STUDY ON DYNAMIC RELATIONSHIP AMONG GOLD PRICE, OIL PRICE ...library.imtdubai.ac.ae/Faculty...

International Journal of Applied Business and Economic Research, Vol. 9, No. 2, (2011): 145-165

* Institute of Management Technology, Dubai International Academic City, UAE

STUDY ON DYNAMIC RELATIONSHIP AMONGGOLD PRICE, OIL PRICE, EXCHANGE RATE AND

STOCK MARKET RETURNS

K. S. Sujit1 and B. Rajesh Kumar2

Abstract: The dynamic and complex relationship among economic variables has attractedthe researchers, policy makers and business people alike. This study is an attempt to test thedynamic relationship among gold price, stock returns, exchange rate and oil price. All thesevariables have witnessed significant changes over time and hence, it is absolutely necessaryto validate the relationship periodically. This study takes daily data from 2nd January 1998to 5th June 2011, constituting 3485 observations. Using techniques of time series the studytried to capture dynamic and stable relationship among these variables using vectorautoregressive and cointegration technique. The results show that exchange rate is highlyaffected by changes in other variables. However, stock market has fewer roles in affectingthe exchange rate. In this study we tested two models and one model suggests that there isweak long term relationship among variables.

JEL classification: C22; E3;

Keywords: Unit root tests; granger causality test, Cointegration; Vector auto regression (VAR)

INTRODUCTION

Gold was one of the first metals humans excavated. Gold as an asset has a hybridnature: it is a commodity used in many industries but also it has maintainedthroughout history a unique function as a means of exchange and a store of value,which makes it akin to money. After World War II, the Bretton Woods systempegged the United States dollar to gold at a rate of US$35 per troy ounce. Thesystem existed until the 1971, when the US unilaterally suspended the directconvertibility of the United States dollar to gold and made the transition to a fiatcurrency system. The last currency to be divorced from gold was the Swiss Franc in2000.

In 1833 the price of gold was $20.65 per ounce, about $415 in 2005 terms, whilein 2005 the actual price of gold was $445 – a very small change in the real price ofgold over a period of one hundred and seventy two years3

In September 2001 the price of gold was as low as $257 and a downfall of twodecades had preceded it. In the early 80’s, the price of gold was over $800 for somedays and for almost 20 years the price of gold was in a stalemate. In December2005, gold broke the $500 barrier for the first time since 1982.

146 K. S. Sujit and B. Rajesh Kumar

In 2005, one ounce of gold can now buy only 7.7 barrels of crude oil. That’sthe least over the past 40 years - since the relationship between the prices of thetwo commodities was first noticed. The average ratio over the past 40 years was15.2 barrels of crude oil for every ounce of gold. Between 1975 and1980, when theOrganization of Petroleum Exporting Countries sharply increased the price ofcrude oil for the first time, an ounce of gold could buy just over eight barrels ofcrude.

As the dollar prolonged its decline in the aftermath of the 1973 breakdown ofdollar/gold convertibility, oil prices increased four-fold to nearly $12 per barrel in1974, triggering sharp run ups in U.S. gasoline prices and a subsequent halt inconsumer demand. Gold also pushed higher during the same period, gaining about15%.

A tumbling dollar and record oil prices were the main culprits in the 1980-82recession. The gold/oil ratio dropped from 15.3 in January 1979 to 11.4 in August1979 due to a doubling in oil to$29 per barrel and a more modest 30% increase ingold.

There was a temporary spike in the gold/oil ratio from 12.5 in autumn 1979 to21 in winter 1980. This was due to a $400 jump in gold from September 1979 toJanuary 1980 resulting from the Soviet Union’s invasion of Afghanistan.

In Autumn 1985, the gold/oil ratio bottomed at 10.6 after declining from a 16.9high in February 1983 amid relative stability in both the metal and the fuel,coinciding with a peaking fed funds rate of 8%.

Upon Iraq’s fateful invasion of Kuwait on Aug. 2, 1990, oil prices surged fromless than $21 per barrel to $31 per barrel in less than two weeks, before extending toa then-record $40 per barrel in October. The oil price jump dragged the gold/oilratio by 50% to a five-year low of 10.6 in less than three months.

In December 1998, oil prices plummeted due to OPEC’s decision to increasesupplies combined with the break of Asian oil demand amid the 1997-98 marketcrisis. OPFC’s miscalculation cut oil prices by more than half to $11 per barrel inDecember1998, their lowest since the glut of 1986. Once again, the recession waspredicted by the gold/oil ratio’s tumble to a nine year low of 1 M in 1999.

