PRIVATE DEBT: VOLATILITY, CREDIT RISK, AND RETURNS
Douglas Cumming, Grant Fleming and Zhangxin (Frank) Liu
24 July, 2016
Douglas Cumming is Professor and Ontario Research Chair, York University - Schulich School of Business, 4700 Keele
Street, Toronto, Ontario M3J 1P3, Canada ( http://ssrn.com/author=75390; [email protected])
Grant Fleming is Partner, Continuity Capital Partners, Level 8, 12 Moore Street, Canberra, ACT 2601, Australia
(http://ssrn.com/author=1454188; [email protected])
Frank Liu is Assistant Professor, Accounting and Finance, University of Western Australia Business School, 35 Stirling
Highway, Crawley, WA 6009, Australia (http://ssrn.com/author=1948476; [email protected])
Acknowledgements:
We owe thanks to the seminar participants at the Moody’s Corporation / SAIF Credit Conference, Shanghai, Sussex
University School of Business, York University Schulich School of Business, and the Asian FMA Annual Conference.
1
PRIVATE DEBT: VOLATILITY, CREDIT RISK, AND RETURNS
ABSTRACT
We examine the performance of investments made by private credit fund managers into over 443
private companies in 13 Asian countries from 2001 to 2015. We show that the returns to private debt
investments are relatively uniform across size, country and industry despite diversity in legal and
economic system, size and age of credit markets. We compare the returns to two investments
strategies commonly adopted by credit fund managers – buy-and-hold and secondary trading
strategies. We find that strategies which involve buying/selling private debt on the secondary market
deliver higher returns than a strategy of buying-and-holding a primary issuance. Finally, we conduct
time series analysis of the variation in the performance of private debt investments. We build a private
credit return index from the underlying loan data and calculate excess returns to private debt
investments. Excess returns are positive and stationary over time. Excess returns are positively related
to volatility (as measured by ΔVIX) and to credit risk (ΔTED spread) but are not influenced by market
liquidity.
JEL Codes: C53, D82, G23, G24
Keywords: Private debt; performance; trading strategies; excess returns; volatility; credit risk;
liquidity
2
1.
Introduction
Debt is the predominant source of financing for private companies around the world (Cosh et
al. 2009; Robb and Robinson 2014; Tykvová 2016). A number of studies have examined the
borrower’s decision on the source of debt (public, bank or non-bank) (Denis and Mihov 2003; Lin,
Ma, Malatesta and Xuan 2013), the characteristics of private debt issuers (Krishnaswami, Spindt and
Subramaniam 1999; Cantillo and Wright 2000; Denis and Mihov 2003; Ackert, Huang and Ramirez
2007; Lambrecht and Pawlina, 2013), private debt loan contracts (Strahan 1999; Rajan and Winton
1995; Carey, Post and Sharpe 1998; Bradley and Roberts 2004; Ackert, Huang and Ramirez 2007)
and the risk and return of private loans (Carey 1998; Cumming and Fleming 2013). Most of this
research focuses on the United States and few studies examine the performance of private debt
investments to the lender/investor.
This paper extends the empirical literature on loans to private companies in several ways.
First, we examine the performance of mature private loan investments as measured by the internal rate
of return and return on investment (return multiple) to non-bank lenders. Our hand-collected dataset
comprises private debt investments made by specialist credit investment funds in over 443 private
companies in 13 Asian countries from 2001 to 2015. Eighty-five percent of the loans in the dataset are
located in companies in Mainland China, India, Australia and Indonesia providing diversity by legal
and economic system, size and age of credit markets. The median sized investment was US$16.6
million (average US$24.4 million) delivering an investor a median internal rate of return of 22%
(average 32%) with a return multiple of 1.27 (average 1.33). We find that there is some evidence of
the return to private debt investments varying by size, but no statistically significant differences in
returns by country or industry.
Second, we compare the returns to private loans for two investment strategies commonly
adopted by specialist credit investment funds – buy-and-hold and secondary trading strategies –
3
according to whether the loan is senior secured or subordinated in the capital structure of the
borrower. Private debt investors have flexibility as to when they invest (at issuance or acquiring the
loan post-issuance) and into which level of seniority. Both strategies require investors to source
private loans from borrowers and analyse credit quality under conditions of asymmetric information.
Furthermore, secondary markets for private loans are typically over-the-counter markets, imposing
search, due diligence and contracting costs on buyers and sellers. Our multivariate analysis shows
there are statistical differences between buy-and-hold and secondary trading strategies in terms of
rates of return and return multiple. Dynamic trading strategies which involve buying private debt on
the secondary market deliver higher returns than primary issuance buy-and-hold strategies.
Third, we conduct time series analysis of the variation in the performance of private loan
investments in Asia. We build a private credit return index from the underlying loan data using
discretisation techniques and lattice models pioneered by Moody’s KMV in estimation of private
company credit risk. We calculate excess returns to private debt investment as the difference between
the private credit return series and a comprehensive public markets return series (J.P. Morgan Asia
Credit Index). We also decompose our credit index into two separate series for China (the region’s
largest credit market) and the Rest of Asia. We find that excess returns are positive over time and that
the excess return series is stationary as measured by standard unit root tests. We document a positive
relation between the excess return to private debt investments (Asia, China and Rest of Asia) and
volatility (as measured by ΔVIX), and credit risk (ΔTED spread), but find that returns are not
influenced by market liquidity. Finally, we show that our findings are robust to various model
specifications.
The remainder of the paper is organised as follows. In Section 2 we briefly review existing
literature on why firms issue private debt, the common features of debt investments and the
performance of debt investments. We also outline the key questions examined in this paper. In Section
3 we describe the dataset and provide summary statistics. In Section 4 we present univariate and
multivariate results on the performance of private debt investments by investment strategy. We then
4
turn out attention to time series analysis of performance. Section 5 describes out methodology in
constructing a private credit index and multivariate analysis of variations in the return and excess
return indices. Section 6 presents our conclusions.
2.
Existing Literature and Research Questions
In this section we briefly review existing literature on why firms raise private debt and the
common features of private debt contracts. We then discuss the performance of private debt
investments and literature on time series variation in returns. Finally, we outline the research
questions investigated in the paper.
2.1. Private Loans – Firm Characteristics and Contract Terms
Private companies have several options to raise debt financing from capital markets. Public
debt markets provide a mechanism to issue bonds to public bondholders, companies could secure a
bank loan or place debt privately to non-bank financial intermediaries (e.g. investment banks, hedge
funds, pension funds). Denis and Mihov (2003) find that companies with higher credit quality borrow
from public markets before banks and non-bank lenders. Indeed, there is a positive association
between the use of public debt and company characteristics such as size, leverage, age and the amount
of debt issued (see Krishnaswami, Spindt and Subramaniam 1999; Cantillo and Wright 2000; Denis
and Mihov 2003; Ackert, Huang and Ramirez 2007). The degree of asymmetric information between
a company and its lenders also influences the choice of type of debt. Firms with higher levels of
idiosyncratic information are more likely to issue debt privately while those with lower information
asymmetry issue public debt (Diamond 1991; Denis and Mihov 2003). Dennis and Milleneaux (2000)
find a positive relation between information opaqueness and private issuances. It may be the case
however that higher quality firms choose private over public debt to avoid disclosing information.
Yosha (1995) shows that small and mid-sized private companies might be willing to incur higher
5
financing costs to keep sensitive information away from competitors. These firms opt for bilateral
financing (typically privately) over multilateral financing.
