Dynamics of oil price, precious metal prices, and exchange rate

Energy Economics 32 (2010) 351–362

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Energy Economics j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / e n e c o

Dynamics of oil price, precious metal prices, and exchange rate Ramazan Sari a, Shawkat Hammoudeh b, Ugur Soytas a,⁎ a b

Middle East Technical Univ., Dep. of Bus. Admin., 06531 Ankara, Turkey Drexel University, LeBow College of Business, Philadelphia, PA 19104, USA

a r t i c l e

i n f o

Article history: Received 3 April 2008 Received in revised form 10 February 2009 Accepted 25 August 2009 Available online 1 September 2009 JEL classification: E32 O13 Q30 Q43

a b s t r a c t This study examines the co-movements and information transmission among the spot prices of four precious metals (gold, silver, platinum, and palladium), oil price, and the US dollar/euro exchange rate. We find evidence of a weak long-run equilibrium relationship but strong feedbacks in the short run. The spot precious metal markets respond significantly (but temporarily) to a shock in any of the prices of the other metal prices and the exchange rate. Furthermore, we discover some evidence of market overreactions in the palladium and platinum cases as well as in the exchange rate market. In conclusion, whether there are overreactions and re-adjustments or not, investors may diversify at least a portion of the risk away by investing in precious metals, oil, and the euro. Policy implications are provided. © 2009 Elsevier B.V. All rights reserved.

Keywords: Precious metal prices Oil prices ARDL Generalized variance decompositions Generalized impulse responses

1. Introduction Precious metals — gold, palladium, platinum and silver —, oil and the exchange rate have received much attention recently from investors, traders, policy-makers and producers, partly because of the recent flare up in their prices, increases in their economic uses and synchronization of their movements.1 Their relationships have even attracted the attention of lay persons. Among the major precious metal class, increases in gold prices seem to lead to parallel movements in the prices of the other precious metals which are also considered investment assets as well as industrial commodities. Using gold as a hedge against increasing risk in the financial markets sparked a movement towards utilizing other precious metals as risk management tools in hedging and diversifying commodity portfolios. An environment of high inflation and inflationary expectation build up invokes tendencies towards using precious metals as safe havens to avoid risk. The increase in industrial use of precious metals have also generated substitution among close metal cousins such as platinum and palladium, which has made the prices of

⁎ Corresponding author. Tel.: +90 312 210 2048; fax: +90 312 2107962. E-mail address: [email protected] (U. Soytas). 1 It is estimated that 80% of the world's platinum supply comes from South Africa, whereas Russia is the top producer of palladium. China seems to have overtaken South Africa as the No. 1 producer of gold. Mexico and Poland are the largest producers of silver. 0140-9883/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.eneco.2009.08.010

these precious metals play catch up against each other. If we examine price returns, how are the precious metals related to each other? Does gold lead in this group? Gold and silver are both used in the jewelry industry and also traded as investment assets but silver is more commodity-driven than gold as its monetary element has gradually been replaced. The platinum price often moves due solely to movements in the prices of other precious metals, in particular gold. Gold and platinum seem to be in lock-step recently, while silver and palladium have moved more closely together. Palladium is a poor relative to platinum but plays catch up with it because both are used in the auto industry.2 Are there price return transmissions among these precious metals and oil in the short run and/or the long run? Neither the empirical literature nor economic theory has given us enough information about the directional relationships among the major precious metals, whether they have a leader or a driver, and how they are related to other variables such as oil. Many economists would consider gold as the leader of the precious metal pack. But silver has more industrial uses than gold and has sometimes led gold. Would the industrial platinum and palladium relate more to silver or to gold? Or would the inclusion of gold among the foreign reserves dominate the industrial uses of the precious metals in the price leadership process? It would be useful and interesting to have answers to these questions, particularly when price returns are used.

2

Palladium jewelry also competes with platinum jewelry in China.

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The race among the precious metals has also been accompanied by what seems to be a parallel and related race in the oil market. Oil and the precious metals are commodities that are priced in US dollar and are included in the commodity portfolios of most serious individual and institutional investors. These commodities can be related because they are used as hedges by investors and traders, moving from dollardenominated soft assets such as stocks to dollar-denominated physical assets such as oil and the precious metals. Moreover, increases in oil prices may lead to power shortages which affect the production of precious metals as is the case in South Africa. Then what is the relationship between price returns of oil and precious metals, particularly gold? Is there a directional relationship between precious metals, particularly gold, and oil? Is the relationship between their price returns weak or strong? Who drives who? Does gold, being part of foreign reserves and is highly sensitive to crises, lead oil in the presence of other precious metals, and not vice versa? This question is important because oil and gold are the most widely traded commodities. Investors switch between oil and precious metals or combine them in diversified portfolios. Beahm (2008) believes that the price relationship between gold and oil is one of the five fundamentals that drive the prices of precious metals, particularly gold.3 In this study, we are eager to know how oil price return is related to those of all major precious metals, and not just gold. The dollar exchange rate may also co-drive both oil and the precious metals simultaneously partly because they are denominated in the dollar currency. Investors move from dollar-denominated soft assets to dollar-denominated physical assets during expected inflation. Is the exchange rate the one that greases the movements of oil and precious metals? It is well known that investors use precious metals, particularly the yellow metal, as a safe haven in their flight to safety when the green currency weakens against the other major currencies, especially euro. However, it has become more evident recently that a deteriorating dollar against the euro can also push up oil prices as they are priced in the green currency.4 Therefore, it will be informative and useful to traders, investors and policy makers to understand the dynamics and the relationships among the major precious metals when they play catch up with each other and when they have feed back relationships with oil and the exchange rate. Oil and precious metal markets focus on the relationship between US dollar and euro because both are the world's major reserve currencies. Lucey and Tully (2006) find gold to be particularly sensitive to the dollar/euro exchange rate. Moreover, most of the imports and exports of oil and precious metals producers come from the United States and the euro land. Thus, changes in this particular exchange rate feed back to the prices of these commodities. Therefore, the objective of this study is to examine the directional relationships between the spot prices of the major precious metals, oil price, and euro-US dollar exchange rate. It also includes examining who drives who in the long run? Surprisingly, the study finds that the presence of a long-run equilibrium relationship among the variables is rather weak. However, the spot commodity markets seem to respond significantly (but temporarily) to shocks in any one of the prices. Furthermore, we also discover some evidence of market overreactions in the palladium and platinum cases as well as in the exchange rate market. The paper is organized as follows. Section 2 discusses the existing literature. Section 3 describes the data and the methodology. In Section 4 the empirical results are presented, and the last section concludes. 2. A review of the literature Upon examination of the literature, one can discern three major areas of research on commodities that are relevant to this study: price co3

