exposure of stock returns to conditional silver volatility changes with a change in exchange rate regime.
Dummy variable equals one for silver standard, zero otherwise. Netherlands Indies switched from silver to gold in March
1877, British Malaya switched in August 1904, and French Indochina switched in May 1930.
Region
British
Malaya
French
Indochina
Hong Kong
Japan
Netherlands
Indies
Philippine
Islands
Shanghai
Basic Model
Model with dummy variable for exchange rate regime
Slope coefficient (standard
error) for impact of
conditional silver volatility on
stock return
Slope coefficient (standard
error) for impact of
conditional silver volatility
on stock return
Slope coefficient (standard
error) for impact of
conditional silver volatility
times silver standard dummy
on stock return
-.9156
(.8352)
2.446
(1.835)
-1.8535
(.8665)
-2.0665
(1.2293)
-.9316
(.6604)
-.9592
(1.1161)
-1.7372
(.6260)
-.9065
(.8047)
2.5772
(2.1575)
-
-.7773
(2.665)
-.2139
(2.5802)
-
-
-
-.9122
(.6568)
-
4.0209
(9.2062)
-
-
-
45
Industry portfolios with
significant individual
results (sign of
relationship)
Mining (-), Ports (-),
Rubber (-)
Mining (+)
Utilities (-)
Table VIII. Event-Time Reactions of Stock Prices to U.S. Silver Purchase Legislation
Cumulative return residuals (z-statistics) are presented for a four month event window around five principal events
concerning initiation, increase, or removal of price support for silver by the U.S. government. For each return series and
event, we first determine whether there are at least 30 of a possible 36 observations of returns data in the three year period
from four to forty months prior to the event and in the four month event “window” from the event month back to the
previous three months. If this condition is satisfied, we regress the portfolio return series on the silver residual and global
stock index return series from t-40 to t-4. The regression residuals are used to compute an estimate of the standard
deviation of residuals while the regression coefficients are used to generate residuals in the t-3 to t event window. The ratio
of the sum of event window residuals to the estimated standard deviation times the square root of 4 is a z-statistic with four
degrees of freedom. Joint test across all portfolios is performed following Schipper and Thompson (1983).
Passage of BlandAllison Act,
February 1878
Passage of
Sherman Act, July
1890
Senate passage of
free coinage of
silver, July 1892
Repeal of purchase
clause of Sherman Act,
November 1893
Passage of Silver
Purchase Act, June
1934
British Malaya
Index
French
Indochina index
Hong Kong
index
Japan index
0.0283
0.6635
-
-0.1496
-0.8445
-
-0.1913
-1.4613
-
0.0976
0.7856
-
0.0713
0.2581
-
0.0315
0.3709
-
-0.1284
-1.5199
-
0.0389
0.4452
-
Netherlands
Indies index
Philippine
Islands index
Shanghai index
-0.0466
-0.6807
-
-0.0146
-0.2473
-
0.0389
1.0202
-
-0.0223
-0.7171
-
-0.0227
-0.496
-0.0499
-0.4373
-0.051
-0.4708
-0.0904
-2.1066
0.2575
2.1132
0.0297
0.1213
-0.0612
-0.5811
-0.0518
-0.3137
-0.0251
-0.2144
-0.1148
-0.6597
-0.0196
-0.1669
8/31
11/31
14/31
13/31
27/31
3.353
(.910)
5.499
(.905)
46.715
(<.001)
29.100
(.006)
104.686
(<.001)
Number of
industry
portfolios
available
χ2 test (p
value) for
industry
portfolio
reactions jointly
zero
Industry
portfolios with
significant
positive
reactions
Industry
portfolios with
significant
negative
reactions
N.I. Bank (19%)
H.K. Bank (-21%)
46
Malaya Rubber (50%)
N.I. Bank (-15%),
Shanghai Port (-13%),
Shanghai Utilities
(-13%)
H.K. Real Estate
(-21%)
Table IX. Event-Time Reactions of Stock Prices to Changes in Currency Standards
Cumulative return residuals (z-statistics) are presented for a four month event window around five principal switches from silver to
gold or from gold to paper. For each return series and event, we first determine whether there are at least 30 of a possible 36
observations of returns data in the three year period from four to forty months prior to the event and in the four month event
“window” from the event month back to the previous three months. If this condition is satisfied, we regress the portfolio return series
on the silver residual and global stock return series from t-40 to t-4. The regression residuals are used to compute an estimate of the
standard deviation of residuals while the regression coefficients are used to generate residuals in the t-3 to t event window. The ratio
of the sum of event window residuals to the estimated standard deviation times the square root of 4 is a z-statistic with four degrees
of freedom. Joint test across all portfolios is performed following Schipper and Thompson (1983).