The issuing of private debt by companies to banks and non-bank lenders typically involves
greater levels of monitoring than in the case of “arms-length” investors (Diamond 1984). Banks and
non-bank lenders are more likely to have access to information on the borrower, detect managerial
expropriation and early warning signs of changes in ability to service and repay the loan. Lin, Ma,
Malatesta and Xuan (2013) show that firms with large shareholders (and excess control rights) seek to
avoid such monitoring by preferring public debt financing over bank debt. Banks and non-bank
lenders are able to structure a loan to a company which incorporates price and non-price terms
(collateral, covenants, information rights, control rights) in order to mitigate higher credit risk
(Strahan 1999; Ackert, Huang and Ramirez 2007). If the borrower is a long-term bank customer or
repeat issuer on the private market, lenders are able to capture idiosyncratic firm information not
available in financial statements – management expertise, their ability to respond to changes in market
conditions or competitor threats, the nature of customer and supplier relationships (Dennis and
Mullineaux 2000).
Private debt is typically more costly for a company to issue and private debt contracts will
usually include more restrictive terms and conditions. Bradley and Roberts (2004) argue that the
agency theory of covenants provides a rationale as to why debt contracts contain covenants (see also
Rajan and Winton 1995). Covenants allow lenders to mitigate potential conflicts between themselves
and managers who act on behalf of shareholders. They find that covenants are more likely in smaller,
higher growth firms with less leverage and fewer tangible assets. Notably, Bradley and Roberts show
empirically that the inclusion of covenants and the pricing of a loan are determined simultaneously.
Ackert, Huang and Ramirez (2007) show that private loan terms are driven by the degree of
asymmetric information between borrower and lender, contracting costs and credit risk. Loans to
small private firms also tend to have shorter maturities than public firms as lenders limit their
exposure to the most risky firms (Berger and Udell 1999; Hubbard, Kuttner and Palia 2002). Finally,
6
loans to private firms tend to have higher levels of collateral (Berger and Udell 1999) although
whether this is associated with higher credit risk (Rajan and Winton 1995) or lower credit risk (higher
quality firms signalling to lenders by willing to post collateral)(Bester 1985; Besanko and Thakor
1987) is an open empirical question.
2.2. The Performance of Private Loans
The performance of private loans and returns to private lenders has received less attention in
the finance literature. Lenders to private firms receive financial return from the loan from the cash
coupon (typically a fixed rate, paid regularly), payment-in-kind (interest accrued and paid at
maturity), upfront fees associated with providing the loan and early repayment penalties (penalties
stipulated in loan agreements should the firm repay the loan prior to maturity). Banks and non-bank
lenders will take all features of the loan into account when evaluating a new loan and when
calculating a fair value of the loan during its holding period (Tschirhart, O’Brien, Moise and Yang
2007). Carey (1998) has shown that a portfolio of private loans has lower default and higher recovery
rates than a risk-equivalent portfolio of public bonds, and that the difference increases with credit risk.
That is, there is good evidence that the highly structured nature of private loans (collateral, covenants
etc), close monitoring and scrutiny by private lenders has value which lowers the ex-ante riskiness of
the borrower.
Another strand of finance literature examines the relation between bond yields and legal
institutions (in particular, creditor rights). Qian and Strahan (2007) and Bae and Goyal (2009) show
that bank loan yields are negatively related to the quality of a country’s legal institutions. This body of
work draws on the “law matters” finance literature which has established a positive relation between
the strength of a country’s legal system, credit rights, structure of covenants, and the size of corporate
bond markets (La Porta, Lopez-de-Silanes, Shleifer and Vishny, 1998; Djankov, McLiesh and
Schleifer, 2007; Djankov, Hart, McLiesh and Schleifer, 2008; Qi, Roth and Wald 2010). Cumming
and Fleming (2013) extend the law and finance literature by examining the returns to private debt
7
investments in 25 countries. They show that there is no relation between returns to private debt
investments and a country’s legal system, suggesting that borrowers and lenders negotiate terms and
conditions in loan agreements which mitigate specific country/jurisdictional risk.
Macroeconomic and credit market factors have been identified as important determinants of
the variation in credit spreads and public bond performance over time. Greenwood and Hanson (2013)
show that the quantity of credit (market liquidity) is negatively related to credit quality and lower
excess returns to public bondholders. The average quality of issuances on public bond markets
deteriorates during the credit boom, resulting in significant underperformance of public corporate
bonds against Treasury bonds of similar maturity. Similarly, Collin-Dufresne, Goldstein and Spencer
Martin (2001) find that monthly credit spread changes are largely driven by local demand/supply
shocks rather than by idiosyncratic default risk. Tang and Yan (2010) document a positive association
between credit spreads and volatility in the growth (or change) in gross domestic product (GDP).
They also show that credit spreads widen when investors are more risk averse (as measured by
investor sentiment). Cumming and Fleming (2013) provide, to our knowledge, the only analysis of
private debt returns and macroeconomic and credit market factors. Using panel data over ten years
they find no cross-sectional relationship between in private debt returns and GDP per capita of the
borrower’s location, or between private debt returns and credit market risk as measured by the TED
spread (levels or changes).
2.3. Research Questions
We draw upon the existing literature to formulate three sets of research questions.
(a). Size, Geography and Industry
Credit quality and firm risk tend to be negatively associated with the size of the firm. The
literature on private loans indicates that size is a proxy for credit quality, information opaqueness and
8
associated information asymmetries (Krishnaswami, Spindt and Subramaniam 1999; Cantillo and
Wright 2000; Denis and Mihov 2003; Ackert, Huang and Ramirez 2007). We postulate a negative
relation between the size of a private debt investment (a proxy for firm size) and returns (as measured
by internal rate of return and return multiple). In terms of country of private debt issuer, we have no
prior as to whether returns vary by the location of the issuer. Cumming and Fleming find that there is
no relationship between private debt returns and the legal jurisdiction of the company issuing the debt.
By contrast, Qian and Strahan (2007) and Bae and Goyal (2009) show that legal system can influence
credit spreads. Finally, we expect that investment returns vary by industry given differences in levels
of tangible assets, revenue and earnings volatility.
(b) Investment strategies: Buy-and-hold versus dynamic trading strategies
Our data allows an examination of the returns to buy-and-hold versus “dynamic trading
strategies”. Does a trading strategy of buying and selling loans in the secondary market add or detract
value from a primary loan only (buy-and-hold) strategy? Our a priori view is that credit fund
managers are rational and that compensation structures encourage value enhancing behaviour. On this
basis, we expect the investment returns to trading on the secondary market to be at least as high as
those available from primary placements (buy-and-hold). Second, we measure the extent to which
private debt investment returns vary by a particular ownership type – a leveraged buyout (LBO) debt
issuer. To our knowledge our study is the first to examine the private debt returns associated with
LBO and non-LBO backed firms. We have no prior view as to whether debt issued by LBO-backed
firms performs differently from non-LBO backed private debt. LBO backed firms have large
shareholders (typically an LBO firm will own in excess of 90% of the equity of a private company)
which are motivated to maximise equity value and use debt to disciple managers by limiting free
cashflow (Cao, 2011). Also, LBO firms may be incentivised to ensure that private firm issuers do not
renege on debt contracts in order to build reputation on debt markets. In cases when LBO firms
default on debt, Cressy and Farag (2012) find that LBO backed firms have higher recover rates than
non-LBO backed firms during periods of high credit availability. On the other hand, LBO firms also
9
tend to use debt to bolster returns during periods of high credit availability. Greenwood and Hanson
(2013) show that the quantity of credit (market liquidity) is negatively related to credit quality and
lower excess returns to public bondholders. Thus, it is possible that LBO backed firms have higher
leverage levels and higher default risk than non-LBO backed firms during credit booms.
