Evolving Relationship between the Dollar and the Price of Oil: An Empirical Investigation Peter Brust * Vivekanand Jayakumar Abstract As the global economy evolves, changes in the interaction between the value of the American dollar and the price of key commodities are to be expected. This paper evaluates the changing nature of the relationship between the value of the trade­weighted dollar index and the dollar price of oil using data from the period 1986­2009. In particular, it shows that in recent years (since 1999) an inverse relationship between the value of the dollar index and crude oil prices occurred. The paper also attempts to provide explanations for this recent observation of the negative link between dollar value and crude oil prices. Aspects highlighted include – the growing significance of oil demand from fast growing emerging economies (that is, rising importance of non­US oil demand), and the increased interest in commodity investments among speculators and financial market participants as a hedge against dollar weakness and inflationary concerns. I. INTRODUCTION Many financial market observers and commodity traders have in recent years suggested the possibility of a link between oil prices and the value of the US dollar. Nowadays, the financial press often contains reports noting that a weak dollar may be behind rising oil prices, or alternately, that a strong dollar may be pushing oil prices down (Birkner (2009), Hanke (2008a), and Hanke (2008b)). However, no established consensus exists in the economic literature regarding the nature of the relationship between oil prices and the dollar. This paper hopes to contribute by undertaking a simple statistical and economic analysis of the relationship between oil prices and the value of the US dollar index, using recent data. We first examine the statistical relationship between monthly crude oil prices and the value of the dollar during the January 1986 to August 2009 period. A primary goal of our paper is to capture evidence of the presence of a link between oil prices and the value of the dollar, and to establish the direction of causality. Our findings suggest that, unlike the period prior to 1999, a statistical link between crude oil prices and the dollar index does exist between January 1999 and August 2009. Additionally, our analysis suggests that variations in the value of the dollar index Granger causes oil price changes. Following our statistical evaluation of the relationship between oil prices and the dollar index, we attempt to explain the observed direction of causation between the two variables during recent years. We specifically focus on two broad sets of explanations to explain the negative impact of the value of the US dollar index on crude oil prices. Specifically, we emphasize the impact in recent years of a decrease in the significance of US demand for oil (which is relatively stable and in­elastic) and the concomitant increase in the significance of demand for oil from fast growing emerging markets (where consumption levels are rising rapidly). Also, we highlight the role played by financial speculators and investors pursuing investment alternatives to a weakening dollar. Recently, whenever the dollar has weakened and whenever concerns regarding US inflation expectations have grown, many speculators and * Brust and Jayakumar are from Sykes College of Business at University of Tampa. Burst is an Associate Professor of Economics, while Jayakumar is an Assistant Professor of Economics. Their emails are [email protected] and [email protected] respectively.
Journal of Financial and Economic Practice Page 1 investors have turned to commodities such as oil, which may act as a hedge against a falling dollar or high inflation. The rest of the paper is organized as follows. In section II, we discuss the statistical analysis (structural break tests and Granger causality tests) undertaken to establish the ties between dollar value and crude oil prices. Section III examines the factors that may have led to the negative relationship between the dollar and crude oil price in recent years. Finally, we conclude in section IV. II. EXAMINING THE DOLLAR INDEX – CRUDE OIL PRICE LINK Earlier studies have noted the presence of a link between the value of the US dollar and the dollar price of crude oil. For instance, Krugman (1983a, 1983b) and Golub (1983) argued that rising