British Malaya
Index
French
Indochina index
Hong Kong
index
Japan index
Netherlands
Indies index
Philippine
Islands index
Shanghai index
Number of
industry
portfolios
available
χ2 test (p
value) industry
portfolio
reactions jointly
zero
Industry
portfolios with
significant
positive
reactions
Industry
portfolios with
significant
negative
reactions
British Malaya
changes from
silver to gold,
August 1904
U.K. and the
Netherlands
change from paper
to gold, April 1925
French Indochina
changes from silver
to gold, May 1930
U.K. changes from gold
to paper, September
1931
Japan changes from
gold to paper,
December 1931
0.032
0.2496
-
0.0442
0.5717
0.0285
0.3182
-0.1311
-0.9486
0.0131
0.127
0.0961
0.6926
0.0122
0.2112
-0.2048
-2.0224
0.049
0.5462
-0.052
-0.5067
-0.3249
-3.3996
0.0852
0.8457
-0.2334
-2.1939
-0.0932
-2.2897
-0.0192
-0.3378
0.0897
1.3023
-0.0146
-0.1892
-0.2726
-2.0523
-0.0189
-0.179
0.3775
2.4797
0.0352
0.415
0.1423
3.3375
-0.0791
-1.0951
0.085
1.1182
0.0548
0.3877
0.2413
2.2464
0.544
3.4265
-0.1715
-1.8072
0.2591
6.2859
0.1231
1.4978
17/31
24/31
27/31
27/31
27/31
72.116
(<.001)
201.659
(<.001)
189.536
(<.001)
128.235
(<.001)
146.871
(<.001)
Indochina Ag (55%),
Philippine Bank (30%),
Japan Comm/Ind
(53%), Japan Food
(58%), Philippine Food
(19%), Philippine Mine
(39%), H.K. Port
(42%), H.K. Real
Estate (23%), Shanghai
Utilities (18%)
N.I. Bank (-14%),
Malaya Comm/Ind
(-35%)
0.2416
4.9932
0.1012
2.9657
-
N.I. Ag (19%),
H.K. Food
(40%), H.K. Port
(17%), N.I.
Transport (66%),
H.K. Utilities
(36%)
H.K. Utilities
(34%)
H.K. Real Estate
(24%), Shanghai
Real Estate (35%)
Japan Comm/Ind (31%),
Malaya Food (16%),
Japan Food (49%),
Philippine Food (19%)
Philippine Bank
(-36%)
Japan Comm/Ind
(-25%), Malaya
Mining (-25%),
Indochina Mining
(-42%), N.I.
Transport (-13%),
Malaya Ports (12%)
Indochina Mining
(-34%), Indochina
Rubber (-46%)
47
Table X. The Power of Lagged Silver and Global Stock Returns to Forecast Factor-Mimicking Portfolio Returns
The table reports the final pass of a three-pass procedure. The first pass is time series regressions of the industry portfolio excess returns (expressed in
U.K. pounds) on the silver price change residual, global stock index excess return, and equally-weighted Asian stock index excess return. This yields
estimates of risk exposures or betas. In the second pass, cross-sectional regressions of stock portfolio returns on the estimated risk exposures
produced at the previous step produce estimates of the ex post returns for three portfolios with unit risk exposure to each of the three factors and one
portfolio with no exposure to any factor. See Fama and MacBeth (1973). In the third pass, the mimicking portfolio returns are regressed on lags of
the silver price change residual, global stock index return, Asian stock index return, and global interest rate. This measures the ability to predict the
mimicking portfolio returns or detect ex ante risk premiums in the mimicking portfolio returns. See Ferson and Harvey (1991). T-statistics reported
beneath each coefficient estimate are adjusted for serial correlation and heteroskedasticity following Newey and West (1987) and White (1980). More
efficient one-pass procedures are not employed due to the complex patterns of missing observations across the 31 portfolio return series in the sample.