(c) Excess returns
Our third set of research questions relate to the time series features of private debt returns. We
develop a private credit return index from underlying private debt information to chart whether and
how private debt returns vary over time. First, we examine whether there is excess returns to private
debt investing over and above publicly traded debt. We draw inspiration from the alternative assets
literature which has found excess returns (alpha) in private equity (Nielsen, 2008; Dittmar, Li, and
Nain., 2012; Fidrmuc, Palandri, Roosenboom, and van Dijk, 2013; Fan, Fleming and Warren 2013;
Tykvová, 2016) and private real estate (Kaiser 2005; Alcock, Baum, Colley and Steiner 2013) and
postulate positive excess returns to private debt. We also construct indices for China (the region’s
largest credit market) and the Rest of Asia to examine whether excess returns exist in credit markets
with different institutional and finance market structures. Second, are excess returns stationary over
time? Finally, we examine the time series variation in our excess returns series. Following CollinDufresne, Goldstein and Spencer Martin (2001), Greenwood and Hanson (2013) and Tang and Yan
(2009) we expect excess returns to be positively related to credit risk (as measured by the TED
spread), positively related to volatility (VIX) and negatively related to market liquidity.
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3.
Data and Summary Statistics
The dataset comprises private debt investments made by fifteen specialist credit investment
funds in over 443 private companies in 13 Asian countries from 2001 to 2015. The data were handcollected from confidential, private placement memorandum issued by Asia-based credit fund
managers which raised capital from “sophisticated” (or wholesale) institutional investors. The data
represent the total investment track record for each credit fund manager, typically audited by a
reputable accounting firm. The median fund manager had been investing in Asian credit markets for
13 years (average 11.9 years), had invested US$1.7 billion (average US$2.2 billion) and had 10
investment professionals (average 32 investment professionals). The institutional investor which
provided the dataset only invested in a subset of the credit fund managers (2 out of 15 managers),
reducing any selection bias in the collection of private placement memorandum.
Each private placement memorandum provides prospective investors with the historical track
of the credit fund manager at the individual private debt investment level. The data typically includes
the following information:
•
Issuance and realisation data of the private debt investment;
•
Location (country) of company issuing the private debt;
•
A company description and industry in which the issuing company operates;
•
The type of debt instrument – senior secured loan or subordinated loan;
•
The cash coupon, payment periodicity and overall yield on the debt instrument;
•
Private debt investment metrics for the credit fund manager – the amount of capital invested
in the debt instrument; the realised component of the investment and total return; and
•
Private debt investment returns: an internal rate of return for the investment (based in audited
cashflows), and the return on investment (or return multiple)(defined as the total amount of
capital returned – principal, coupon and additional payments (e.g. upfront arrangement fees;
early prepayment fees) divided by the initial investment outlay).
11
Our process for data collection and verification involved double-checking the entry of all data, crosschecking investment returns with each credit manager and against audited financial statements (where
possible), and re-calculating internal rates of return.
The summary statistics for dataset are provided in Table 1 below.
TABLE 1
ABOUT HERE
The median sized investment was US$16.6 million (average US$24.4 million) delivering an investor a
median internal rate of return of 22% (average 32%) with a return multiple of 1.27 (average 1.33).
Private debt investment returns range from an internal rate of return of 1,310% to -100%, and a return
multiple of 3.97 times investment to 0.00 times investment (that is, a total loss of the loan). The
relatively higher internal rates of return at the right-hand side of the distribution are likely explained
by the fact that the dataset includes investments which are secondary trades of private debt. Secondary
trading strategies involve a credit fund managers acquiring private debt investments over-the-counter
at discount to par at times when liquidity is at a premium or a specific holder of the debt needs to sell
the debt instrument. Short holding periods and low acquisition prices can result in high internal rates
of return as compared with buy-and-hold investment strategies (see Duffie, Gârleanu and Pedersen
2007 for a discussion of how such “price jumps” can occur in over-the-counter markets). Similar
cross-sectional variation in returns has been observed in hedge fund studies on dynamic trading
strategies across various styles (see, for example, Fung and Hsieh 1997; Sadka 2010). Given such a
large range, we winsorize the dataset to account for outliers/influential points later in our analysis.
Table 2 reports the country (location) of each private debt issuer and the industry in which the
issuer operates (as defined by the Global Industry Classification Code; GICS). Eighty-five percent of
the loans in the dataset are issued by companies headquartered in four countries – Mainland China
12
(36.0%), India (23.4%), Australia (14.4%), and Indonesia (12.4%). The data provides diversity by
legal and economic system, size and age of credit markets.
TABLE 2
ABOUT HERE
Eighty-four percent of the loans in the dataset are located in three industries – financials
(which includes real estate)(46.6%), industrials (13.7%) and consumer discretionary (13.5%).
Our first set of research questions relate to private debt investment returns and the size,
geography and industry of the private debt issuer. We hypothesized a negative relation between size
of investment (a proxy for issuer size) and investment returns, due to smaller firms being lower credit
quality and having higher degrees of information opaqueness. We found no differences in returns by
size as they relate to the internal rate of return on the investment, but a weak significant negative
association between size and return multiple. 1 We conclude that there is unsufficient statistical
evidence to suggest that private debt investments vary by size. The law and finance literature shows
that bank loan yields are negatively related to the quality of a country’s legal institutions (see Qian
and Strahan 2007; Bae and Goyal 2009). By contrast, Cumming and Fleming (2013) find no relation
between private loans and location of private debt issuers. We might also expect to find differences in
private debt returns across industry, as levels of tangible assets, revenue and earnings volatility varies
by industry. We investigated the variation in returns by geography and industry at the univariate level
through tests for equality of country/industry means and medians using analysis of variable
(ANOVA)(means) and Chi-Squared/Kruskal-Wallis (medians) tests. We found no statistically
significant differences in means or medians across the dataset for either country or industry.
1
We tested for differences in investment returns by size using ordinary least squares regressions on size (investment cost)
and log of size. Both full sample and winsorized samples produced similar results (although with extremely low model
adjusted R2 (approximately 1-2%) and F-statistics (significant at the 10% level only).
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4.
Trading Strategies – Buy-and-Hold versus Secondary Trading
Private credit managers have several ways in which they can invest in private company debt.
The credit manager can participate in the primary debt issuance (solely as a bilateral loan or as part of
a private syndicate) and hold the investment to maturity (or early repayment). In this scenario the
private credit manager is party to negotiation of price and non-price terms of the loan agreement
(collateral, covenants, information rights, control rights) in order to mitigate credit risk (Strahan 1999;
Ackert, Huang and Ramirez 2007). An alternative investment strategy is for the private credit
manager to acquire the private debt instrument on the secondary market. Such a “dynamic trading
strategy” involves the credit fund manager acquiring the private loan over-the-counter, usually
brokered by an investment bank (see Duffie, Gârleanu and Pedersen 2007; Duffie 2010).
We have stratified the data on the basis of whether investment returns were generated by the
credit fund manager by a primary issuance (buy-and-hold) or secondary (trading) strategy, and
whether the debt instrument was senior secured or subordinated.
TABLE 3
ABOUT HERE
Each quadrant in Table 3 shows the average internal rate of return and average return multiple
for the various combinations. Primary issuance investments in senior secured loans generated an
average return of 31.0% and an average return multiple of 1.29 times, as compared with average
secondary returns of 46.3% and 1.75 times. We also note that returns to subordinated loans appear to
be lower than those for senior loans despite being lower in the capital structure of the firm (and by
definition, having higher credit risk).
We next estimate OLS regressions on a winsorized dataset to examine whether there are
statistical differences to returns in investment strategies. As noted above, we observe large variation
14
in the returns to private debt investments, resulting in several influential points which bias estimates
in ordinary least squared regressions. We adopt a 95% winsorized approach for our regressions,
excluding the upper and lower 2.5% of data points. Table 4 reports results for various estimates of the
generalised regression model:
Returns = f(Secondary, Subordinated, LBO, Age, Historical AuM, Cost of Contract Enforcement)
where the base return is a buy-and-hold investment at primary issuance and indicator variables equal
one for the type of investment, zero otherwise. We report results with and without fund fixed effects.