After the outbreak of the second Iraq War in March 2003, oil prices began theirmulti-year bull market, rising from $30 per barrel in March 2003 to more than $50per barrel in March 2005. Oil ended the year at $61 per barrel, up more than 100%over the prior two years compared to a 54% increase for gold over the same period.The oil price moves dragged the gold/oil ratio to 6.7 % in August 2005, its lowestlevel over the past 35-year history.

It is often stated that gold is the best preserving purchasing power in the longrun. Gold also provides high liquidity; it can be exchanged for money anytime theholders want. Gold investment can also be used as a hedge against inflation and

Study on Dynamic Relationship among Gold Price, Oil Price, Exchange Rate and Stock Market Returns 147

currency depreciation. From an economic and financial point of view, movementsin the price of gold are both interesting and important. It is often argued thatinvestment in gold is historically associated with fears about rising inflation and/or political risk. However, financial markets do not currently show the classicsymptoms associated with such fears.

In the context of commodities overwhelming financial assets, it is quiteinteresting to study the relationship between prices of fuel and metals specificallyoil and gold. For commodities that are traded continuously in organized marketssuch as the Chicago Board of Trade, a change in any exchange rate will result in animmediate adjustment in the prices of those commodities in at least one currencyand perhaps in both currencies if both countries are “large”. For example, whenthe dollar depreciates against the euro, dollar prices of commodities tend to rise(and euro prices fall) even though the fundamentals of the markets––all relevantfactors other than exchange rates and price levels––remain unchanged. The powerof this effect is suggested by the events surrounding the intense appreciation of thedollar from early 1980 until early 1985, during which the U.S. price level rose by 30per cent but the IMF dollar-based commodity price index fell by 30 per cent, anddollar-based unit-value indices for both imports and exports of commodity-exporting countries as a group declined by 14 per cent.4

The high oil price pushed up worldwide inflation, which in turn forced thegold price up. In 1983, the gold price climbed briefly to more than $800/oz and anounce of gold could buy more than 30 barrels of oil.

The rally in the gold price has been underway since April 2001. Since the currentrally is now in its tenth year, and that historically gold price rallies last no longerthan four years, this represents the most durable rally in history.

Figure 1

Source: Data collected from World Gold Council


HIGH OIL AND GOLD PRICES – A REFLECTION

The rise of gold price in 1980 could be attributed to political reasons. At the time,the Soviet invasion of Afghanistan, which began around Christmas 1979, was aterrible global shock. The Soviets had just signed a “bilateral treaty of cooperation”with Afghanistan in 1978, but by the next year relations had deteriorated. In themidst of cold war, this action was a major setback to America which had alreadybeen weakened by high inflation and unemployment and energy prices.

The future of the American economy and American power did not feel at allcertain. As a safe haven in times of panic and strife, gold simply reflected that fear.However the buying panic quickly subsided and this all time peak was followedby the beginning of a 22 year old bear market in gold.

Between 2000 and 2010, the price of gold jumped from $255 to over $1400 perounce. In 2009 and 2010, the inflation percentages have dropped dramatically evendipping into deflationary levels at times. The stock market is down significantlyfrom its 2007 highs. The indexes are ambivalent as to direction as of late 2010. Theglobal economy is recovering from a recession and still on shaky ground. Theseconflicting indicators create mixed signals for gold buyers. Still, it is worth notingthat gold is only 10 years into its long-term bull cycle.

Oil prices hit an all-time high of $145 a barrel in July 2008. This drove gas pricesto $4.00 a gallon. Most news sources blamed this on surging demand from Chinaand India, combined with decreasing supply from Nigeria and Iraq oil fields. Infact, global demand was actually down and global supply up during that time. Oilconsumption decreased from 86.66 million barrels per day (bpd) in the fourth quarter2007 to 85.73 million bpd in the first quarter of 2008. At the same time, supplyincreased from 85.49 to 86.17 million bpd. It was also stated that commodity pricesdrove up the oil prices. As investors retreated from the falling real estate and globalstock markets, they diverting their funds to oil futures .This sudden surge droveup oil prices, creating a speculative bubble. This bubble soon spread to othercommodities. Investor funds swamped wheat, gold and other related futuresmarkets. This speculation drove up food prices dramatically around the world.High oil prices were also said to be driven by a decline in the dollar. Most oilcontracts around the world are traded in dollars. As a result, oil-exporting countriesusually peg their currency to the dollar. When the dollar declines, so do their oilrevenues, but their costs go up.