Each regression uses 750 observations.
Return series used as dependent
variable
Intercept
coefficient
(t-statistic)
Adjusted
Slope coefficient (t-statistic) on lagged :
R2
Silver price
change residual
Global stock index
excess return
Asian stock index
excess return
Global interest rate
Mimicking portfolio for exposure to
silver price change residual
2.631 E-3
-.3627
-.1721
-.1039
-2.383
(.20)
(-2.36)
(-1.06)
(-.35)
(-.47)
Mimicking portfolio for exposure to
global stock index excess return
8.715 E-3
-.5592
-.1405
.2561
-3.926
(.77)
(-3.92)
(-.75)
(1.29)
(-.97)
Mimicking portfolio for exposure to
Asian stock index excess return
9.358 E-3
.1829
.0868
-.1957
-2.844
(1.81)
(3.78)
(1.42)
(-1.21)
(-1.44)
Portfolio with zero exposure to any
three factors
-6.541 E-3
.2884
.1419
.1365
1.364
(-1.33)
(5.84)
(2.46)
(1.25)
(.79)
48
.003
.023
.011
.032
Figure 1.
49
Figure 2. The plot shows the cumulative yearly purchasing power parity deviations for the Netherlands Indies
(gold) guilder per Chinese silver dollar and British pound per Chinese silver dollar exchange rates. The
purchasing power parity deviation equals the rate of change of the value of the Chinese dollar minus the
difference between Chinese and foreign wholesale price inflation. Values greater than one indicate that the
Chinese currency is overvalued relative to the other currency.
50
Figure 3. The plots show the price of silver, global stock indexes, and national stock indexes. All series have been scaled to unity.
51
Figure 4. The plot shows the yearly dividend yield and yearly ratio of earning-per-share to price for the Hong
Kong and Shanghai Banking Corporation. (old)
52
Figure 5. The plot shows the interim, final, and bonus dividend (U.K. pounds per share) for the Hong Kong and
Shanghai Banking Corporation.
53
Appendix Table. Significance of Time-Variation in Risk Exposures
GMM is used to test whether the exposures of stock portfolio returns to the silver change residual and the global stock index return vary linearly with
lags of those two factors. We minimize the moment condition of Ferson (1990) while replacing the constant covariance parameter with one which is
linearly dependent on lags of the silver price change residual and the global stock index return.
Portfolio Series
Region
Agricultural
Groups
French
Indochina
Netherlands
Indies
British Malaya
Banking and
Insurance
French
Indochina
Hong Kong
Commercial
Industrial
Netherlands
Indies
Philippine
Islands
British Malaya
French
Indochina
Japan
Shanghai
Food
British Malaya
Hong Kong
Japan
Constant
component (tstatistic) of the
silver risk
exposure
-0.55897
-3.66658
0.019548
0.327505
-0.5338
-2.90133
-0.50593
-2.92621
-1.19337
-13.8717
0.077163
2.453248
0.187032
2.141322
-0.04025
-0.58942
-0.28982
-1.44381
0.17873
1.868993
-0.64659
-4.81132
-0.04688
-0.64135
-1.01643
-10.0096
0.053448
Slope coefficient
(t-statistic)
permitting the
silver risk
exposure to vary
linearly with the
lagged silver
change residual
Slope coefficient
(t-statistic)
permitting the
silver risk
exposure to vary
linearly with the
lagged global
index return
Constant
component (tstatistic) of the
global index risk
exposure
Slope coefficient