http ://www.mi neweb.com/mineweb/view/mineweb/en/page33?oid= 47700&sn=Detail. 4 The literature on the relation between real oil price and real exchange demonstrates that real oil price is dominant (see for example Amano and van Norden, 1998).

movement, information transmission in the presence of economic fundamentals, and non linear dependence between special metals and whether nonlinearities are consistent with chaos. Pindyck and Rotemberg (1990) in one of the early studies on commodities price co-movement find excess co-movement among seven major commodities that are unrelated. The excess co-movement is attributed to irrational or herding behavior that goes beyond common macroeconomic factors. The Pindyck and Rotemberg study did not focus on a particular group of commodities and does not answer the questions raised in the Introduction about the movements and leadership among precious metals, oil and exchange rate. However, it stimulated further research to examine the co-movement of related and unrelated commodities. Other studies found less “excessive” co-movement in prices of commodities (see Palaskas and Varangis (1991), Palaskas (1993), Trivedi (1995), and Deb et al. (1996)). Using the concordance measures, Cashin et al. (1999) analyze the veracity of the comovements of prices of 17 related and unrelated commodities. In particular, they find no evidence of such co-movement in the prices of the seven unrelated commodities studied by Pindyck and Rotemberg (1990). They do, however, find strong evidence of co-movement in the prices of related commodities. On the other hand, Ciner (2001) finds that the stable long-run relationship between gold and silver on the Tokyo Commodity Exchange disappeared in the 1990s, and more recently they have their own separate markets as they are considered to have different economic uses. However, more recently Lucey and Tully (2006) suggest that while there are periods when the relationship between those two precious metals is weak, overall a stable relationship prevails. In the second area of information transmission, Pindyck and Rotemberg (1990) find unidirectional causality from macroeconomic variables to commodity prices. More recently, Awokuse and Yang (2003),5 among others, find that commodities as represented by the CRB (Commodity Research Bureau) index have informational content on predicting the direction of inflation, interest rate and industrial production.6 Others such as Christiano et al. (1996) argue that commodity prices are set in continuous auction markets with efficient information, and thus they can be early indicators of macroeconomic activity and should be used in monetary VARs. Baffes (2007) uses annual data from 1960 to 2005 to examine the crude oil price pass through to 35 other commodities, including gold and silver. Controlling for the inflationary effects, he estimates that the pass through in precious metals is rather high. He further argues that since oil can be viewed as entering the production functions of other commodities, or as an alternative that enters the substitutability conditions, the Granger causality can be interpreted as an economical relationship. This economical relationship seems to hold rather strongly between oil prices and prices of gold and silver. Generally, Baffes (2007) finds the prices of precious metals exhibiting a strong response to the crude oil price. In terms of nonlinearity and chaotic structure, Yang and Brorsen (1993) conclude that palladium, platinum, copper and gold futures have chaotic structures. In contrast, Adrangi and Chatrath (2002) find that the nonlinearity in palladium and platinum is inconsistent with chaotic behavior. They conclude that ARCH-type models with controls for seasonality and contractibility can explain the nonlinear dependence in their data. Plourde and Watkins (1998) compare the volatility in the prices of nine non-oil commodities (including gold and silver) to the volatility in oil prices. Utilizing several nonparametric and robust parametric tests, they find that the oil price tends to be more volatile than the prices of gold, silver, tin and wheat. They argue that the differences stand out more in the case of precious metals. Nevertheless, they do not view oil price changes as outliers,

5 For other studies that do not agree with Awokuse and Yang (2003) see Marquis and Cunningham (1990); Cody and Mills (1991), and Hua (1998), among others. 6 The CRB arithmetically averages 19 commodities which include our four strategic commodities. This index suffers from aggregate bias because it includes both related and unrelated commodities.