COMPARISON OF GOLD, OIL IN REAL TERMS DURING THE PERIOD 1900-2010(BASE YEAR 2009)

In real terms gold hit all time high of $1537.94 in the year 1980 .The highest oil priceof $96.91 in real terms was in the year 2008. The second highest gold price of $1208.55was observed in the year 2010. The oil price of $95.89 in 1980 was the second highestin the last 110 years.


Table 1Comparison of 10 Year Average Gold, Oil Prices in1900-2010 Period

Year Real Gold Price in Real Oil Price indollars Dollars

1900-1909 519.74 20.181910-1919 398.32 19.651920-1929 254.84 19.891930-1939 451.81 14.781940-1949 405.36 15.421950-1959 277.53 14.991960-1969 240.18 12.171970-1979 490.84 38.381980-1989 868.09 55.371990-1999 505.27 26.572000-2010 624.06 54.97(11 year average)Overall (1900-2010) 459.32 26.83

The real oil prices were fluctuating over the time period. During the period1960-1969, the oil price was the lowest in the time period 1900-2010 with an averagevalue of $12.17. The real gold price was also lowest during the period 1960-69 withan average value of $240.18.The second lowest real gold prices were observed inthe period 1920-29 with an average value of $254.84 per ounce. On a closer look atthe time window of 40 years from 1930-1969, both oil and gold prices werefluctuating in an irregular manner. The average gold prices and oil prices showeddecreasing pattern from 1940s till 1969. During this period of thirty years the averagegold prices in dollar decreased by 46.8% .In the period 1940-49 , the average oilprices increased by 4.3% compared to the previous period of 10 years. In the 1950sand 1960s, the average oil prices decreased by 2.79 per cent and 18.8 per centrespectively. During the 70s and 80s the average real gold prices increased by 2.04and 1.76 times compared to the previous period. In the 1990s the average real goldprice declined by 41.79 per cent. During the same period, the real oil prices alsodeclined by 52 per cent .In the 11 period of 2000 -2011, the average oil price increasedby 23.5 per cent and the gold price by approximately 107 per cent.

Over the last century and decade, the gold prices have fluctuated to the greatestextent. The period 2000-2011 signified the highest variation in gold prices. Post1970, the gold price fluctuations increased manifold times compared to the previoustime windows of analysis. The lowest variation in the real gold prices was observedduring the time window of 1920-1929 and 1960-1969.Compared to 1960s, thefluctuations in gold prices increased by 872 times in the 2000s. Oil prices were verystable in the period 1950-1959. In the 70s and 80s fluctuations in oil prices increasedto a greater extent.


Table 2Variance Analysis of the 10 year Gold, Oil Prices in1900-2010 Period

Year Real Gold Price in Real Oil Price indollars Dollars

1900-1909 342.27 23.341910-1919 8252.03 29.401920-1929 92.94 27.731930-1939 14162.29 7.521940-1949 5805.32 3.791950-1959 176.13 0.381960-1969 98.04 0.881970-1979 44408.15 687.011980-1989 70951.82 604.701990-1999 6833.39 35.672000-2010 85572.06 489.86(11 year average)Overall 50441.86 396.52

REVIEW OF LITERATURE

Considerable research exists to understand the relationships or interactions amongvarious indicators of economic activity. Researchers have studies gold and oilrelationships with stock prices. Economic indicators included, among others,industrial production (Flood and Marion, 2006), interest rates (Hondroyiannis andPapapetrou, 2001), inflation (Moore, 1990), and currency rates (Amoateng and Jovad,2004). El-Sharif. et al. (2005) found positive, often significant, relationships betweenthe price of oil and equity values in the oil and gas sector using data relating only tothe United Kingdom. Basher and Sadorsky (2006) reported strong evidence for theobservation that oil price risk impacts stock price returns in emerging markets.

A large number of studies have attempted to statistically model the determinantsof the price of gold.

Broadly these studies follow three main approaches.