TABLE 4
ABOUT HERE
The regression results indicate that secondary trading generates additional returns over above returns
to a buy-and-hold strategy. The secondary coefficient is positive and statistically significant at the 1%
level in all model estimations using internal rate of return and return multiples, where the economic
significant is approximately 10% higher for IRR and 20% higher for ROI on the more conservative
estimates. We also find that return multiples for subordinated debt are higher than senior secured debt,
but not for internal rates of return, and the statistical significance of the results depends on the
econometric specification. We find that there is no difference between LBO and non-LBO private
debt issuances, no significant effect of age, historical assets under management and cost of contract
enforcement.
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5.
The Returns to Private Credit Investing Over Time
There is little empirical evidence on the returns to private debt investing over time. In this
section we describe the methodology used to construct an Asia private credit return index (APCR),
examine the characteristics of the index and provide multivariate analysis of variations in the return
and excess return indices as they relate to changes in macroeconomic and credit market factors.
5.1. Private Credit Return Index – Methodology
Private loans can be valued several different ways, each imposing a valuation model which
attempts to estimate credit risk at a point in time and a net present value of expected cashflows under
no default and default scenarios (Turnbull 2003; Dwyer, Kocagil and Stein 2004; Tschirhart, O’Brien,
Moise and Yang 2007; Agrawal, Korablez and Dwyer 2008). The challenge to building a return index
with our dataset is the lack of loan revaluations information between the start and maturity dates.
That is, only the amount of the investment on the start date and the final realized and unrealized return
on investment are recorded although we do have information on coupon rates, payment periodicity
and overall yield for a subset of the loans. This means that the performance trajectory of each
investment (the loan’s valuation and return since inception at discrete points of time) is unknown. In
order to conduct the time-series analysis on the relationship among Asia private credit returns and
market volatility, we employ discretisation techniques and apply a lattice model to construct the credit
return index.
First, we discretise the time interval between the maturity or valuation date and the start date
into T days. At each time period t, there are a finite number of credit states, N, where the investment
can be. In a classic lattice model analysis, when modelling a T-day loan investment as having N
credit states, there are NT possible paths for this investment. These credit states include default, nondefault and prepaid. It is a general practice to consider prepayment options when evaluating a loan
(for example, see Agrawal, Korablez and Dwyer 2008). However, we do not directly observe
16
sufficient information to infer whether a loan was prepaid in our data set. 2 This reduces the possible
paths in the lattice analysis down to 2T.
For each credit state in each time period, being default or non-default, there is a risk-neutral
probability of moving from this state to the next. This probability could be approximated by using the
expected default frequency (EDF), which is a firm specific and forward-looking measure of actual
default probability (Kealhofer 2003). A common practice is to use the Moody’s KMV model to
estimate EDF, which requires inputs of the value of equity and other items from the borrower’s
balance sheet (Dwyer, Kocagil and Stein 2004; Agrawal, Korablez and Dwyer 2008). Without access
to such variables, we are not able to estimate the firm specific EDF and instead we use the cumulative
default rates among speculative-grade ratings in Asia-Pacific region (as provided by S&P 2013) to
approximate the individual default probability.
The second step is to determine the value of the investment for each credit state. Let us use Si,t
𝑁
to represent the value of an investment i at time t in [0, Ti], where Ti is the maturity day; and 𝑆𝑖,𝑡
and
𝐷
to represent the value at time t if it is non-default and default from previous time t-1, respectively.
𝑆𝑖,𝑡
In this setting, only Si,0 and Si,T are known. We start at the maturity date or the last report date. In the
credit state where the investment has not gone into default from the previous period Ti-1, the value of
the investment at Ti is:
𝑁
𝑆𝑖,𝑇
= 𝑆𝑖,𝑇
(1)
In the alternate credit state where the investment has been defaulted from the previous period, the
value of the investment at Ti is:
𝐷
𝑆𝑖,𝑇
= (1 − 𝐿𝐺𝐷)𝑆𝑖,0
(2)
It is important to note that here we assume a fixed proportion of the original investment can
be recovered from the original investment in the event of default. An alternative assumption could be
2
One may argue that a loan could be assumed to be prepaid if the maturity of the loan was shorter than a certain length of
time; however, there is no empirical finding to support selection of an arbitrary time period(s).
17
to assume a recovery of the investment value from the previous period i.e. Si,T-1 in this case. However,
𝑁
we do not directly observe the true value Si,T-1. If we used 𝑆𝑖,𝑇−1
to proxy for the true value, it would
𝑁
is determined by itself. We then step back one day to Ti – 1. For
have induced a loop such that 𝑆𝑖,𝑇−1
each credit state, we compute the expected value of the next period’s cash flows under the risk-neutral
measure. In the credit state that the investment has not gone default from the previous period Ti – 2,
the value of the investment at Ti-1 is:
𝑁
𝐷
𝑁
= 𝑝𝑖,𝑡 𝑆𝑖,𝑇
+ �1 − 𝑝𝑖,𝑡 �𝑆𝑖,𝑇
= 𝑝𝑖,𝑡 𝑆𝑖,0 (1 − 𝐿𝐺𝐷) + �1 − 𝑝𝑖,𝑡 �𝑆𝑖,𝑇
𝑆𝑖,𝑇−1
(3)
where pi,t is the probability of default for investment i, LGD is the loss given default rate and Si,0 is the
value of investment at the start, which is known in this setting. Given the lack of firm specific
information we set LGD as 20%. Our approach is consistent with Kealhofer (2003) and Gupton and
Stein (2005) who argue that LGD values should be set with reference to historical averages to avoid
endogeneity issues in estimating the probability of default. As Kealhofer (2003, 84), states, a
“…problem arises if LGD has cross-sectional variation that correlates with default probability; this
characteristic makes it difficult to separately identify the effect of default probability... this choice of
specification makes the default probability to do all the work in fitting the bond prices.” In the
alternate credit state where the investment has been defaulted from the previous period, the value of
the investment at Ti-1 is:
𝐷
𝑆𝑖,𝑇−1
= (1 − 𝐿𝐺𝐷)𝑆𝑖,0
(4)
That is, the investment would terminate at Ti-1 and no further movement in valuation will be
observed.
We continue to work backward and track the value of the investment at each credit state
until time 1, assuming that the loan investment would stop following a default. It follows some basic
algebra to show that at any time t in [1, …, T-1],
18
𝑁
𝑇−𝑡−𝑗
𝑆𝑖,𝑡
= (1 − 𝑝𝑖,𝑡 )𝑇−𝑡 𝑆𝑖,𝑇 + 𝑝𝑖,𝑡 𝑆𝑖,0 (1 − 𝐿𝐺𝐷) ∑𝑇−𝑡
𝑗=1(1 − 𝑝𝑖,𝑡 )
𝐷
𝑆𝑖,𝑡
= (1 − 𝐿𝐺𝐷)𝑆𝑖,0
(5)
(6)
In the third step, we incorporate coupon payments during the life of an investment. Most
private credit investments provide an investor with the combination of cash and non-cash interest
(payment-in-kind), with the proportion negotiated as part of the terms of the loan agreement between
the borrower and the private credit lender at the start of the loan period. Out data includes the coupon
rate on the private debt investment for approximately 20% of investments. The median coupon rate is
13.8%, payable on a quarterly basis. We assume this coupon rate for all transactions with missing
data. In the Appendix, we investigate the robustness of this assumption by examining the APCR index
against indices constructed using different assumptions of coupon frequencies and coupon rates (six
alternative model specifications).