R. Sari et al. / Energy Economics 32 (2010) 351–362

but argue that they are bounded by changes in the prices of other commodities. Our study lines more with price co-movements, price drivers and information transmissions. However, it is more focused and timelier than the previous studies because it concentrates on the movements of the four major precious metals’ spot prices which have been in the spot light. It follows their movements in playing catch up to each other in response to variations to both investor and industry demands. It examines the long-run drivers behind each metal and its responses to shocks and how these metals react to changes in both oil prices and dollar/euro exchange rate. This makes our results and conclusions more relevant for traders, investors and policy-makers. By including the dollar/euro exchange rate and oil with the precious metals, this paper also takes into account the current thinking that this exchange rate takes a center place in the precious metals and oil world. 3. Data and Methods We utilized daily time series data (five working days per week) for the four precious commodity closing spot prices (gold, silver, platinum and palladium), oil spot prices and USD/euro exchange rate. The exchange rate is the value of the US dollar to one euro and thus a rise in this rate implies a devaluation of the dollar. The crude oil price is for the benchmark West Texas Intermediate (WTI) delivered at the end of the pipeline at the Cushing, Oklahoma center and it is expressed in US dollars per barrel. The gold spot is traded at COMEX in New York and its price (GOLD) is measured in US dollars per troy ounce and the silver spot is also traded at COMEX and its price (SLVR) is given in US dollars per troy ounce.7 Palladium and Platinum are also traded at COMEX and their prices, PAL and PLAT, are dollars per ounce. We use dummy variables for the establishment of the oil price band by OPEC in 2000, the 911 New York City attack, and the 2003 Iraq war. We examine daily data for the period between 1/4/1999 and 10/19/ 2007. All series are modeled in natural logarithms. The descriptive statistics of all logged and raw data are reported in Tables 1 and 2, respectively. The coefficient of variation indicates that the poor relative palladium and the oil price are the two commodities having the highest volatility. As indicated above, palladium was active during crisis times. Gold has the lowest volatility amongst all the commodities in this study, which is consistent with the fact that gold has a monetary component, and a good portion of its demand goes to hoarding and of its supply comes from recycling. The correlations between the variables are reported in Table 3. As expected, gold and silver have the highest positive correlation, reflecting their jewelry and monetary elements. Among all precious metals, platinum has the highest positive correlation with oil, expressing their importance in industrial uses. Palladium (the poor cousin) has a negative linear association with all other variables under investigation including its rich cousin platinum despite the recent catching up in prices, making it a good possible diversification hedge in the short run. Methodologically, we employ the generalized forecast error variance decompositions and the generalized impulse response functions of Koop et al. (1996) and Pesaran and Shin (1998) to understand the impacts and responses to shocks. The generalized variance decomposition and generalized impulse response approaches have advantages over the orthogonalized approach of Sims (1980). The results of orthogonalized approach are sensitive to the order of variables in the VAR in contrast to the results generated from generalized approach which do not vary according to the ordering. Forecast error variance decompositions (VDC) show how much of the variance of a variable can be explained by shocks to another variable in the same system of simultaneous equations known as the vector autoregressive model (VAR). Unexpected

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innovations to an individual variable can affect both “changes in itself” and the other variables. In a VAR system, the relative importance of these effects can be identified by the forecast error variance decompositions. For this reason, the variance decomposition method is considered to be an out-of-sample causality analysis. On the other hand, the impulse responses trace out the direction of the dynamic responses of a variable to innovations in other variables in the VAR. Both generalized impulse response functions and generalized forecast error variance decompositions are based on the estimation of the moving-average representation of the original VAR (Pesaran and Pesaran, 1997). Before running the generalized methods, it should be decided whether to use first-differenced data or levels. Engle and Granger (1987) emphasized the importance of this issue. If the variables are cointegrated and the corresponding cointegration vector is not used in the VAR system, the model with only first-differenced data will be misspecified. We use both the method developed by Johansen (1991, 1995) and Johansen and Juselius (1990) (JJ, thereafter) and the bounds testing approach (Pesaran et al., 2001) to check for cointegration. The bounds testing approach has several advantages over the JJ method. First, The JJ approach requires that all the series must be I(1), while the bounds testing approach does not require the same order of integration. Second, with the bounds testing approach it is possible to determine more efficient cointegrating relationship(s) even if the sample size is very small (Ghatak and Siddiki, 2001). Finally, the bounds testing approach overcomes the problems resulting from series with unit roots (Laurenceson and Chai, 2003). The important advantage of JJ over bounds testing approach is that the JJ can be applied to I(2) or higher series as long as the series are having same order of integration. To utilize the bounds testing approach, it is necessary to model the variable relationship using the Autoregressive Distributed Lag (ARDL) technique developed by Pesaran and Pesaran (1997) and Pesaran et al. (2001). The bounds testing procedure requires the estimation of the following equations. k X

ΔLWTISt = a0iW + +

k X

k X

biW ΔLWTISt − i +

i=1

eiW ΔLSLVRt − i +

i=1

k X

ciW ΔLPALt − i +

i=1

fiW ΔLPLAT t − i +

i=1

k X

k X

diW ΔLGOLDt − i

i=1

giW ΔLERt − i

i=1

+ λ1W LWTISt − 1 + λ2W LPALt − 1 + λ3W LGOLDt − 1

ð1Þ

+ λ4W LSLVRt − 1 + λ5W LERt − 1 + e1τ

ΔLPALτ = a0iP +

l X

l X

biP ΔLWTISt − i +

i=1

+

l X

diP ΔLGOLDt − i +

i=1

+

l X

ciP ΔLPALt − i

i=1 l X

eiP ΔLSLVRt − i

i=1

fiP ΔLPLAT t − i +

i=1

l X

giP ΔLERt − i + λ1P LWTISt − 1

i=1

+ λ2P LPALt − 1 + λ3P LGOLDt − 1 + λ4P LSLVRt − 1 + λ5P LERt − 1 + e2t ΔLGOLDt = a0iG +

n X

ð2Þ

biG ΔLWTISt − i +

i=1

+

n X

+

n X

ciG ΔLPALt − i

i=1

diG ΔLGOLDt − i +

i=1

n X

n X

eiG ΔLSLVRt − i

i=1

fiG ΔLPLAT t − i +

i=1

n X

giG ΔLERt − i + λ1G LWTISt − 1

i=1

+ λ2G LPALt − 1 + λ3G LGOLDt − 1 + λ4G LSLVRt − 1

7

Price of silver is usually quoted in cents per troy ounce but we transformed it into dollars per troy ounce.