(t-statistic)
permitting the
global index risk
exposure to vary
linearly with the
lagged silver
change residual
Slope coefficient
(t-statistic)
permitting the
global index risk
exposure to vary
linearly with the
lagged global
index return
Wald statistic
(p-value) testing
whether slopes
permitting silver
risk exposure to
vary are jointly
zero
Wald statistic
(p-value) testing
whether slopes
permitting
global index risk
exposure to vary
are jointly zero
-0.07458
-0.04068
0.600196
0.967105
2.29137
1.11242
6.631431
3.104209
-0.90865
-1.0133
-0.25515
-0.72795
0.488214
0.469903
0.040935
0.051553
0.00686
0.002507
-0.31866
-0.29481
1.263352
0.825656
-0.48644
-0.57369
-1.33405
-1.12175
-2.77594
1.533728
0.436149
-0.73553
-0.59334
3.458254
0.71262
-1.54627
-0.39471
0.187988
0.103704
0.716799
1.1226
-1.51214
-0.72745
0.070287
0.045992
-7.06694
-2.01979
0.119554
0.056612
-0.68958
-0.23037
0.656529
0.399147
-0.98084
-0.42738
1.460328
0.873198
5.220676
0.514836
7.511025
0.417331
1.084693
0.566575
2.852375
-0.04736
-0.42446
0.10991
2.269685
0.166324
1.835623
0.206714
2.133916
0.924679
3.571501
0.258496
2.91102
-0.11347
-0.74231
0.106791
1.652439
-0.10211
-0.84519
0.225039
-0.32224
-0.1714
-0.18134
-0.24387
-4.19214
-1.05943
8.508632
4.333964
0.610635
0.490873
-0.40525
-0.77559
-1.83486
-1.71415
-3.05108
-2.5279
5.79175
3.298269
0.183567
0.16853
0.818072
0.434643
-1.62075
-2.06478
0.769729
0.528785
-0.18635
0.695488
0.708152
-0.28517
-0.62687
-1.41704
-0.10192
-1.4049
-1.32104
0.920317
1.2161
-0.02263
-0.07069
-1.44627
-2.55131
-0.32178
-0.50996
-0.86433
-0.34351
-1.57044
-2.72125
-1.74179
-1.75367
-0.55014
-1.32621
0.937682
1.225328
-2.10449
0.190694
0.909058
1.266646
0.530825
1.346464
0.510057
9.798216
0.007453
1.033683
0.596401
1.793172
0.40796
0.723248
0.696544
0.004786
0.99761
4.086477
0.129608
0.089561
0.956207
0.730686
0.693959
0.466089
0.792119
1.492822
0.474065
3.54953
0.55676
0.757009
0.452035
0.797704
1.150742
0.562496
21.70419
0.000019
1.714721
0.42428
0.605575
0.738756
8.829479
0.012098
6.528402
0.038227
10.93521
0.004221
7.52879
0.023182
3.358321
0.186531
5.670476
0.058705
1.700422
0.427325
8.717533
54
Mining
Philippine
Islands
British Malaya
French
Indochina
Japan
Ports
Philippine
Islands
British Malaya
Hong Kong
Shanghai
Real Estate
British Malaya
Hong Kong
Shanghai
Rubber
Plantations
Transport
British
Malaya
French
Indochina
British Malaya
Utilities
Netherlands
Indies
Hong Kong
Shanghai
0.442018
0.319876
3.595399
0.092886
0.753264
-0.40696
-2.55445
0.546044
0.578896
0.27973
2.218902
-0.08332
-1.73627
-0.84751
-13.0267
-0.74671
-9.70874
-0.31666
-0.5464
-0.89043
-12.8003
-0.89494
-16.0827
0.095512
0.894365
-0.24847
-0.75878
-0.04589
-0.71241
0.100354
1.947636
-0.84872
-10.4586
-0.83138
-13.1865
-1.8347
1.491957
1.409786
2.129233
1.51885
3.194236
1.397031
-7.4974
-0.52285
-4.32284
-2.8827
0.125712
0.251475
-0.35687
-0.52615
1.111743
1.393221
-15.16
-1.76677
-0.88972
-1.10612
-1.22629
-1.90697
-2.50465
-1.9942
2.735127
0.593305
0.029845
0.043883
-0.1327
-0.24739
1.047383
1.1136
0.409517
0.637768
0.582513
-1.92771
-0.91044
-6.17128
-2.25309
2.755928
0.810972
-91.0469
-1.57887
-1.70949
-0.56978
1.227856
1.215373