oil prices transfer wealth from oil importers to oil exporters. With their additional wealth, exporters choose to invest in dollar assets. Such dollar asset purchases increase dollar demand, hence the dollar appreciates. It is worth noting that the direction of causation according to Krugman (1983a, 1983b) and Golub (1983) goes from oil prices to the value of the dollar. Similarly, Amano and Norden (1998a, 1998b) found that the US real exchange rate and oil prices were co­integrated in the post­Bretton Woods era, with the direction of causality going from oil prices to real exchange rates but not vice versa. Relatedly, Chaudhuri and Daniel (1998) analyzed the impact of real oil prices on the behavior of real US dollar (producer price based) exchange rates for a sample of 16 OECD countries during the post­Bretton Woods era. They found that, for the period (primarily, 1973­1996) considered, most of the real exchange rates and real oil price were co­integrated and the direction of causality was found to go from real oil prices to real exchange rates. More recently, Benassy­Quere, et al., (2007) find causation running from oil prices to the dollar exchange rate at a 10% significance level for the period ending in the early 2000s. Coudert, et al., (2008) also suggest that the causality runs from oil prices to the dollar exchange rate. Their analysis covers the 1974­2004 time period and they find that the channel of transmission is through the US net international investment position. Meanwhile, Medlock and Jaffe (2009) highlight the trade sector transmission mechanism – they propose that rising oil prices expand the US trade deficit which leads to a weaker dollar. The weak dollar in turn drives oil prices upward, and a self­perpetuating cycle is started. Interestingly, Zhang, et al. (2008), in their examination of the spillover effect of the dollar exchange rate on crude oil prices during the period 2000­2005, find that dollar depreciation is a contributing factor to high oil prices but the volatility spillover from the dollar foreign exchange market to the oil market is not statistically significant. This study attempts to unravel the direction of the causality between oil prices and the value of the dollar index using data from January 1986 to August 2009. Specifically, we attempt to answer three interesting questions regarding the relationship between dollar value and crude oil prices:
· Does a statistical relationship exist between the value of the dollar index and crude oil price?
· Is the relationship between the dollar index and crude oil prices observed only in recent data periods or has the link existed for longer durations?
· If a relationship exists, what is the direction of causality? That is, does the dollar value Granger cause an oil price change or does the oil price change Granger cause a dollar value change? A. Data:
Journal of Financial and Economic Practice Page 2 Our analysis covers the period between 1986 and 2009. We examine monthly data from January 1986 to August 2009. The dollar index used in our analysis is the trade weighted­major currencies dollar index constructed by the US Federal Reserve (Data was obtained from the FRED II Database – Federal Reserve Bank of St. Louis 1 ). For oil price, we use the monthly spot price of West Texas Intermediate crude oil price (Cushing, OK; units ­ Dollars per Barrel). Data on oil prices were obtained from the Energy Information Administration (US Department of Energy). Figure 1 shows the times series of crude oil prices and the dollar index between January 1986 and August 2009 – our full sample period. We separate our full sample into two time periods – one between January 1986 and December 1998 and the other between January 1999 and August 2009 – in Figures 2 and 3. The choice of Dec 1998 as a break point is critical because it represents the lowest monthly WTI crude oil price ($11.35 per barrel) observed during the entire sample period. A graphical evaluation (Figures 2 and 3) indicates that there is a distinct change in the relationship involving crude oil prices and the dollar index going from one period to the next. Careful observation suggests no clear long­term structural relationship between crude oil price and the dollar index between 1986 and 1998. However, for the period between 1999 and late 2009, there are indications of a clear negative relationship between the