+ λ5G LERt − 1 + e3t

ð3Þ

354

R. Sari et al. / Energy Economics 32 (2010) 351–362

Table 1 Descriptive statistics (Log).

Mean Std. dev. Skewness Kurtosis Jarque-Bera Probability Observations

LWTI

LGOLD

LPAL

LPLAT

LSLVR

LER

3.587 0.447 − 0.000 2.242 54.951 0.000 2295

5.933 0.315 0.596 2.079 216.845 0.000 2295

5.799 0.439 0.597 2.845 138.843 0.000 2295

6.547 0.381 − 0.016 2.009 93.835 0.000 2295

1.843 0.377 0.929 2.477 356.069 0.000 2295

0.096 0.149 − 0.237 1.700 182.942 0.000 2295

Note: All variables are in logarithmic form.

Table 2 Descriptive statistics (level).

Mean Std. dev. Coef. of variation Skewness Kurtosis Jarque-Bera Probability Observations

WTI

GOLD

PAL

PLAT

SLVR

ER

39.870 17.737 0.445 0.629 2.154 219.931 0.000 2295

397.648 135.99 0.342 0.939 2.624 351.064 0.000 2295

366.374 186.480 0.509 1.601 5.306 1488.949 0.000 2295

748.948 282.472 0.377 0.527 2.210 165.960 0.000 2295

6.838 2.997 0.438 1.260 3.148 609.154 0.000 2295

1.112 0.162 0.146 − 0.091 1.686 169.409 0.000 2295

Note: we provide the data in levels to put the absolute USD values of the prices in perspective. PAL stands for palladium, PLAT for platinum and ER for the dollar/euro exchange rate.

Table 3 Correlation matrix.

LWTI LGOLD LPAL LPLAT LSLVR LER

LWTI

LGOLD

LPAL

LPLAT

LSLVR

LER

1.0000 0.8854 − 0.2774 0.9413 0.8351 0.7107

1.0000 − 0.4054 0.9310 0.9478 0.8609

1.0000 − 0.3246 − 0.2066 − 0.6431

1.0000 0.8491 0.7642

1.0000 0.8040

1.0000

Notes: All variables are expressed in logarithmic form. PAL stands for palladium, PLAT for platinum and ER for the dollar/euro exchange rate.

Table 4 Unit root test results. ADF Levels Intercept

Intercept and trend

First differences Intercept

Intercept and trend

LER LGOLD LPAL LPLAT LSLVR LWTI LER LGOLD LPAL LPLAT LSLVR LWTI

DLER DLGOLD DLPAL DLPLAT DLSLVR DLWTI DLER DLGOLD DLPAL DLPLAT DLSLVR DLWTI

− 0.188454 (0) 0.686800 (0) − 1.220886 (1) − 0.627545 (0) − 0.141359 (0) − 1.588878 (0) − 2.988340 (0) − 2.488783 (0) − 1.309324 (1) − 2.812025 (0) − 1.978731 (0) − 3.170958c (0)

− 47.70126a (0) − 47.91507a (0) − 44.32655a (0) − 48.43416a (0) − 50.50606a (0) − 47.93264a (0) − 47.77596a (0) − 47.95794a (0) − 44.31740a (0) − 48.42400a (0) − 50.52898a (0) − 47.92427a (0)

DFGLS − 0.458648 (0) 1.868169 (0) − 1.225039 (1) 1.503382 (0) 0.574335 (0) 1.006362 (0) − 0.595844 (0) − 1.043174 (0) − 1.224994 (1) − 2.821300c (0) − 1.290414 (0) − 2.113127 (0)

− 2.936833a (18) − 47.83632a (0) − 1.733300c (16) − 1.271600 (19) − 1.429612 (18) − 1.735163c (17) − 11.48160a (7) − 47.94001a (0) − 3.553291a (16) − 3.122213b (0) − 2.945449b (18) − 3.632105a (17)

PP

KPSS

NPZa

− 0.199161 0.590642 − 1.260423 − 0.559102 − 0.060327 − 1.510101 − 2.988244 − 2.591901 − 1.327227 − 2.800564 − 1.915335 − 2.990374

4.336871a 5.411291a 2.320234a 5.355907a 4.524586a 4.973777a 0.654257a 1.030724a 0.724791a 0.274188a 1.290088a 0.478456a

− 0.77935 (0) 2.12674 (0) − 3.01184 (1) 1.31144 (0) 1.00009 (0) 0.98481 (0) − 1.01623 (0) − 3.02501 (0) − 3.16736 (1) − 15.9967c (0) − 3.88091 (0) − 8.90007 (0)

− 47.70176a − 47.96723a − 44.41937a − 48.56252a − 50.46752a − 48.17850a − 47.77590a − 47.99540a − 44.41026a − 48.55177a − 50.51230a − 48.17182a

0.501256b 0.239598 0.167505 0.041955 0.200342 0.070076 0.157375b 0.016816 0.157018b 0.036867 0.026297 0.067950

− 7.43366c (18) − 1164.13a (0) − 4.35679 (16) − 4.56338 (19) − 2.07658 (18) − 4.07551 (17) − 68.0548a (7) − 1156.62a (0) − 10.2335 (16) − 26.2719a (17) − 7.49484 (18) − 7.65036 (17)

Notes: D and L are first difference and natural log operators, respectively. Superscripts a, b, and c represent significance at 1%, 5%, and 10% levels, respectively. Lag lengths are determined via SIC and are in parentheses. PAL stands for palladium, PLAT for platinum and ER for the dollar/euro exchange rate.