0.592812
0.432425
2.049929
1.270412
-12.3928
-0.30044
-0.7431
-0.47587
-0.14632
-0.11691
-0.58694
-0.2412
4.697593
0.657464
0.987915
0.736578
-1.00849
-0.94175
-1.58286
-0.86742
0.352129
0.271064
2.018838
0.10824
1.290403
0.158303
0.794407
0.563081
3.786051
-0.32247
-0.461
0.234706
1.131874
0.114708
1.881527
-0.1031
-1.00733
-0.09501
-0.97485
0.063026
0.091741
-0.12984
-1.23124
-0.06939
-0.61292
0.577021
3.297177
1.17323
5.495075
0.150077
2.094803
0.401848
4.92525
-0.19671
-1.44925
-0.17285
-1.95749
55
-0.1409
-0.53174
-0.5366
-1.22334
-0.54593
5.312588
3.56632
41.91429
1.826619
1.66563
0.679938
-1.6141
-2.37374
0.325695
0.285256
-0.73023
-0.67482
-45.0438
-1.19475
-0.33407
-0.28115
-0.18874
-0.14821
0.52924
0.248971
3.312775
1.517398
-1.76462
-2.23467
-0.33596
-0.37934
-0.13599
-0.08906
-0.2304
-0.24127
-2.94731
-0.47572
-0.90652
2.459277
1.863501
-0.49857
-0.62057
9.824723
0.197881
3.280554
2.528743
-0.65186
-1.57604
1.954822
2.81446
-0.90919
-1.38012
37.46509
1.110815
0.859042
1.23274
0.774491
1.036787
5.436443
4.928679
-3.11439
-2.59156
-0.12377
-0.2566
0.736261
1.360216
2.06232
2.303126
0.212047
0.336377
0.169523
2.715973
0.257178
7.322155
0.025705
2.471778
0.290576
2.497289
0.286893
8.783633
0.012378
1.553448
0.45991
0.454808
0.796599
3.628636
0.162949
4.038326
0.132767
1.459407
0.482052
3.656014
0.160734
4.081481
0.129932
0.72778
0.694968
0.54556
0.76126
0.956736
0.619794
1.973671
0.372754
0.491802
0.781999
0.012794
1.04132
0.594128
3.805981
0.149122
13.55142
0.001141
3.36807
0.185624
6.632423
0.03629
8.08893
0.017519
7.995552
0.018356
2.351906
0.308525
2.220416
0.32949
1.6121
0.446619
1.102054
0.576357
24.32416
0.000005
9.581505
0.008306
5.049353
0.080084
1.995817
0.36865
5.321565
0.069893
0.196747
0.90631
Appendix Table. Sign and Significance of Associations between Industry Portfolio Returns
and Changes in Silver Volatility
A bivariate “garch-in-mean” specification detects the dependence of monthly industry portfolio price changes on changes in
silver price volatility:
εs,t = rs,t - β0,s - β1,s hss,t
εp,t = rp,t - β0,p - β1,p hss,t
hss,t = γss,0 + γss,1 hss,t-1 + γss,2(εs,t-1)2
hpp,t = γpp,0 + γpp,1 hpp,t-1 + γpp,2(εp,t-1)2
hsp,t = γsp(hss,thpp,t)0.5
(1a)
(1b)
(1c)
(1d)
(1e)
Betas and gammas are parameters, “s” and “p” subscripts refer to silver price change and portfolio return series. (1a)
defines the residuals from an equation which allows (raw) silver price changes to depend on silver price volatility while (1b)
allows portfolio returns to depend on silver price volatility. Coefficient β1,p tests propositions H4 and H5. (1c) and (1d)
define the dynamics of the volatility of the silver price change and the portfolio return respectively while (1e) specifies a
constant correlation between the silver and portfolio processes. A second specification includes a slope dummy term to test
whether the exposure of stock returns to conditional silver volatility changes with a change in exchange rate regime.
Dummy variable equals one for silver standard, zero otherwise. Netherlands Indies switched from silver to gold in March
1877, British Malaya switched in August 1904, and French Indochina switched in May 1930.