two variables. The scatter plots confirm these preliminary observations. B. Identifying Structural Breaks: We next undertake a more thorough and statistically rigorous investigation of the interaction between the two variables by conducting Granger causality tests. Before proceeding with Granger causality tests, we first attempt to validate our choice of Dec 1998 as the specific structural breakpoint by undertaking a couple of useful statistical tests. To test for unknown structural breakpoints, we utilized the Quandt­Andrews Unknown Breakpoint Test (Andrews (1993), Andrews and Ploberger (1994), and Hansen (1997)). The test essentially consists of performing a single Chow’s Breakpoint Test at every observation between two periods (t0 and t1). The n test statistics obtained from the Chow’s Breakpoint Tests are then summarized into one test statistic, which is used to test the null hypothesis of no breakpoints between t0 and t1. Since the equations are linear in our case, the two statistics, the Likelihood Ratio F­statistic and the Wald F­statistic, obtained for each of the Chow tests are the same. Individual Chow test statistics are then summarized as follows (summary statistics are conveniently generated by a software package such as EVIEWS): The Sup or Maximum statistic (essentially the maximum of the individual Chow F­statistics) is given by: Max F = max ( ) (1) The Exp statistic is given by: Exp F = ln ∑ ( ) (2) Finally, the Ave statistic (essentially the average of the individual F­statistic) is given by: Ave F = ∑ Journal of Financial and Economic Practice ( ) (3)
Page 3 In Table 1, we report the results for the Quandt­Andrews Breakpoint Test (performed using EVIEWS). The test procedure evaluated 28 break points between January 1996 and December 1998, and based on the Max F test statistic, Dec 1998 was identified as a statistically significant breakpoint (Table 1). We undertook a simple Chow’s Breakpoint Test (Table 2) to verify the choice of Dec 1998 as the structural breakpoint period for our analysis. With the basic Chow test, the sample is split into two periods (Jan 1986 to Dec 1998, and Jan 1999 to August 2009) and estimates for each subsample is undertaken separately to see if there are significant differences between the two periods. Our results clearly indicate the presence of a structural break (based on F­statistic, Log­likelihood ratio, and Wald statistic, the null hypothesis that there are no breaks at the specified breakpoint can be rejected at the 1% level). C. Granger Causality Test: A key aspect of Granger causality test analysis is the choice of an appropriate set of lags for the variables of interest. To determine the proper number of lags to use in the Granger causality test, we employed the Lag Exclusion Wald Test (using EVIEWS). As shown in Table 3, the joint probability suggests that the inclusion of three lags would be optimal (Wald F­ statistics indicates that the joint hypothesis that coefficients for lags one thru three equal zero can be rejected at the 5% significance level). We also undertook VAR Lag Order Selection Criteria procedure (using EVIEWS) to identify the right number of lags, and three lags were found to be appropriate based on the Schwarz Information Criteria (Table 4). Following the determination of appropriate lags (L), we carried out Granger causality tests to examine the link between oil prices and the dollar index. In general, when addressing the issue of whether two variables (suppose that x represents oil price and z represents the dollar index value) are related, Granger causality tests (Granger, 1969) examine if zt can be explained by the values of zt­1, z t­2 … z t­L, and check if the inclusion of xt­1, xt­2 … xt­L improves the explanatory power. So, x is said to Granger cause z if the inclusion of lagged x variables improves the predictability of z (the coefficients on the lagged x­variables are statistically significant in this case). Checking for two way causation (does x Granger cause z, and does z Granger cause x) is often quite appropriate, especially when theory does not provide a clear and unambiguous direction of causality. z t = γ0+ γ1z t­1 + … + γLz t­L + δ1xt­1 + …+ δLxt­L + εt xt = γ0+ γ1xt­1 + … + γLxt­L + δ1z t­1 + …+ δLz t­L + εt (4) (5) F­statistics (Wald statistics) are utilized to test the joint hypothesis that δ1= δ2 = … = δL = 0 for equation (4) and for equation (5). Granger causality tests (using three lags) were conducted for the following three sample periods: full sample (January 1986 – August 2009); sample I (January 1986 – December 1998); and, sample II (January 1999 – August 2009). As shown in Table 5, we find that the hypothesis that changes in dollar index does not Granger cause changes in crude oil prices can be rejected only for the January 1999 – August 2009 sample period. We are unable to reject the hypothesis that changes in dollar index does not Granger cause changes in crude oil prices for the full sample period (January 1986 – August 2009) and for sample I (January 1986 – December 1998). Interestingly, for all three sample period breakdowns, we cannot reject the null hypothesis that oil price changes do not Granger cause changes in dollar index. The above results suggest that
Journal of Financial and Economic Practice Page 4 during recent years (January 1999 – August 2009) changes in the dollar index influences crude oil prices but not vice versa. Based on our empirical analysis in this section, it is apparent that only during the past decade or so, we find evidence of a statistically significant link between the value of the dollar and crude oil prices. Also, the direction of causality appears to go from the dollar value to crude oil prices. This result is in contrast to that obtained by several earlier studies, which suggested that the direction of causality goes from oil prices to the dollar exchange rate. The following section examines a few important factors that may be driving the relationship between the dollar index and crude oil prices in recent years. III. FACTORS DRIVING THE DOLLAR VALUE­CRUDE OIL PRICE RELATIONSHIP In this section, we focus on three possible explanations for the growing negative link between the value of the dollar and the price of crude oil. First, it is highly likely that the low interest rate environment prevalent during much of the past decade encouraged investors to search for higher yields in commodity markets. Typically, low interest rates are expected to lead to less risk aversion and greater focus on non­traditional financial instruments. As real interest rates declined during the past decade, and as the dollar weakened, the search for higher yields and for protection against a weak dollar led many investors to the commodity markets. There is growing evidence that crude oil contracts were being used as a hedge against dollar weakness and inflationary concerns. For instance, Medlock and Jaffe (2009) note that the non­commercial share of open interest contracts in the US crude oil futures market increased during this period from approximately 20% to 55%. This increase in oil futures demand contributed to higher oil futures prices during periods of dollar weakness. Rising crude oil futures in turn lead to higher spot oil prices. To gauge the effects of a low interest rate environment, we conducted an empirical investigation of the relationship between real interest rates and crude oil prices. Prior to undertaking a regression analysis using time series data, it is generally appropriate to check for stationarity. Augmented Dickey Fuller Test and the Phillip­Perron Test were used to test for unit roots (test results shown in Appendix). The test results suggest that crude oil prices (WTI_SPOT_PRICE), the value of the dollar index (DOLLAR_INDEX_MC) and real interest rates 2 (R) all exhibit non­stationarity. Hence, the first differences of these variables (which were found to be stationary) were used in our regression analysis. Table 6 presents the regression results for two sets of regressions. First, we find that the value of the dollar index has a statistically significant impact on crude oil prices only in recent periods, a result that reinforces our conclusion from the previous section. The dummy variable (D_99_09 takes a value of 0 for the period between January 1986 and December 1998 and takes a value of 1 for the period between January 1999 and August 2009) is used to set up a regression that attempts to check for the impact of changes in the value of the dollar index on crude oil prices (equation (6)). Results clearly suggest that the value of the dollar index negatively impacts crude oil prices for only the most recent period (α2 is estimated to be ­1.131078 and the null hypothesis that the α2 is zero can be rejected at the 1% level; the null hypothesis that α1 and α3 are each equal to zero cannot be rejected at even the 10% level). D(WTI_SPOT_PRICEt) = α0+ α1D_99_09 +α2 D_99_09×D(Dollar_Index_MCt) + α3D(Dollar_Index_MCt) + εt (6) Taking into account the relationship between the value of the dollar index and crude oil prices, we next consider whether real interest rates also affect oil prices (see equation (7)). Our