R. Sari et al. / Energy Economics 32 (2010) 351–362

ΔLSLVRt = a0iS +

r X

r X

biS ΔLWTISt − i +

i=1

+

r X

diS ΔLGOLDt − i +

i=1

+

r X

ciS ΔLPALt − i

ð4Þ

i=1 r X

eiS ΔLSLVRt − i

i=1

fiS ΔLPLAT t − i +

i=1

r X

giS ΔLERt − i + λ1S LWTISt − 1

i=1

+ λ5S LERt − 1 + e4t

+

v X

v X i=1

+

ciPL ΔLPALt − i

ð5Þ

i=1

v X

diPL ΔLGOLDt − i +

i=1 v X

v X

biPL ΔLWTISt − i +

eiPL ΔLSLVRt − i

fiPL ΔLPLAT t − 1 +

i=l

i−1

giPL ΔLERt − i + λ1PL LWTISt − 1

+ λ4PL LSLVRt − 1 + λ5PL LERt − 1 + e5t z X

z X

biT ΔLWTISt − i +

i=1

+ +

z X i=1 z X

fiT ΔLPLAT t − i +

i=1

ciT ΔLPALt − i

ð6Þ

i=1

diT ΔLGOLDt − i +

z X

λtrace

5% C.V.

λmax

5% C.V.

70.12080 37.81732 16.01006 4.620079 1.408690

69.81889 47.85613 29.79707 15.49471 3.841466

32.30349 21.80726 11.38998 3.211389 1.408690

33.87687 27.58434 21.13162 14.26460 3.841466

rephrased from Cheung and Yuen (2002) and based on Koop et al. (1996), Pesaran and Pesaran (1997), and Pesaran and Shin (1998). Consider that Ht can be represented by the following VAR: 1 X

/Ht − i + et

ð7Þ

i

+ λ2PL LPALt − 1 + λ3PL LGOLDt − 1

ΔLERt = a0iT +

H0 r=0 r≤1 r≤2 r≤3 r≤4

Ht = α +

i=1 v X

Table 6 Johansen–Juselius multivariate cointegration test results.

Notes: C.V. denotes critical values. λtrace and λmax are the test statistics used to determine the existence of cointegration and, specifically, the number of cointegrating vectors.

+ λ2S LPALt − 1 + λ3S LGOLDt − 1 + λ4S LSLVRt − 1

ΔLPLAT t = a0iPL +

355

eiT ΔLSLVRt − 1

i=1 z X

where Ht is a m × 1 vector of jointly determined endogenous variables, α is a vector of constant, ϕ1 through ϕp are m × m matrices of coefficients to be estimated, and εt is an m × 1 vector well-behaved disturbances with covariance Σ = σ ij. The generalized impulse response of Ht + n with respect to a unit standard deviation shock to j-th variable at time t is represented by (MnΣej)(σij)− 1, where Mn = ϕ1Mn − 1 + ϕ2Mn − 2 +…+ ϕpMn − p, n = 1,2,…, M0 = I, Mn = 0 for n b 0, and ej is m × 1 selection vector with unity as its j-th element and zero elsewhere. Then, generalized forecast error variance decompositions can be computed by −1

σ ij

giT ΔLERt − i + λ1T LWTISt − 1

2 n  P V ei Mi Σej

i=0

n  P

i=1

i=0

+ λ2T LPALt − 1 + λ3T LGOLDt − 1 + λ4T LSLVRt − 1 + λ5T LERt − 1 + e6t

0

eiVMi ΣBMi ei

ð8Þ

2

Table 7 Generalized forecast error variance decomposition.

where k, l, r, n, v and z are the lag lengths and determined by the Akaika Information Criterion (AIC). b, c, d, e, f and g denote the shortrun coefficients, while λs are the long-run coefficients. The null hypothesis of “no cointegration” in the long run in each equation is that λ1 = λ2 = λ3 = λ4 = λ5 = λ6 = 0. The general F-statistics are calculated and compared with two different critical values obtained from Pesaran et al. (2001). Depending on the time series properties of the series, two sets of critical values are reported. One set is for the purely I(1) series, and the other one is for the purely I(0) series. If all the series are either I(1) or I(0), we fail to reject the null hypothesis if the calculated F-statistic is lower than the critical values. On the other hand, if the series have different ordering, the test is inconclusive if the F-statistic is between both critical values. If the calculated statistic is greater than the critical value for I(1) series, we conclude cointegration. The following brief technical discussion for generalized variance decomposition and generalized impulse responses approach is Table 5 Bounds-testing cointegration procedure results.