Portfolio
Series
Region
Agricultural
Groups
French Indochina
Netherlands
Indies
Banking and
Insurance
British Malaya
French Indochina
Hong Kong
Netherlands
Indies
Philippine
Islands
Commercial
Industrial
British Malaya
French Indochina
Japan
Shanghai
Portfolio
Series
Region
Basic Model
Model with dummy variable for exchange rate regime
Slope coefficient
(standard error)
for impact of
conditional silver
volatility on stock
return
Slope coefficient
(standard error) for
impact of conditional
silver volatility on stock
return
3.55361
2.3656
-.77883
.80746
-43.248
72.3205
3.7033
1.9312
-1.4756
1.8085
-.514139
.65138
-.94148
1.1651
-1.38965
1.3525
2.4944
1.99965
-1.78616
1.3978
-2.56132
2.1933
Basic Model
Slope coefficient (standard
error) for impact of
conditional silver volatility
times silver standard dummy
on stock return
4.51198
2.4729
-.840603
.90206
-2.1619
3.1413
-6.52204
9.60430
.69336
1.6063
2.76341
3.0874
-1,18661
1.4347
2.30556
1.9681
-3.60116
6.2417
-2.89774
12.8917
Model with dummy variable for exchange rate regime
56
Slope coefficient
(standard error)
for impact of
conditional silver
volatility on stock
return
Food
British Malaya
Hong Kong
Japan
Philippine
Islands
Mining
British Malaya
French Indochina
Japan
Philippine
Islands
Ports
British Malaya
Hong Kong
Shanghai
Real Estate
Shanghai
British Malaya
French Indochina
Transport
British Malaya
Netherlands
Indies
Utilities
-1.41759
.96844
-1.028613
2.07678
-1.31348
1.4887
-1.50517
2.11902
-11.03213
1.6441
5.9594
2.4501
4623.319
31350.04
2.38349
4.55600
-1.40369
.73078
-1.21536
1.61401
-1.87795
1.5574
Slope coefficient (standard
error) for impact of
conditional silver volatility
times silver standard dummy
on stock return
-1.57097
1.03207
-3.51221
4.9321
-11.04337
1.6605
5.83381
2.5275
-5.85260
4.28569
9.66070
6.38054
-1.521138
.72836
1.83696
2.3359
5.6234
4.3932
-1.87108
.91064
-.724944
.83412
12.2562
19.3090
.064072
2.37714
10.26625
9.3810
British Malaya
Hong Kong
Rubber
Plantations
Slope coefficient
(standard error) for
impact of conditional
silver volatility on stock
return
Hong Kong
Shanghai
-1.57703
2.27116
5187.863
4358.891
-5.07492
2.1672
-41.0519
46.7128
-.145136
.85131
-.773034
.82167
-.0739648
2.78630
-3.66087
1.01713
57
Appendix Table. Cumulative Event-Time Reactions of Industry Portfolio Price Changes to
U.S. Silver Purchase Legislation
Cumulative return residuals (z-statistics) are presented for a four month event window around five principal
events concerning initiation, increase, or removal of price support for silver by the U.S. government. For each
combination of sub-industry portfolio and legislative event, we first determine whether there are at least 30 of a
possible 36 observations of returns data in the three year period from four to forty months prior to the event and
in the four month event “window” from the event month back to the previous three months. If this condition is
satisfied, we regress the portfolio return series on the silver residual and global stock index return series from t40 to t-4. The regression residuals are used to compute an estimate of the standard deviation of residuals while
the regression coefficients are used to generate residuals in the t-3 to t event window. The ratio of the sum of
event window residuals to the estimated standard deviation times the square root of 4 is a z-statistic with four
degrees of freedom. China and Hong Kong were on silver for the entire sample period, Netherlands Indies
switched from silver to gold in March 1877, Japan switched on March 1897, Philippine Islands was formally
pegged to the gold dollar in March 1903, British Malaya switched from silver to gold in August 1904, French
Indochina switched in May 1930.