Let us use ci to represent the coupon payment rate for investment i and Ii,t as an indicator function that
equals to 1 if t is a coupon paying day. The value of the investment at any time t in [1, …, T-1] is a
sum of the investment value in the non-default credit state and an accrued amount of coupons
received up until t,
𝑁
𝑡
𝑆�
𝚤,𝑡 = 𝑆𝑖,𝑡 + ∑𝑗=1 𝑐𝑖 𝑆𝑖,0 𝐼𝑖,𝑗
(7)
After the third step, we are able to recover one particular trajectory of investment i:
�
�
�𝑆𝑖,0 , 𝑆�
𝚤,1 , 𝑆𝚤,2 … , 𝑆𝚤,𝑇−1 , 𝑆𝑖,𝑇 �
(8)
The last step is to construct the Asia private credit return index, which is capitalizationweighted and requires a minimum of two active investments. More specifically, the individual’s
weight, wi,t, is determined by the size of the total investment at its inception Si,0. If there are N
investments underlying the index on time t, the weight for investment i is,
19
𝑆
𝑤𝑖,𝑡 = ∑𝑁 𝑖,0
(9)
𝑗=1 𝑆𝑗,0
For any t in the sample period from 2006 to 2015, the return index can be calculated as
𝑆�
𝚤,𝑡
𝐴𝑃𝐶𝑅𝑡 = ∑𝑁
𝑖=1 𝑤𝑖,𝑡 ( � − 1)
𝑆𝚤,𝑡−1
(10)
In this model, the loan investment values are quite stable such that a daily change can be close to zero.
Such observations are not uncommon in private debt valuations. Agrawal, Korablez and Dwyer
(2008) find that monthly changes in loans quotes are equal to zero around 47% of the time in their
sample of LPC loans quotes from 2002 to 2006.
5.2. Private Credit Returns, Public Credit Returns and Stationary
Our private credit return index provides a monthly return series for Asian private credit
investments between January 2006 and December 2015. We decompose the APCR index into two
separate indices: China Private Credit Return index, which is based on private debt investments made
in China; and Rest of Asia Private Credit Return index, which is based on private debt investments
that are not made in China.
In order to investigate whether private debt returns differ to public debt returns we calculate
an excess return series as the difference between our APCR index and the J.P. Morgan Asia Credit
Index (JACI). The JACI is a broad public credit markets index comprising U.S. dollar denominated
bonds issued by sovereign, quasi-sovereign and corporates in 15 Asian countries, excluding Japan and
Australia/New Zealand. The index is market capitalisation-weighted (market capitalisation of US$544
billion at 31 October 2014) and is 76% investment grade debt and 24% non-investment grade debt.
The monthly JACI is subtracted from the monthly APCR index to obtain the excess return index. We
20
calculate the excess return indices for China and the Rest of Asia in a similar way. The summary
statistics for the excess return private credit indices are shown in Table 5.
TABLE 5
ABOUT HERE
Table 5 shows that monthly average excess returns for all Asia investments is a mean of
1.08% per month, with a median between 1.01% per month. The China monthly average excess return
is a mean of 0.82% per month (median 0.45% per month) and the Rest of Asia monthly average
excess return is a mean of 1.18% per month (median 1.25% per month). However, we can also note
periods of private credit underperformance, with minimum monthly returns ranging between -4.1%
and -4.8% per month. Skewness and kurtosis statistics indicate that the distribution of excess monthly
returns contains a higher proportion of positive excess returns. We test for the stability of the return
series using an Augmented Dickey-Fuller test. All test statistics indicate that we cannot reject the null
hypothesis that the series is stationary. Figure 1 shows the time series variation in the excess return
series and Figure 2 shows excess returns for the China and rest of Asia indices.
FIGURE 1
ABOUT HERE
FIGURE 2
ABOUT HERE
In summary, our excess return series shows that Asian private credit returns deliver outperformance over public market credit returns, that the excess returns on average ranges between
0.82% per month (China) and 1.18% per month (Rest of Asia), and that positive excess returns are
stationary over time. These findings are consistent with excess returns documented for private equity
(Kaplan and Schoar 2005; Fan, Fleming and Warren 2013) and private real estate (Kaiser 2005;
21
Alcock, Baum, Colley and Steiner 2013). We turn next to examine in more detail the variations in the
excess return series.
5.3. Time Series Variation in Asian Private Credit Returns
Our third set of research questions relate to the time series features of private debt returns. We
complete two set of analyses using the Asia private credit index. First, we examine variation in the
excess private credit index as it relates to global credit risk, global volatility and funds flows into/out
of the region (liquidity). Second, we focuses specifically on China by calculating a China private
credit index and comparing its time series properties to the rest of Asia.
The generalised form of our regression involves regressing the excess private credit index
against three variables measuring credit risk, volatility and liquidity, as follows:
EXCESSt = a + b(ΔVIXt) + b(ΔTEDt-1) + d(Liquidityt) + e
(11)
Financial market volatility is measured as the change in the volatility index (ΔVIX) as calculated by
the Chicago Board Options Exchange. Global credit risk is defined as the ΔTED spread, the daily
percentage spread between 3-Month LIBOR rate (based on U.S. dollars) and the 3-Month Treasury
bill rate, as calculated by the Federal Reserve Bank of St. Louis. We use the immediate historical
change in the TED spread (ΔTEDt-1 - ΔTEDt-2) as lagged variables, in our view, better approximate the
information on systematic global credit risk important in investment decision-making. We adopt two
Asia-specific measures of market liquidity using the quarterly year-on-year percentage change in
domestic credit and cross-border credit, using data from the Bank of International Settlements. An
increase in the liquidity measure indicates that there is greater amount of credit available in the Asian
region as compared with the previous year, due to domestic and/or cross-border capital inflows.
Summary statistics for the ΔVIX, ΔTEDt-1 and liquidity measures are provided in Table 5.
22
We hypothesise that excess returns are positively related to credit risk and volatility, and
negatively related to market liquidity. Ceteris paribus, an increase in global credit risk indicates
higher levels of investor risk aversion which require higher excess returns as compensation. Similarly,
times of higher volatility in finance markets will be associated with higher excess returns (Tang and
Yan 2009; Greenwood and Hanson 2013). Finally, we hypothesise that increases in market liquidity
in the Asian region will result in excess supply of credit for private firms, and lower excess returns
(Collin-Dufresne, Goldstein and Spencer Martin 2001).
The correlation probabilities for the excess return series and explanatory variables used in our
regressions are shown in Table 6.
TABLE 6
ABOUT HERE
The correlation probabilities show that there are statistically significant correlations between several
of the explanatory variables and the excess private credit index. The ΔVIX and ΔTED spread are
significantly positively correlated with each of the excess return series (APCR, China and Rest of
Asia), in most cases at the 1% or 5% significance level. The liquidity index is not significantly
correlated with the excess return indices. We also note a positive significant correlation between the
ΔTED spread and ΔVIX (correlation of 0.195, significant at 5%).
The results of regression analysis are reported in Table 7.
TABLE 7
ABOUT HERE
The results of our modelling indicates that there is a significant positive association between the
excess return indices and ΔVIX, and a significant positive association between the excess return
23
indices and ΔTED. We find no association between liquidity and excess returns. These results are
consistent for the APCR excess return index, the China excess return index and the Rest of Asia
excess return index. We estimate the regressions using two measures of liquidity – change in domestic
credit and in cross-border credit. These variables are not significant. Alternative estimations (not
reported) combining liquidity measures are also not significant. Finally, we estimate the China models
using a China-specific financial market volatility index (ΔC-VIX) as calculated by O’Neill, Wang and
Liu (2016). Our results do not change.