Dependent variable

Horizon DLWTI

DLGOLD DLPAL

DLPLAT

DLSLVR

DLER

DLWTI

0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4

0.00750 0.00822 0.00873 0.00872 0.00872 1.00000 0.88224 0.88112 0.88100 0.88099 0.06546 0.06476 0.06635 0.06634 0.06634 0.10566 0.10487 0.10491 0.10491 0.10491 0.15940 0.16168 0.16149 0.16145 0.16145 0.09383 0.09376 0.09481 0.09478 0.09478

0.00858 0.00986 0.00985 0.00992 0.00992 0.10566 0.10103 0.10092 0.10105 0.10105 0.21960 0.21837 0.22009 0.22010 0.22011 1.00000 0.98852 0.98658 0.98654 0.98653 0.09921 0.09873 0.09891 0.09890 0.09890 0.02338 0.02343 0.02351 0.02354 0.02354

0.02076 0.02082 0.02080 0.02095 0.02095 0.15940 0.23238 0.23209 0.23208 0.23208 0.10374 0.10810 0.10882 0.10898 0.10898 0.09921 0.10296 0.10318 0.10320 0.10320 1.00000 0.99752 0.99599 0.99594 0.99594 0.07145 0.07175 0.07168 0.07196 0.07196

0.00069 0.00082 0.00082 0.00088 0.00088 0.09383 0.09296 0.09340 0.09339 0.09339 0.02309 0.02287 0.02331 0.02332 0.02332 0.02338 0.02396 0.02651 0.02651 0.02652 0.07145 0.07106 0.07158 0.07161 0.07161 1.00000 0.99902 0.99649 0.99616 0.99615

DLGOLD

DLPAL

DLPLAT

DLSLVR

Cointegration hypotheses

F-statistics

Lags

F(LWTISt|LGOLDt, LPALt, LPLATt, LSLVRt, LERt) F(LGOLDt|LWTISt, LPALt, LPLATt, LSLVRt, LERt) F(LPALt|LWTISt, LGOLDt, LPLATt, LSLVRt, LERt) F(LPLATt|LWTISt, LGOLDt, LPALt, LSLVRt, LERt) F(LSLVRt|LWTISt, LGOLDt, LPALt, LPLATt, LERt) F(LERt|LWTISt, LGOLDt, LPALt, LPLATt, LSLVRt)

2.356058 2.724912 2.186114 2.706250 1.901947 2.675500

2 3 5 3 2 2

Notes: Critical values for the 1% significance level I(1) is 3.41 and I(0) is 4.68; for the 5% significance level I(1) is 2.62 and I(0) is 3.79; and for the 10% significance level I(1) is 2.26 and I(0) is 3.35. Critical values are from Pesaran, Shin and Smith (2001).

DLER

1.00000 0.99644 0.99582 0.99558 0.99558 0.00750 0.01689 0.01739 0.01739 0.01739 0.00171 0.00171 0.00187 0.00188 0.00188 0.00858 0.01063 0.01077 0.01078 0.01078 0.02076 0.02076 0.02073 0.02073 0.02073 0.00069 0.00083 0.00090 0.00093 0.00093

0.00171 0.00187 0.00188 0.00190 0.00190 0.06546 0.06269 0.06261 0.06264 0.06264 1.00000 0.99527 0.99356 0.99342 0.99341 0.21960 0.21828 0.21796 0.21796 0.21796 0.10374 0.10320 0.10309 0.10309 0.10309 0.02309 0.02319 0.02317 0.02317 0.02317

Notes: PAL stands for palladium, PLAT for platinum and ER for the dollar/euro exchange rate.

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Fig. 1. Generalized impulse response of DLWTI to 1 standard deviation innovations.

We specifically employ generalized forecast error variance decompositions and generalized impulse response to assess the relative strength of precious metals spot prices, oil spot prices and exchange rate, in the price and exchange rate equations and the transmission mechanism between the variables under investigation. 4. Empirical results Before testing for the presence of cointegration, we have to determine the time series properties of the variables. Dickey and Fuller (1979) (ADF), and Phillips and Perron (1988) (PP) tests are the most commonly used methods to test for unit roots. However, it has been reported that both of these methods have weaknesses so new techniques have been developed. Thus, in addition to the ADF and PP, we also utilized Dickey–Fuller GLS detrended (DF-GLS), Kwiatkowski et al. (1992) (KPSS), and Ng and Perron's (2001) MZα (NPZa) unit root tests. The results are reported in Table 4. The common suggestion of all the tests is that all variables are I(1), thus we can use both the bounds test and the JJ method to test for cointegration.

The bounds test results are reported in Table 5. The results suggest that there is no cointegration among the precious spot precious metals and oil prices and the exchange rate. That is, the oil prices, precious metal prices, and exchange rates are not collectively driving forces of each other in the long run, despite their strong correlations among the precious metals in the short run. The JJ results are reported in Table 6 and they also confirm that no evidence of cointegration is detected by the maximum eigenvalue test. However, the trace test (λtrace) suggested the presence of cointegration. The test results seem to be conflicting. Since scholars generally prefer the maximum eigenvalue test (λmax) over the trace test,8 and considering the result of the bounds testing approach, we assume that the variables are not cointegrated. Thus, we used the first differences of the data series in the VAR to estimate the generalized-forecast error variance decompositions and generalized impulse response functions.

8

See Enders (1995), page 393.

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357

Fig. 2. Generalized impulse response of DLGOLD to 1 standard deviation innovations.

Table 7 reports the results of the generalized forecast error variance decompositions for all the variables. The results clearly suggest that most of the variations in each of the four precious metals' prices, oil price and the exchange rate are due to own innovation. Among the four precious metals, there is a relatively stronger bidirectional relationship between the oil price return and that of silver, with each explaining more than 2% of each other's variation. Silver is highly volatile like oil and also has industrial uses in the auto industry which consumes oil. On the other hand, the relationship between the oil price return and that of gold is very weak and asymmetric. Basically, gold return does not explain much of the oil price return partly because gold is the least volatile of the precious metal class, while oil is a very volatile commodity. Gold is a reserve currency, a safe haven and the number one choice for jewelry. Moreover, oil and gold have different hedging strategies. The reverse relationship between oil and gold is somewhat stronger as oil explains 1.7% of gold price returns. When oil price changes, it may move because of inflation, crises, changes in exchange rate and/or recession. All these factors affect gold which behaves as a safe haven and a hedge against inflation. But when gold price changes because of changes in