Journal of Financial and Economic Practice Page 5 findings (β 2 is ­3.351764 and the null hypothesis that β 2 is zero can be rejected at the 1% level) suggest that real interest rates have a significant and negative effect on crude oil prices (see bottom of Table 6). This is as expected given our earlier discussion. D(WTI_SPOT_PRICEt) = β0+ β 1 D_99_09×D(Dollar_Index_MCt) + β 2D(R) + εt (7) A second explanation is based on the fact that oil is priced in dollars internationally (Brown, et al. 2008). Whenever the US dollar declines, the real price of oil to foreign purchasers abates (in countries whose currencies are appreciating relative to the dollar). Thus, the world quantity demanded of oil may remain strong despite the price pressures wrought upon American consumers by a falling dollar. The level of demand destruction from the rising dollar price of crude oil has been minimal or non­existent in recent years. On the supply side, OPEC member nations, especially those in the Middle East, are increasingly tied economically to the EU and East Asia and not as dependent on conditions in the US (Higgins, et al. 2006). In fact, imports from EU account for a much larger share (relative to imports from the US) of total imports purchased by oil exporting countries of the Middle East. The trade patterns of the so­called Gulf­ Cooperation Countries (GCC) countries clearly highlight the significance of imports from the EU. During 2008, for instance, imports from EU as a share of total imports was 31.3% in the case Saudi Arabia, whereas the US accounted for only 12.5%. In the case of United Arab Emirates (UAE), imports from EU accounted for 27.4% of all imports, while imports from the US were 9.3%. For Qatar, imports from EU accounted for 39% of all imports while US imports were 12.4% of the total. In the case of Oman, it was 24.2% from EU and 7% of total imports from the US. In the case of Kuwait, 28.6% of total imports came from the EU and 11.6% came from the US. Finally, Bahrain imported 21.5% of its total imports from the EU and just 8.1% from the US 3 . Thus, petrodollar recycling (into US goods and services) has become considerably less important. In fact, a weakening dollar these days is more likely to elicit upward price pressure on crude oil prices as Middle East (and OPEC) consumers try to maintain their purchasing power in terms of the euro, pound or the yen rather than in terms of the US dollar. Lastly, an important factor driving the negative link between the value of the US dollar index and crude oil prices is the changing pattern in oil consumption at the global level (Hamilton, 2009). While the US still remains the world’s largest consumer of oil, its share of the global oil market has started to decline of late. In fact, much of the global growth in oil consumption during the past decade was driven by fast growing emerging markets. Figure 4 clearly highlights the growing influence of emerging countries on oil markets (and thus on oil prices). It is apparent that much of the increase in global oil demand in recent periods is being driven by increased consumption by fast growing countries such as Brazil, India and China (BIC). In contrast, the oil consumption levels are relatively stable in advanced economies such as the US and the EU (Halligan, 2009). Recently, even the International Energy Agency (IEA) acknowledged that emerging markets were overturning the long standing patterns of global oil consumption, as non­OECD actors increasingly drive world oil demand (Blas, 2010). China has fast become one of the most important crude oil consumers as its rapid economic growth creates legions of new middle class customers (Leung, 2010). A startling development over the past two decades has been the evolution of China from being a net exporter to one of the largest importers of oil. As shown in Figure 5, the rise of China as a major oil importer is increasingly becoming apparent. As China has become a major player in global oil markets, the significance of the US and the American dollar in the crude oil marketplace has declined.