6.
Concluding Remarks
Private debt is the predominant source of debt financing for companies around the world. A
number of studies have examined the borrower’s decision on the source of debt, the characteristics of
private debt issuers, private debt loan contracts and the risk and return of private loans. Most of this
research focuses on the United States and few studies examine the performance of private debt
investments to the lender/investor. Our paper provides the first analysis of the cross-sectional and
time series returns to private debt investments in Asian companies, using a sample of credit fund
manager investments across the region. Our data provides insights into private debt investments in
diverse economic systems and finance markets including Mainland China, India, Australia and South
East Asia. We show that the returns to private debt investments are relatively uniform across size,
country and industry despite country diversity. We find no evidence that “laws matter” for private
debt returns; rather if laws do matter we suggest that borrowers and lenders negotiate terms and
conditions in loan agreements which mitigate specific country/jurisdictional risk.
Private credit fund managers commonly execute two investment strategies in Asian debt
markets. The first involves investment at primary issuance in a private company’s debt and holding
that debt to maturity. The second involves a more active investment strategy where credit fund
managers buy and sell debt over-the-counter. We find that strategies which involve buying/selling
private debt on the secondary market deliver higher returns than a strategy of buying-and-holding a
24
primary issuance. The regression results indicate that secondary trading returns are positive and
statistically significant at the 1% level in all model estimations using internal rate of return and return
multiples. We find that there is no difference between LBO and non-LBO private debt issuances. Our
results suggests that credit fund manager trading skills are important in assessing excess returns, over
and above the skills involved in the evaluation of private debt opportunities at issuance no matter
whether debt is senior secured or subordinated or in LBO-backed or non-LBO backed private
companies. Further research is required on how private credit manager trade on the secondary market
through the credit cycle and on whether the success or regularity of secondary trading strategies varies
due to macroeconomic and credit market factors.
Our private credit return index is the first index to show excess investment returns to private
credit investments in Asia (and as far as we are aware, anywhere in the world). We have used
discretisation techniques and lattice models pioneered by Moody’s KMV to estimate private company
credit risk and backwards induce credit returns during the holding period of the investment. We find
that excess returns are on average 1.08% per month, and that positive excess returns are stationary
over time. We also find that excess returns to Chinese private debt (0.82% per month on average) are
lower than the Rest of Asia (1.18% per month on average). Excess returns are positively related to
volatility (as measured by ΔVIX), and to systemic global credit risk (ΔTED spread) but are not
influenced by market liquidity. Our findings are robust across Asian credit markets (Asia, China and
Rest of Asia) and to various model specifications.
Analyses of private credit markets involve studying private data, and our analyses are based
on proprietary access to data from private debt funds. Future research could examine other samples
from other countries, and explore the interaction of private debt investors with other types of investors
to better understand the dynamics of private debt investments around the world.
25
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Appendix
The construction of the Asia private credit return index (APCR) involves incorporating
coupon payments during the life of an investment into the index. This allows for a valuation index to
be constructed for each month which takes into account the fact the debt investments deliver regular
coupons (monthly, quarterly or annually) which may be paid as cash or accrued as part of the
principal.
Our APCR index uses an average coupon rate of 13.8% per annum paid quarterly (that is,
3.45% per quarter), derived from our underlying data. In order to assess whether our approach of
using the median coupon rate in all unobserved cases causes unnecessary bias we have compared the
benchmark APCR index to six models (Models a to f) with two assumed rates of returns (10% per
annum and 20% per annum). Table A1 summarises the model assumptions.
TABLE A1
ABOUT HERE
Each model is defined according to one of three coupon frequencies – quarterly, semi-annually and
annually – with six coupon rates. Half coupon means that 50% of the assumed return (a return of 20%
per annum) is paid as cash coupon; full coupon means that 100% of the return is paid as a cash
coupon. Letters a – f denote six different models with corresponding assumptions. For example,
Model a assumes that the investments in the private credit index pay coupons to investors every 90
days at a rate of 2.5% per quarter (or 10% per annum, half the total return of the investment).
Figure A1 shows the time series of the APCR excess return index and the excess return
indices for the six models.
FIGURE A1
29
ABOUT HERE
The correlation between excess return indices varies between 92% and 100%, and the correlation of
differences between excess return series varies between 69% and 98%. In addition, re-estimation of
the regression models reported in Table 7 using excess return series from models a to f (rather than
APCR excess return indices) delivers similar results. All robustness checks and estimations are
available upon request.
30
TABLE 1
Asia Private Debt Investment Returns
Summary Statistics
Table 1 Panel A shows the summary statistics for over 443 private debt investments made by fourteen specialist credit
investment funds in private companies in 13 Asian countries from 2001 to 2015. Panel B shows the statistics for 153
investments made in China from 2001 to 2015. Investment is the amount of money invested in the private debt investment.
Realised is the return to the private debt investment comprising principal, coupon and additional payments (e.g. upfront
arrangement fees; early prepayment fees). Unrealised is the credit manager assessed fair market value of the remaining loan.
Total Return is calculated as the summation of an investment’s realised proceeds and unrealised (fair market) value at the
valuation date. IRR is the internal rate of return to the private debt investment, calculated from the audited cashflows of the
credit fund manager. ROI is the return on investment (or return multiple) (defined as the total amount of capital returned
divided by the initial investment outlay). All figures are current US dollars.
Panel A
Average
Median
Stdev
Max
Min
N
Total
Investment
24,412,910
16,591,175
29,413,605
300,000,000
200,000
443
9,860,000,000
Realised
19,821,816
7,987,743
30,638,216
204,000,000
-747,916
267
5,290,000,000
Unrealised
16,097,721
2660000
32,831,099
332,000,000
-3,977,002
221
3,560,000,000
Total
30,963,493
18,255,999
39,133,295
332,000,000
0
358
11,100,000,000
IRR
32%
22%
76%
1310%
-100%
409
ROI
1.33
1.27
0.47
3.97
0
393
Investment
33,033,140
21,950,363
38,260,641
300,000,000
200,000
153
5,054,070,848
Realised
31,548,697
17,230,903
38,058,796
203,879,478
-747,916
101
3,186,418,400
Unrealised
17,679,976
0
45,195,458
332,438,356
-3,977,002
92
1,626,557,758
Total
43,113,996
30,438,956
48,947,467
332,385,737
0
134
5,777,276,091
IRR
39%
21%
116%
1310%
-46%
150
ROI
1.29
1.27
0.47
3.14
0
136
Panel B
Average
Median
Stdev
Max
Min
N
Total
31
TABLE 2
Asia Private Debt Investment Returns by Geography and Industry
Summary Statistics
Table 2 shows the summary statistics for over 443 private debt investments made by fifteen specialist credit investment
funds in private companies in 13 Asian countries from 2001 to 2015. Panel A shows the data by the country of the company
issuing the private debt (country was defined by the credit fund manager). Panel B shows the data by industry classification,
using Global Industry Classification Codes (GICS). IRR is the internal rate of return to the private debt investment,
calculated from the audited cashflows of the credit fund manager. ROI is the return on investment (or return
multiple)(defined as the total amount of capital returned divided by the initial investment outlay).