Portfolio Series
Region
Agricultural
Groups
French
Indochina
Netherlands
Indies
British Malaya
Banking and
Insurance
French
Indochina
Hong Kong
Commercial
Industrial
Netherlands
Indies
Philippine
Islands
British Malaya
French
Indochina
Japan
Shanghai
Food
British Malaya
Hong Kong
Passage of
Bland-Allison
Act, February
1878
-0.00751
-0.09613
0.016955
0.05881
-
Passage of
Sherman Act,
July 1890
Senate
passage of
free coinage
of silver, July
1892
Repeal of
purchase clause
of Sherman Act,
November 1893
Passage of
Silver Purchase
Act, June 1934
-0.05765
-0.64314
0.054314
0.457727
0.012905
0.242846
0.119585
1.331782
-
0.085612
1.46924
0.095882
0.632057
-0.20854
-2.17632
0.193704
3.216209
-
-0.02436
-0.52991
-0.08632
-0.5183
-0.07532
-0.67544
-0.14816
-2.57944
-
0.099485
0.342441
0.06361
0.438181
0.183166
0.742764
-0.04659
-0.28057
-0.06949
-0.65605
-0.2931
-1.37408
0.104195
0.715709
-0.08863
-0.56727
-0.18773
-0.87071
0.162421
1.704642
-0.12191
-0.76774
58
Portfolio Series
Region
Japan
Mining
Philippine
Islands
British Malaya
French
Indochina
Japan
Ports
Philippine
Islands
British Malaya
Hong Kong
Shanghai
Real Estate
British Malaya
Hong Kong
Shanghai
Rubber
Plantations
British Malaya
Transport
French
Indochina
British Malaya
Utilities
Netherlands
Indies
Hong Kong
Shanghai
Passage of
Bland-Allison
Act, February
1878
Passage of
Sherman Act,
July 1890
0.03148
0.434464
0.14529
0.396128
-0.03167
-0.39538
0.006194
0.097088
-0.06148
-0.7718
0.005887
0.096626
-0.38117
-0.3224
0.004944
0.079403
0.102126
0.848079
-0.01776
-0.29017
-0.23693
-0.82718
-0.05579
-1.38723
0.0184
0.181475
59
Senate
passage of
free coinage
of silver, July
1892
Repeal of
purchase clause
of Sherman Act,
November 1893
Passage of
Silver Purchase
Act, June 1934
-0.74039
-1.58499
-0.02581
-0.42846
-0.07738
-0.65652
0.023504
0.292724
0.090434
0.152447
-0.08834
-0.93261
-0.20029
-0.51209
-0.05925
-1.25058
-0.18895
-0.60543
-0.02041
-0.24325
0.261143
0.698114
-0.01076
-0.22208
-0.05278
-0.50351
-0.1627
-2.61015
0.09422
0.976399
-0.00443
-0.04402
0.021828
0.439569
0.104656
0.377738
-0.16771
-2.53763
0.001618
0.00782
-0.03524
-0.20864
0.145188
0.516188
-0.07739
-0.31678
-0.2343
-0.77736
0.098601
0.687241
-0.01183
-0.0608
-0.13897
-0.76695
-0.24494
-1.56163
-0.09391
-0.52824
0.502533
2.007462
0.058539
0.160806
0.163027
1.467127
-0.06954
-0.44393
-0.10235
-0.4826
-0.00184
-0.01205
Appendix Table. Cumulative Event-Time Reactions of Industry Portfolio Price Changes to Changes in Currency
Standards
Cumulative return residuals (z-statistics) are presented for a four month event window around five principal switches from silver to gold or from gold to paper. For each
combination of sub-industry portfolio and legislative event, we first determine whether there are at least 30 of a possible 36 observations of returns data in the three year period
from four to forty months prior to the event and in the four month event “window” from the event month back to the previous three months. If this condition is satisfied, we
regress the portfolio return series on the silver residual and global stock return series from t-40 to t-4. The regression residuals are used to compute an estimate of the standard
deviation of residuals while the regression coefficients are used to generate residuals in the t-3 to t event window. The ratio of the sum of event window residuals to the
estimated standard deviation times the square root of 4 is a z-statistic with four degrees of freedom. China and Hong Kong were on silver for the entire sample period,
Netherlands Indies switched from silver to gold in March 1877, Japan switched on March 1897, Philippine Islands was formally pegged to the gold dollar in March 1903,
British Malaya switched from silver to gold in August 1904, French Indochina switched in May 1930.