Journal of Financial and Economic Practice Page 6 It is worth noting some export dependent emerging markets such as China peg their currencies to the US dollar. As a result, they may see their export competitiveness actually improve due to dollar weakness against other major currencies. This in turn may lead to an economic upswing in such economies, which may end up boosting their consumption of energy and thus their oil consumption. The growing economic clout of emerging markets and their effect on the crude oil market may be a key factor behind the recently observed link between the value of the dollar and crude oil prices. IV. CONCLUSION This paper has provided a thorough statistical examination of the relationship between the value of the dollar index and the crude oil price. Empirical evidence from recent years reveals the presence of a significant and negative relationship between dollar value and crude oil price. Our analysis of the direction of causality indicates that the changes in the value of the dollar negatively impacts crude oil prices. Among the likely reasons behind the recently observed links between the US dollar index and crude oil prices are the growing role of financial market participants and speculators in the commodity markets as they search for better yields (in a low interest environment) and attempt to overcome the effects of dollar weakness. Additionally, the increasing role played by non­American consumers and producers in the global crude oil market is of great significance. The lack of oil demand destruction in the face of rising dollar price of oil observed in recent years is a clear indicator of the evolving global landscape. The extraordinary growth in China and other emerging markets and their increasing clout in the commodity markets appear to be a factor behind the dollar value and oil price link. Notes 1 Note: The Fed describes major currencies based dollar index as a: “weighted average of the foreign exchange value of the U.S. dollar against a subset of the broad index currencies that circulate widely outside the country of issue”. Major currency index includes the Euro Area, Canada, Japan, United Kingdom, Switzerland, Australia, and Sweden. 2 Monthly real interest rates were calculated by subtracting the inflation rate (percentage change in CPI) from the yield on the 3­month T­Bill. 3 The import share data for 2008 was obtained from the EU Commission (http://ec.europa.eu/trade/creating­opportunities/bilateral­relations/regions/gulf­region/)
Journal of Financial and Economic Practice Page 7 Figure 1: Monthly US Dollar Index & Crude Oil Prices 1986­2009 (Data Source: Federal Reserve Bank of St. Louis – FRED II Database; and, EIA – US Department of Energy) Figure 2: Monthly US Dollar Index & Crude Oil Prices 1986­1998 – Time Series & Scatter Plot (Data Source: Federal Reserve Bank of St. Louis – FRED II Database; and, EIA – US Department of Energy)
Journal of Financial and Economic Practice Page 8 Figure 3: US Dollar Index & Crude Oil Prices (1999­2009) – Time Series & Scatter Plot (Data Source: Federal Reserve Bank of St. Louis – FRED II Database; and, EIA – US Department of Energy) Figure 4: Oil Consumption ­ Major Regions (Data Source: BP Statistical Review – 2009; Note: BIC refers to Brazil, India and China)
Journal of Financial and Economic Practice Page 9 Figure 5: US and China – Net Export (+), Net Import (­) of Oil (Source: US Energy Information Administration) Table 1: Quandt­Andrews Breakpoint Test Quandt­Andrews Unknown Breakpoint Test Null Hypothesis: No breakpoints within trimmed data Equation Sample: 1986M01 2009M08 Test Sample: 1996M09 1998M12; Number of breaks compared: 28 Statistic Maximum LR F­statistic (1998M12) Maximum Wald F­statistic (1998M12) Value 278.2182 Prob. 0.0000 278.2182 0.0000 Exp LR F­statistic Exp Wald F­statistic 135.8117 135.8117 0.0000 0.0000 Ave LR F­statistic Ave Wald F­statistic 233.9359 233.9359 0.0000 0.0000 Table 2: Chow Breakpoint Test Chow Breakpoint Test: 1998M12 Null Hypothesis: No breaks at specified breakpoints Varying Regressors: DOLLAR_INDEX_MC Equation Sample: 1986M01 2009M08 F­statistic 225.8218 Prob. F(1,281) Log likelihood ratio 167.5045 Prob. Chi­Square(1) Wald Statistic 225.8218 Prob. Chi­Square(1) Journal of Financial and Economic Practice 0.0000 0.0000 0.0000