Panel A
Country
China
Australia
Indonesia
Hong Kong
India
Korea
New Zealand
Thailand
Singapore
Philippines
Taiwan
Japan
Malaysia
Panel B
GICS Code/Sector
10 Energy
15 Materials
20 Industrials
25 Consumer Discretionary
30 Consumer Staples
35 Health Care
40 Financials
45 Information Technology
50 Telecommunication Services
55 Utilities
IRR
ROI
Frequency
160
64
55
17
104
10
7
7
11
3
3
1
1
Percent
36.0%
14.4%
12.4%
3.8%
23.4%
2.3%
1.6%
1.6%
2.5%
0.7%
0.7%
0.2%
0.2%
Mean
39.3%
19.3%
26.8%
48.2%
26.7%
30.2%
13.4%
28.7%
46.0%
34.0%
21.7%
18.0%
29.0%
Median
20.0%
14.8%
15.6%
24.8%
24.3%
27.0%
25.0%
24.5%
20.1%
32.0%
16.0%
18.0%
29.0%
Mean
1.29
1.25
1.26
1.24
1.42
1.37
1.24
1.64
1.28
2.23
1.35
NA
2.30
Median
0.32
0.21
0.59
0.56
0.53
0.54
0.53
0.83
0.74
1.20
0.78
NA
1.30
25
38
61
60
26
4
207
6
3
13
5.6%
8.6%
13.7%
13.5%
5.9%
0.9%
46.6%
1.4%
0.7%
2.9%
21.9%
22.2%
34.7%
31.5%
30.8%
34.0%
33.5%
19.1%
41.6%
38.5%
15.0%
19.2%
19.7%
18.0%
23.6%
30.5%
22.7%
17.9%
41.0%
28.0%
1.19
1.33
1.22
1.36
1.27
1.91
1.35
1.29
1.21
1.58
0.28
0.32
0.29
0.22
0.49
0.92
0.57
0.18
0.78
0.72
32
TABLE 3
Investment Returns to Buy-and-Hold and Secondary Trading Strategies
Table 3 shows univariate returns by investment strategy and seniority of debt instrument. Primary indicates that the
investment made by the credit fund manager was at the primary issuance of the loan, and that the investment remained in the
portfolio until realisation. Secondary indicates that the investment made by the credit fund manager was acquired on the
secondary market. Senior secured and subordinated refers to whether the loan was senior or subordinated in the capital
structure. IRR is the internal rate of return to the private debt investment, calculated from the audited cashflows of the credit
fund manager. ROI is the return on investment (or return multiple)(defined as the total amount of capital returned divided by
the initial investment outlay).
Panel A
Senior (0)
IRR (%)
N
Subordinated (1)
IRR (%)
N
Panel B
Senior (0)
ROI
N
Subordinated (1)
ROI
N
Primary (0)
Secondary (1)
31.0%
308
46.3%
30
28.4%
58
Primary (0)
27.4%
11
Secondary (1)
1.29
294
1.75
30
1.24
56
1.70
11
33
TABLE 4
Regressions Results for Buy-and-Hold and Secondary Trading Strategies
Table 4 reports results for twelve estimations of the generalised regression model Returns = f(Secondary, Subordinated, LBO, Age, Historcial AuM, Cost of Contract Enforcement), with and
without with fund fixed effects. The base return (0,0) is a buy-and-hold investment at primary issuance. Indicator variables equal one for the type of investment, zero otherwise. IRR is the
internal rate of return to the private debt investment, calculated from the audited cashflows of the credit fund manager. ROI is the return on investment (or return multiple)(defined as the total
amount of capital returned divided by the initial investment outlay). LBO is an indicator variable showing whether the loan was to a firm which was owned by a private equity fund. Age is
measured as the number of years the credit fund manager has been in business. Historical AuM in Strategy is the total dollar amount of assets under management the credit fund manager has
raised since inception. Cost of Contract Enforcement (Enforcing Contracts DTF) is the distance to frontier score measuring the time, cost and procedural complexity to resolve a standardized
commercial dispute (as measured by the World Bank Doing Business, Enforcing Contracts score for each country/jurisdiction matched to each loan).
Fund Fixed Effects
Model
Intercept
Secondary
Subordinated
(1)
0.238***
(0.019)
0.102**
(0.043)
-0.034
(0.037)
LBO
IRR
(2)
0.242***
(0.022)
0.104**
(0.044)
-0.051
(0.031)
0.043
(0.068)
(4)
1.391***
(0.059)
0.465***
(0.114)
-0.014
(0.112)
-7.08E-5
(0.001)
(3)
0.266***
(0.026)
0.088**
(0.044)
-0.056*
(0.031)
0.023
(0.066)
-0.005
(0.004)
-4.27E-6
(8.59E-6)
0.001
(0.001)
-0.002*
(0.001)
1.29%
1.53%
9.30%
Age
Historical AuM in Strategy
Enforcing Contracts DTF
2.70E-6
(0.001)
1.35%
Adj R 2
*p < 0.10, **p < 0.05, ***p < 0.01.
Standard errors are reported in parentheses.
ROI
(5)
1.408***
(0.057)
0.471***
(0.117)
-0.082
(0.150)
0.211
(0.203)
(7)
0.001
(0.023)
0.147**
(0.059)
-0.082*
(0.043)
-0.002**
(0.001)
(6)
1.468***
(0.057)
0.401***
(0.126)
-0.126
(0.142)
0.158
(0.216)
-0.013*
(0.007)
-2.92E-5*
(1.64E-5)
0.001
(0.002)
0.001
(0.001)
9.95%
11.10%
2.29%
34
IRR
(8)
0.001
(0.023)
0.148**
(0.059)
-0.082*
(0.043)
-0.002
(0.089)
ROI
(11)
-0.054**
(0.022)
0.199*
(0.122)
0.256**
(0.105)
0.019
(0.196)
(10)
-0.054**
(0.022)
0.199
(0.122)
0.255**
(0.104)
0.001
(0.001)
(9)
0.001
(0.037)
0.148**
(0.059)
-0.082*
(0.043)
-0.002
(0.090)
-1.18E-4
(0.003)
4.38E-7
(7.40E-6)
0.001
(0.001)
0.001
(0.002)
0.001
(0.002)
(12)
-0.138**
(0.061)
0.198
(0.121)
0.258**
(0.104)
0.028
(0.195)
0.003
(0.006)
3.13E-5**
(1.51E-5)
0.001
(0.002)
2.04%
1.54%
1.27%
1.02%
2.23%
TABLE 5
Asia Private Credit Excess Return Series
Summary Statistics
Table 5 shows summary statistics of the private credit excess return index, where excesses are measured as the difference
between the overall Asia private credit return index, China private credit return index and Rest of Asia private credit return
index and the J.P. Morgan Asia Credit Index (JACI). Liq1 is the first liquidity measure that calculates the quarterly year-onyear percentage change in cross-border and domestic credit, using data from the Bank of International Settlements Liq2 is
the second liquidity measure that calculates the quarterly percentage change in cross-border credit. The change is estimated
as, for example, VIXt – VIXt-1.
Summary
Statatistics
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
ADF t-stats
Excess Index
All
1.08%
1.01%
16.28%
-5.10%
2.82%
1.20
8.99
-9.19
Excess Index
China
0.82%
0.40%
22.95%
-8.46%
3.97%
2.22
12.94
-7.64
Excess Index
Non-China
1.18%
1.25%
16.13%
-7.83%
3.01%
1.02
8.69
-9.47
ΔVIX
0.04%
-0.41%
20.50%
-15.28%
5.21%
0.81
6.31
-9.58
35
ΔTED
-0.09%
-0.25%
203.00%
-86.00%
28.55%
3.50
27.18
-12.55
Liq1
-8.76%
-10.23%
35.14%
-33.55%
16.61%
0.84
3.68
-2.27
Liq2
0.03%
0.03%
0.06%
-0.02%
0.02%
-0.38
2.63
-3.38
TABLE 6
Asia Private Credit Excess Return Series
Correlation Probability Matrix
Table 6 shows the correlation probabilities between the excess return time series, the lagged change of TED, the
contemporaneous change of VIX and two Liquidity measures. TED is measured as the daily percentage spread between 3Month LIBOR rate (based on U.S. dollars) and the 3-Month Treasury bill rate, as calculated by the Federal Reserve Bank of
St. Louis. VIX is measured as the change in the volatility index (VIX) as calculated by the Chicago Board Options
Exchange. Liq1 is the first liquidity measure that calculates the quarterly year-on-year percentage change in cross-border and
domestic credit, using data from the Bank of International Settlements Liq2 is the second liquidity measure that calculates
the quarterly percentage change in cross-border credit. The change is estimated as, for example, ΔVIXt = VIXt – VIXt-1.