Portfolio Series
Region
Agricultural
Groups
French
Indochina
Netherlands
Indies
British Malaya
Banking and
Insurance
French
Indochina
Hong Kong
Commercial
Industrial
Netherlands
Indies
Philippine
Islands
British Malaya
French
Indochina
Japan
Shanghai
Food
British Malaya
British Malaya
changes from
silver to gold,
August 1904
U.K. and the
Netherlands
change from paper
to gold, April 1925
French Indochina
changes from
silver to gold,
May 1930
U.K. changes from
gold to paper,
September 1931
Japan changes
from gold to
paper, December
1931
0.185767
2.380822
0.078795
1.192776
0.018077
0.577266
-0.02443
-0.10542
0.097477
0.635051
0.13393
0.644068
-0.05834
-0.26239
-0.02496
-0.25185
-0.26238
-1.60892
-0.07692
-0.64665
0.028639
0.360561
-0.36421
-2.58513
0.137646
1.152811
0.119555
0.967183
0.027897
0.166869
-0.0322
-0.36574
-0.13934
-0.67049
-0.09844
-1.65413
-0.38382
-2.30835
0.070491
0.926354
-0.00768
-0.20253
-0.00564
-0.08652
-0.0593
-0.46619
-0.25458
-2.5186
-0.31856
-1.61187
-0.07568
-1.19373
0.232172
1.027431
0.021991
0.19754
-0.3384
-1.52892
-0.22688
-2.03522
-0.05965
-1.21664
0.036507
0.548812
0.037105
0.313232
0.314427
2.120236
-0.04647
-0.20871
0.157625
2.618719
0.587995
2.467879
-0.18897
-1.56346
-0.09732
-0.45182
0.244848
1.886407
-0.14291
-2.34407
0.300276
3.73928
-0.34597
-2.95998
0.529777
3.449209
0.49026
2.092981
0.090958
1.598808
60
Hong Kong
Japan
Mining
Philippine
Islands
British Malaya
French
Indochina
0.441338
3.275148
0.143782
0.523385
-
0.039203
0.249386
0.038071
0.207271
-0.04195
-0.28085
0.024558
0.259473
-0.09879
-0.64472
0.072961
0.605993
-0.26041
-1.2851
0.020236
0.356115
-0.25398
-2.48941
-0.50496
-3.84559
-0.05151
-0.33627
0.487
2.259997
0.188337
4.117613
-0.05035
-0.34988
-0.30462
-1.88778
0.2513
1.485822
0.578825
2.614158
0.188019
5.710604
-0.10711
-0.77956
0.114453
0.709404
Portfolio Series
Region
British Malaya
changes from
silver to gold,
August 1904
U.K. and the
Netherlands
change from paper
to gold, April 1925
French Indochina
changes from
silver to gold,
May 1930
U.K. changes from
gold to paper,
September 1931
Japan changes
from gold to
paper, December
1931
Mining
Japan
Ports
Philippine
Islands
British Malaya
-0.06306
-0.33668
0.203869
2.805538
0.117585
0.541571
0.127876
1.877307
0.182221
1.368589
0.052557
0.25989
0.062257
2.344027
0.394079
4.369745
-0.20814
-0.78101
-0.00358
-0.03566
-0.21564
-1.55815
0.190074
1.292537
-0.22171
-1.28149
0.057983
0.247467
-0.00087
-0.00744
0.031775
0.452654
0.313935
1.839608
-0.0795
-0.4632
-0.12435
-1.95205
0.164168
1.329241
0.114686
0.805172
0.213609
2.38265
0.325027
2.814614
0.080639
0.314777
-0.18443
-0.97947
0.020909
0.322814
-0.13196
-2.59404
-0.14595
-0.57866
0.214276
1.433953
0.019922
0.273027
-0.11452
-0.77175
-0.27355
-1.61105
-0.11675
-0.85272
-0.28849
-2.4187
-0.13066
-0.67796
-0.41693
-1.91792
0.055872
1.064877
0.092612
0.891328
0.009975
0.050334
0.390039
2.811129
0.004284
0.060559
0.574542
3.554997
0.265135
1.43479
0.385556
2.654138
-0.04827
-0.35492
0.327718
1.688505
-0.04778
-0.21008
0.078672
1.457537
-0.17265
-1.50453
0.37492
1.955571
Hong Kong
Shanghai
Real Estate
British Malaya
Hong Kong
Shanghai
Rubber
Plantations
British Malaya
Transport
French
Indochina
British Malaya
Utilities
Netherlands
Indies
Hong Kong
61
Shanghai
0.045801
0.639617
-
0.080889
1.125493
-0.08086
-0.97855
62
0.333616
3.195142