Page 10 Journal of Financial and Economic Practice Page 11
Table 3: VAR Lag Exclusion Wald Tests (Note: WTI_SPOT_PRICE refers to crude oil price) Sample: 1986M01 2009M08; Included observations: 278 Chi­squared test statistics for lag exclusion [Numbers in [ ] are p­values]: Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 WTI_SPOT_PRICE 495.8877 [ 0.000000] 3.017031 [ 0.082394] 4.601966 [ 0.031935] 0.025537 [ 0.873037] 1.093540 [ 0.295688] 6.948941 [ 0.008387] Joint 495.8877 [ 0.000000] 3.017031 [ 0.082394] 4.601966 [ 0.031935] 0.025537 [ 0.873037] 1.093540 [ 0.295688] 6.948941 [ 0.008387] Table 4: VAR Lag Order Selection Criteria (Schwarz Information Criterion) VAR Lag Order Selection Criteria Endogenous variables: WTI_SPOT_PRICE Exogenous variables: C DOLLAR_INDEX_MC Sample: 1986M01 2009M08 Included observations: 278 Lag SC 0 8.620681 1 5.580853 2 5.338816 3 5.335813* 4 5.343489 5 5.350025 6 5.344857 * indicates lag order selected by the criterion SC: Schwarz information criterion
Journal of Financial and Economic Practice Page 12 Table 5: Pairwise Granger Causality Tests Pairwise Granger Causality Tests Sample: 1986M01 2009M08 Lags: 3 Null Hypothesis: Obs DOLLAR_INDEX_MC does not Granger Cause WTI_SPOT_PRICE 281 WTI_SPOT_PRICE does not Granger Cause DOLLAR_INDEX_MC F­Statistic Prob. 0.72294 1.00996 0.5390 0.3887 F­ Statistic Prob. 0.48630 0.57509 0.6923 0.6323 F­Statistic Prob. 3.59625 0.95334 0.0156 0.4172
Sample: 1986M01 1998M12 Lags: 3 Null Hypothesis: Obs DOLLAR_INDEX_MC does not Granger Cause WTI_SPOT_PRICE 153 WTI_SPOT_PRICE does not Granger Cause DOLLAR_INDEX_MC Sample: 1999M01 2009M08 Lags: 3 Null Hypothesis: Obs DOLLAR_INDEX_MC does not Granger Cause WTI_SPOT_PRICE 128 WTI_SPOT_PRICE does not Granger Cause DOLLAR_INDEX_MC Journal of Financial and Economic Practice Page 13 Table 6: Regression Results (Note D(variable) refers to the first difference of the variable) Dependent Variable: D(WTI_SPOT_PRICE) Method: Least Squares Sample (adjusted): 1986M02 2009M08 Included observations: 283 after adjustments Variable α0 D_99_09 D_99_09*D(DOLLAR_INDEX_ MC) D(DOLLAR_INDEX_MC) R­squared Adjusted R­squared S.E. of regression Sum squared resid Log likelihood F­statistic Prob(F­statistic) Coefficient Std. Error ­0.095706 0.293793 0.362623 0.436870 t­Statistic ­0.325759 0.830048 Prob. 0.7449 0.4072 ­1.131078 0.274259 ­0.131328 0.182276 ­4.124122 ­0.720491 0.0000 0.4718 0.125444 0.116040 3.639652 3695.931 ­765.1496 13.33969 0.000000 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan­Quinn criter. Durbin­Watson stat Dependent Variable: D(WTI_SPOT_PRICE) Sample (adjusted): 1986M02 2009M08 Included observations: 283 after adjustments Variable 0.170035 3.871182 5.435686 5.487212 5.456347 1.140856 Method: Least Squares Coefficient Std. Error t­Statistic Prob. β 0 0.082472 0.201504 D_99_09*D(DOLLAR_INDEX_ MC) ­0.976566 0.195082 D(R) ­3.351764 0.502906 0.409279 0.6826 ­5.005922 ­6.664796 0.0000 0.0000 R­squared Adjusted R­squared S.E. of regression Sum squared resid Log likelihood F­statistic Prob(F­statistic) 0.242122 0.236709 3.382118 3202.842 ­744.8877 44.72633 0.000000
Journal of Financial and Economic Practice Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan­Quinn criter. Durbin­Watson stat 0.170035 3.871182 5.285425 5.324070 5.300920 1.245222 Page 14 APPENDIX – UNIT ROOT TESTS Null Hypothesis: WTI_PRICE has a unit root Exogenous: Constant, Linear Trend Augmented Dickey­Fuller test statistic Test critical values: 1% level 5% level 10% level Phillips­Perron test statistic Test critical values: 1% level 5% level 10% level t­Statistic Prob.* ­3.984537 ­3.991053 ­3.425898 ­3.136128 0.0102 Adj. t­Stat Prob.* ­2.637470 ­3.990817 ­3.425784 ­3.136061 0.2640 t­Statistic Prob.* ­8.597219 ­3.991412 ­3.426073 ­3.136231 0.0000 Adj. t­Stat Prob.* ­9.512742 ­3.990935 ­3.425841 ­3.136094 0.0000 t­Statistic Prob.* ­2.233649 ­3.991053 ­3.425898 ­3.136128 0.4686 Adj. t­Stat Prob.* ­2.584352 ­3.990817 ­3.425784 ­3.136061