Correlation
Excess All
Excess China
Excess Rest of Asia
ΔVIXt
ΔTEDt-1
Liq1t
Liq2t
Excess All
1.000
0.739***
0.886***
0.413***
0.416***
-0.024
0.091
Excess China
Excess
Rest of Asia
ΔVIXt
ΔTEDt-1
Liq1t
Liq2t
1.000
0.423***
0.347***
0.243**
0.013
0.050
1.000
0.345***
0.422***
-0.106
0.090
1.000
0.195**
-0.080
0.115
1.000
-0.099
0.197**
1.000
0.049
1.000
*p < 0.10, **p < 0.05, ***p < 0.01.
36
TABLE 7
Regressions Results of Asia Private Credit Excess Return Series,
Credit Risk, Volatility and Liquidity
Table 7 shows various results for estimations of the generalised regression model EXCESSt = a + b(ΔVIXt) + b(ΔTEDt-1) + d(Liquidityt) + e. Excess is measured as the difference between the
various indices and the J.P. Morgan Asia Credit Index (JACI). TED is measured as the daily percentage spread between 3-Month LIBOR rate (based on U.S. dollars) and the 3-Month Treasury
bill rate, as calculated by the Federal Reserve Bank of St. Louis. VIX is measured as the change in the volatility index (VIX) as calculated by the Chicago Board Options Exchange. Liq1 is the
first liquidity measure that calculates the quarterly year-on-year percentage change in cross-border and domestic credit, using data from the Bank of International Settlements Liq2 is the second
liquidity measure that calculates the quarterly percentage change in cross-border credit. The change is estimated as, for example, ΔVIXt = VIXt – VIXt-1. Note: For tests with China Excess
return index, the data range is between January 2006 to December 2013, due to lack of continuous investments data in China in 2014 and 2015. The standard errors are reported in brackets are
computed using max[3, 2*horizon] Newey–West lags. ***, **, * denote significance at the 0.01, 0.05 and 0.10 level, respectively.
Model
Intercept
ΔVIXt
Excess All
Excess China
0.011***
0.011***
0.010***
0.010***
0.005
0.004
0.007**
0.006*
0.012***
0.012***
0.010***
0.011***
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
0.211**
0.179***
0.198***
0.196***
0.326***
0.296***
0.303***
0.293***
0.189**
0.153***
0.165***
0.166***
(0.086)
(0.048)
(0.050)
(0.050)
(0.082)
(0.050)
(0.055)
(0.048)
(0.084)
(0.045)
(0.048)
(0.049)
0.035***
0.034***
0.034***
0.024*
0.025***
0.023**
0.039***
0.038***
0.039***
(0.011)
(0.010)
0.007
(0.010)
(0.012)
(0.009)
(0.012)
(0.010)
(0.011)
(0.010)
ΔTEDt-1
Liq1t
(0.014)
Liq2t
Adj R
2
Excess Rest of Asia
14.4%
25.7%
27.0%
0.028*
-0.009
(0.016)
(0.0164
-0.653
-2.880
-0.555
(3.947)
(5.355)
(3.429)
26.8%
25.8%
30.4%
37
32.0%
29.9%
9.9%
22.5%
23.2%
22.9%
FIGURE 1
Asia Private Credit Excess Return Series,
Moving Average Monthly Excess Returns 2005 - 2015
Figure 1 shows excess return time series for the APCR index, the J.P. Morgan Asia Credit Index (JACI) and excess return
index. Excess is measured as the difference between the various models and the JACI.
20.00%
15.00%
10.00%
5.00%
-5.00%
1/1/2006
5/1/2006
9/1/2006
1/1/2007
5/1/2007
9/1/2007
1/1/2008
5/1/2008
9/1/2008
1/1/2009
5/1/2009
9/1/2009
1/1/2010
5/1/2010
9/1/2010
1/1/2011
5/1/2011
9/1/2011
1/1/2012
5/1/2012
9/1/2012
1/1/2013
5/1/2013
9/1/2013
1/1/2014
5/1/2014
9/1/2014
1/1/2015
5/1/2015
9/1/2015
0.00%
-10.00%
-15.00%
-20.00%
JACI
APCR
38
APCR Excess
FIGURE 2
China and Rest of Asia Private Credit Excess Return Series,
Moving Average Monthly Excess Returns 2005 - 2015
Figure 2 shows the China private credit excess return time series and the Rest of Asia private credit excess return time series.
Excess is measured as the difference between the respective index and the J.P. Morgan Asia Credit Index (JACI).
20.00%
15.00%
10.00%
5.00%
1/1/2006
5/1/2006
9/1/2006
1/1/2007
5/1/2007
9/1/2007
1/1/2008
5/1/2008
9/1/2008
1/1/2009
5/1/2009
9/1/2009
1/1/2010
5/1/2010
9/1/2010
1/1/2011
5/1/2011
9/1/2011
1/1/2012
5/1/2012
9/1/2012
1/1/2013
5/1/2013
9/1/2013
1/1/2014
5/1/2014
9/1/2014
1/1/2015
5/1/2015
9/1/2015
0.00%
-5.00%
-10.00%
Excess China
Excess Rest of Asia
39
TABLE A1
Private Credit Return Index Coupon Frequency and Coupon Rate Assumptions
Table A1 shows assumptions adopted in the private credit index with regards to the frequency of coupon payments and
coupon rates.
Coupon Frequency
(days)
Half Coupon
Full Coupon
90
a. 2.50%
b. 5%
180
c. 5%
d. 10%
360
e. 10%
f. 20%
40
FIGURE A1
Asia Private Credit Excess Return Series,
Moving Average Monthly Excess Returns 2005 – 2015
Comparison of Returns Under Different Coupon and Periodicity Assumptions
Figure A1 shows excess return time series graphs under six different assumptions (Models a – f) with regards to the
frequency of coupon payments and coupon rates. Excess is measured as the difference between the various models and the
J.P. Morgan Asia Credit Index (JACI). Excess a, c and e shows the excess return to the private credit index over the JACI,
assuming that the investments in the private credit index pay coupons to investors every 90, 180 and 360 days at a rate of
2.5%, 5% and 10% per period. Excess b, d, and f shows the excess return to the private credit index over the JACI, assuming
that the investments in the private credit index pay coupons to investors every 90, 180 and 360 days at a rate of 5%, 10% and
20% per period. We compare these variations with the main assumption of the APCR index of 13.8% yield, payable on a
quarterly basis.
20.0%
15.0%
10.0%
5.0%
-5.0%
1/1/2006
5/1/2006
9/1/2006
1/1/2007
5/1/2007
9/1/2007
1/1/2008
5/1/2008
9/1/2008
1/1/2009
5/1/2009
9/1/2009
1/1/2010
5/1/2010
9/1/2010
1/1/2011
5/1/2011
9/1/2011
1/1/2012
5/1/2012
9/1/2012
1/1/2013
5/1/2013
9/1/2013
1/1/2014
5/1/2014
9/1/2014
1/1/2015
5/1/2015
9/1/2015
0.0%
-10.0%
90-day 10%
180-day 10%
360-day 10%
180-day 20%
360-day 20%
90-day 13.8%
41
90-day 20%