demand for jewelry, being hoarded as a reserve currency and/or being used as an investment asset, the relationship with oil return is tenuous at best. The relationships are also very weak between oil price return, on one hand, and each of palladium and platinum price returns, on the other hand.9 In particular; the variation in the oil price, explained by these two industrial precious metals, is much less than 1%. Nearly none of the variance of palladium price and only 1% of the variation in platinum price is explained by the oil price return. Another possible explanation for the overall weak relationship between oil price return and the four precious metal price returns is because oil is more than just commodity-driven whose price is simply determined by competitive market conditions and hedging strategies. The oil prices are regularly managed by OPEC, heavily stockpiled and distinctly subjected to special speculative attacks due to seasonality and weather conditions. The weak relationship thus reflects the increasing

9 The relationships between these commodities are weaker when price returns are used instead of price levels.

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Fig. 3. Generalized impulse response of DLPAL to 1 standard deviation innovations.

disparity in economic, monetary and hedging uses among oil and precious metals. The results further reveal that the changes in exchange rate and oil price return do not have considerable linkages with each other, neither in the short run nor the long run. This is surprising. However, the relationship should be stronger when price levels and not price returns are used. In contrast to the weak relationships between oil, and the precious metals and exchange rate, the generalized variance decomposition reveals that there is a strong relationship between gold price return, on one hand, and the other precious metals' price returns and changes in the exchange rate, on the other. The impact of gold is the highest on silver, explaining approximately 16% of the variation in silver. This finding support the results reached by Lucey and Tully (2006) more those reported by Ciner (2001). Gold's second impact is on platinum where it explains 10% of its variation, followed by the impacts on the exchange rate (9.5%) and palladium (7%). Platinum is used as an investment asset in certain portfolios at certain times. Silver can explain approximately 23% of the variance in the gold price in the long run. In contrast to gold, silver's explanatory power of

the variations in platinum and palladium is almost the same (10%). Silver is more of a commodity than jewelry or an investment asset and platinum and palladium are mainly industrial metals. Its impact on the exchanges rate is less than that of gold (7% vs. 9%). The impact is stronger on gold and silver prices because investors sell the dollar and buy these two safe haven metals in asset portfolios, particularly gold, when the dollar is expected to weaken. This is a flight to the safety of gold and silver. The impacts of these precious metals on the exchange rates are almost the same as the impact of exchange rates on them. In addition to the strong feedback relationship between gold and silver, there is another strong mirror–image relationship between the poor and rich industrial relatives: palladium and platinum. Their mirror impact of each other is nearly 22%. This is not surprising because palladium plays catch up with its rich cousin and they share similar industrial uses. The platinum impact on gold is higher than that of palladium (10% vs.7%), while their impacts on silver are very close (9% for platinum vs. 10% for palladium). It is also interesting to note that their impacts on the exchange rate are the same (2.5%). This is much less than the impacts of gold and silver because the two industrial precious metals are not widely used as safe haven

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359

Fig. 4. Generalized impulse response of DLPLAT to 1 standard deviation innovations.

investment assets as gold and silver are. However, the significant impacts of those industrial cousins on the exchange rate should not be surprising as a result of their relatively thin trading and strong industrial uses. They both move in lock-step to gold and silver, and palladium responds to crises as the exchange rate does. The impulse response function results are plotted in Figs. 1–6. Fig. 1 reveals that the initial impacts of gold, silver, platinum and palladium prices on oil spot prices are positive and significant. These impacts die out quickly by the second horizon (day) in the case of gold, silver and platinum as the oil price returns rapidly to its steady state level. In case of palladium, the impact dies out almost instantly. As the following figures indicate, the impacts of oil price on the precious metals prices are almost the same as the impact of those commodities' prices on the oil price. Fig. 2 shows that the initial impacts on the gold price of shocks in other precious metal prices and exchange rate markets are positive and significant. In particular, the link between gold and silver spot markets seem to be in line with the Sari et al. (2007) results on futures markets for those two metals. The impact lasts for almost 3 days (the

longest among all), after which it dies out. It is worth noting that although we observed negative correlations between the price of palladium and all other prices above, as it can be observed from. Fig. 3, the palladium price initially responds positively to shocks in other variables. However, in the case of this precious metal, the responses to innovations in gold and platinum price changes are negative and significant in the third horizon,10 which may be implying an initial overreaction in the palladium market. The initial response of platinum to the unexpected shocks to the other commodity prices is positive and significant (see Fig. 4). However, in the third horizon, the responses to exchange rates are significantly negative. This may also be due to a market overreaction in response to an innovation in the exchange rate. The responses of silver prices to unexpected shocks in all the variables were plotted in Fig. 5. The initial impact of all variables on 10 Although this may not be easily noticed from the visual displays, the IR table shows a significantly negative result at the third horizon (not reported here, but available from the authors upon request).