0.2879 Null Hypothesis: D(WTI_PRICE) has a unit root Exogenous: Constant, Linear Trend Augmented Dickey­Fuller test statistic Test critical values: 1% level 5% level 10% level Phillips­Perron test statistic Test critical values: 1% level 5% level 10% level Null Hypothesis: DOLLAR_INDEX has a unit root Exogenous: Constant, Linear Trend Augmented Dickey­Fuller test statistic Test critical values: 1% level 5% level 10% level Phillips­Perron test statistic Test critical values: 1% level 5% level 10% level Journal of Financial and Economic Practice Page 15 Null Hypothesis: D(DOLLAR_INDEX) has a unit root Exogenous: Constant, Linear Trend Augmented Dickey­Fuller test statistic Test critical values: 1% level 5% level 10% level Phillips­Perron test statistic Test critical values: 1% level 5% level 10% level t­Statistic Prob.* ­11.02969 ­3.991053 ­3.425898 ­3.136128 0.0000 Adj. t­Stat Prob.* ­11.42288 ­3.990935 ­3.425841 ­3.136094 0.0000 t­Statistic Prob.* ­2.470406 ­3.992670 ­3.426682 ­3.136590 0.3427 Adj. t­Stat Prob.* ­2.775727 ­3.990817 ­3.425784 ­3.136061 0.2076 t­Statistic Prob.* ­5.328654 ­3.992670 ­3.426682 ­3.136590 0.0001 Adj. t­Stat Prob.* ­12.51349 ­3.990935 ­3.425841 ­3.136094
0.0000 Null Hypothesis: R has a unit root Exogenous: Constant, Linear Trend Augmented Dickey­Fuller test statistic Test critical values: 1% level 5% level 10% level Phillips­Perron test statistic Test critical values: 1% level 5% level 10% level Null Hypothesis: D(R) has a unit root Exogenous: Constant, Linear Trend Augmented Dickey­Fuller test statistic Test critical values: 1% level 5% level 10% level Phillips­Perron test statistic Test critical values: 1% level 5% level 10% level Journal of Financial and Economic Practice Page 16 REFERENCES Amano, R.A. and Norden, S.V. (1998a). “Oil prices and the rise and fall of the US real exchange rate”, Journal of International Money and Finance, V.17 (2): pp. 299–316. Amano, R.A. and Norden, S.V. (1998b). “Exchange Rates and Oil Prices”, Review of International Economics, V. 6(4): pp. 683–694. Andrews, D.W.K. (1994). “Tests for Parameter Instability and Structural Change with Unknown Change Point”, Econometrica, V.61(4): pp. 821­856. Andrews, D.W.K. and Ploberger, W. (1994). “Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative,” Econometrica, V. 62 (6): pp. 1383­1414. Benassy­Quere, A., Mignon V., and Penot A. (2007). “China and the Relationship between Oil Prices and the Dollar”, Energy Policy, V. 35(11): pp. 5795­5805. Birkner, C. (2009). “Energy Outlook; Crude Correlations and What Comes Next”, futuresMag.com, pp. 240­29. Blas, J. (2010). “Seasonal Oil Demand Shifting”. Financial Times (US Edition), August 20, pp. 11. Brown S., Virmani R., and Alm R. (2008). “Crude Awakening: Behind the Surge in Oil Prices”, FRB Dallas Economic Letter V.3(5): pp. 1­9. Chaudhuri, K. and Daniel, B.C. (1998). “Long­run equilibrium real exchange rates and oil prices”, Economics Letters, V. 58 (2): pp. 231–238. Coudert, V. Mignon, V., and Penot, A. (2007) "Oil Price and the Dollar," Energy Studies Review: V. 15(2), Article 3. Golub, S. (1983). “Oil Prices and Exchange Rates”, The Economic Journal, V. 93(371): pp. 576­593. Granger, C.W.J. (1969). “Investigating Causal Relations by Econometric Models and Cross­Spectral Methods”, Econometrica, V. 37(3): pp. 424­38. Halligan, L. (2009). “Oil Prices will be driven upward by the needs of Developing Nations”, The Daily Telegraph, June 13. Hamilton, J. (2009). “Causes and Consequences of The Oil Shock of 2007­2008”, UCSD Working Paper. Hanke, S. H. (2008a). “Weak Dollar and US Petroleum Reserves Behind Strong Oil Price”, Globe Asia, June 25. Hanke, S. H. (2008b). "The Greenback and Commodity Prices", Globe Asia, August 19. Hansen, B.E. (1997). “Approximate Asymptotic P Values for Structural­Change Tests”, Journal of Business and Economic Statistics, V.15(1): pp. 60­67. Higgins, M., Klitgaard, T., and Lerman, R. (2006). “Recycling Petrodollars”, FRBNY’s Current Issues in Economics and Finance: V.12(9): December. Krugman, P. (1983a), Oil and the dollar, in Bhandari, J. and Putnam, B. (eds), Economic Interdependence and Flexible Exchange Rates, MIT Press: Cambridge, MA. Krugman, P. (1983b), Oil shocks and exchange rate dynamics in Frankel, J.A. (ed.), Exchange Rates and International Macroeconomics, University of Chicago Press: Chicago. Leung, G.C.K (2010). China’s Oil Use, 1990­2008, Energy Policy, V. 38(2): pp. 932­944 Medlock, K. and Jaffe, A. (2009). “Who is in the Futures Market and How Has It Changed?”, James Baker Institute for Public Policy: Rice University. Zhang, Y., Fan, Y., Tsai, H., and Wei, Y. (2008). Spillover effect of US dollar exchange rate on oil prices, Journal of Policy Modeling, V. 30(6): pp. 973­991.
Journal of Financial and Economic Practice Page 17 Journal of Financial and Economic Practice Page 18