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Fig. 5. Generalized impulse response of DLSLVR to 1 standard deviation innovations.

the silver price is positive and significant. However, the responses to the gold price in the second day and to the exchange rate in the fourth day are negative and significant. Again a plausible interpretation might be a re-adjustment to the equilibrium price after an overreaction to an unexpected shock. Fig. 6 indicates that the response of the exchange rate to the silver spot price is similar to the response of the silver price to the exchange rate. Like all the commodity prices, the initial response of the exchange rate to the innovations in all other variables is positive and significant. In the case of palladium and silver, the responses die out quickly by the second horizon, but by the fourth day the impact is once again positive and significant (a possible late reaction which should not occur in the absence of transaction costs). As a final step, The VAR for generalized impulse responses and variance decompositions are checked for stability.11 The VAR system

11 We thank the anonymous referee for bringing this issue to our attention. All the results are available upon request.

is stable in that all inverse roots of AR characteristic polynomial are within the unit circle. We, also, conducted the generalized analyses for the period before the war in Iraq and after it. The VAR systems for both sub-periods are found to be stable. We observed that before the war the precious metal prices returns did not have any significant impact on the oil price returns. On the other hand, the oil price responded significantly to a shock in any one of the precious metals after the war. However, the impact is not permanent and dies off pretty quickly as in the full sample analysis. 5. Conclusions This paper investigates the relationships between spot prices of precious metals and oil, and the US dollar/euro exchange rate. The latter is expected to be the link that relates all these industrial commodities because they are priced in the US dollar. The dollar/euro exchange rate is chosen because those two major currencies are interchangeably used in active portfolios that include precious metals

R. Sari et al. / Energy Economics 32 (2010) 351–362

361

Fig. 6. Generalized impulse response of DLER to 1 standard deviation innovations.

and oil. We find that there does not seem to be long-run equilibrium relationships between those spot price returns and changes in the exchange rate. This probably reflects the increasing disparity in economic, monetary and hedging uses between these commodities and exchange rates. It may also imply that those commodities may not be sensitive to common macroeconomic factors in the long run. Oil is controlled by OPEC and the other oil-producing countries, and has its own seasonality, inventories and hedging strategies. Gold and silvers have almost limited supplies, are considered safe haven assets and respond strongly to inflationary expectations. Since there is only a rather weak evidence of a long-run relationship, investors may benefit from diversification into the precious metals in the long run. Similarly, exporters of one of these commodities may benefit from expanding their exports (if at all possible) into the other precious metals if reserves are available, hence diversifying the risk of price fluctuations in the long run. However, there is evidence that spot precious metals' prices and exchange rate may be closely linked in the short run after shocks

occur. Henceforth, traders may benefit in the short run from the information content of an innovation in one spot market over the others in this regard. This is what has happened in the gold, silver, palladium and platinum and foreign exchange markets recently. However, the speculation window seems rather short and generally tends to die away within two days, with possible overreactions and re-adjustments also within two more days. These overreactions and late adjustments may be providing some support for the “herding behavior” assumption in those commodity markets in the short run, but the effect is short lived. This may in turn reflect that the varying behaviors of both irrational and rational speculators in those markets, with the latter group correcting the overreactions of the former within few days, attesting to the efficiencies of the markets, in the sense of correcting the possibility of abnormal profits. For precious metal traders, the results confirm the prevailing view that the rich and poor metals, platinum and palladium, are close industrial relatives because the former plays catch up to the latter, while gold and silver are their distant cousins. Platinum and

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palladium have information content to each other equivalent to the content between the rich and close relatives, gold and silver. This implies that under normal conditions the currently laggard palladium will continue playing catch up with platinum in a continuing or new bull commodity market as it is increasingly being sought in the same industries that demand platinum. Precious metal traders should consider a pick up in platinum prices as a precursor for an ensuing rise in the palladium price in a short period of time. For mineral traders in general, shocks in the precious metals and oil markets have mutual but small positive impact on each other. Among the precious metals, traders should watch for a rise in sliver price as a signal for higher oil price since silver is highly volatile like oil within days. The profitable opportunities are not only small but also short because they last for only a few days. These results support the efficiency of the markets. For monetary authorities, the precious metal price that those authorities should watch the most for its high information content regarding the daily behavior of the dollar/euro exchange rate is by far the price of gold followed by that of silver. In contrast, changes in the nominal price of oil have basically no information to provide to monetary authorities on changes in the exchange rate behavior, and the opposite holds as well. The recent daily coupling between the nominal exchange rate and the nominal oil and precious metal prices holds in logarithmic form and this is a “numeraire” interpretation because both are in dollars. It may conceptually be circumstantial due to the weakness of the US economy and the repeated drops in interest rates (i.e., common monetary shocks). This relationship is however not statistically significant if one uses an estimator that accounts for serial correlation (e.g., GLS). It does also hold in first differences in this paper as our results indicate no feedback relations. In the literature, the relationship in real terms depends on the terms of trade effect and a host of assumptions regarding home bias in consumption, capital/ labor ratios in the nontradable vs. tradable sector, complementarity of capital and labor with energy, etc.12 Acknowledgement The authors wish to thank three anonymous referees of this journal for their valuable comments. References Adrangi, B., Chatrath, A., 2002. The dynamics of palladium and platinum prices. Computational Economics 17, 179–197. Amano, R.A., van Norden, S., 1998. Oil prices and the rise and fall of the US real exchange rate. Journal of International Money and Finance 17, 299–316. Awokuse, T.O., Yang, J., 2003. The informational role of commodity prices in formulating monetary policy: a reexamination. Economics Letters 79, 219–224. Baffes, J., 2007. Oil spills on other commodities. Resources Policy 32, 126–134. Beahm, D., 2008. Five Fundamentals Will Drive Gold Price Higher in 2008. http://www. mineweb.com/mineweb/view/mineweb/en/page33?oid=47700&sn=Detail;= 47700&sn = Detail. Cashin, P., McDormott, C., Scott, A., 1999. The Myth of Co-Moving Commodity Prices. Discussion Paper # G99/9. Reserve Bank of New Zealand. Cheung, Y.W., Yuen, J., 2002. Effects of U.S. inflation on Hong Kong and Singapore. Journal of Comparative Economics 30, 603–619.

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