Approaches Perspectives Studies

1 Models variation in the price of gold in Ariovich, 1983; Dooley, Isard andterms of variation in main Taylor, 1995; Kaufmannand Winters,macroeconomic variables 1989; Sherman, 1982, 1983, 1986;

Sjaastad and Scacciallani, 1996).2 Focuses on speculation and the rationality (Baker and Van Tassel, 1985; Chua, Sick

of gold price movements and Woodward, 1990; Diba andGrossman, 1984; Koutsoyiannis, 1983;Pindyck, 1993)

3 Gold as a hedge against inflation with Chappell and Dowd, 1997; Ghosh et al.,particular emphasis on short-run and 2004; Kolluri, 1981; Laurent, 1994;long-run relationships Mahdavi andZhou, 1997; Moore,

1990; Ranson, 2005a, b).


The study by Janabi et al. (2010) explores whether the Gulf Cooperation Council(GCC) equity markets are informationally efficient with regard to oil and gold priceshocks during the period 2006–2008 using daily dollar-based stock market indexesdataset. The study also examines the impact of the impact of oil and gold prices onthe financial performance of the six distinctive GCC stock markets. The study findsthat GCC equity markets are informationally efficient with regard to gold and oilprice indexes.

The study by Zang et al. (2010) analyze the cointegration relationship andcausality between gold and crude oil prices. The study finds that there are consistenttrends between the crude oil price and gold price with significant positive correlationduring the sampling period. The study further suggests that long term equilibriumbetween the two markets and the crude oil price change linearly Granger causesthe volatility of gold price. With respect to the common effective price between thetwo markets, the contribution of the crude oil price seems larger than that of goldprice.

The study by Laughlin (1997) suggests that whether commodities fall in relationto gold or gold rises in relation to commodities, in either case the value of gold hasrisen .The study by Ashraf (2005) examines five cases in which the five instancesare noted in which a bottom gold-oil ratio coincided with falling {or negative) yieldspreads, a peaking fed funds rate, a falling dollar and eventually falling growth.

Pravit (2009) uses Multiple Regression and Auto Regressive Integrated MovingAverage (ARIMA) to forecast gold prices. The research result suggests that ARIMA(1, 1, 1) is the most suitable model to be used for forecasting gold price in the shortterm. Using multiple regression model the study suggests that that AustralianDollars, Japanese Yen, US dollars, Canadian Dollars, EU Ponds, Oil prices and GoldFuture prices have effect on the change of Thai gold price.

The study by Larry et al. (1997) supports the hypothesis of market efficiency forthe world gold market during the 1991-2004 periods. The study also finds that thereal appreciations or depreciations of the euro and the yen against the U.S. dollarhave profound effects on the price of gold in all other currencies. Further the studysuggests that the major gold producers of the world (Australia, South Africa, andRussia) appear to have no significant influence over the world price of gold.

The significant highlights of the study by Ismail et al( 2009) reflects the fact thatseveral variables like USD/Euro exchange rate , Inflation rate , Money supply (M1),NYSE Index, S&P Poor Index and US dollar index have an influence on gold prices.

The paper by Max (2004) presents a monetary theory of nominal oil and goldprices. It tests the model with a VAR system with a priori undetermined structuralbreaks. Results with US data indicate that nominal oil and gold prices is Grangercaused by monetary factors. Also money Granger causes inflation which in turnGranger causes output growth rate changes.


The paper by Mu Lan et al. (2010) uses daily data and time series method toexplore the impacts of fluctuations in crude oil price, gold price, and exchangerates of the US dollar vs. various currencies on the stock price indices of the UnitedStates, Germany, Japan, Taiwan, and China respectively, as well as the long andshort-term correlations among these variables. The results show that there exist co-integrations among fluctuations in oil price, gold price and exchange rates of thedollar vs. various currencies, and the stock markets in Germany, Japan, Taiwanand China.

To explore whether the prices of gold were affected by inflation and other marketfactors, Moore (1990) used the leading signals of inflation to test the relationshipbetween these leading signals and the gold prices of the New York Market since1970. Empirical results show that, from 1970 to 1988, gold prices and the stock/bond markets had a negative correlation, that is, when gold prices were rising, thestock/bond markets were declining.

The paper by Ai Han et al. (2008) proposes an interval method to explore therelationship between the exchange rate of Australian dollar against US dollar andthe gold price, using weekly, monthly and quarterly data. With the interval method,interval sample data are formed to present the volatility of variables. The ILSapproach is extended to multi-model estimation and the computational schemesare provided. The empirical evidence suggests that the ILS estimates wellcharacterize how the exchange rate relates to the gold price, both in the long-runand short-run.

Using cointegration techniques, Eric et al. (2006) suggests that there is a long-term relationship between the price of gold and the US price level. Second, the USprice level and the price of gold move together in a statistically significant long-runrelationship supporting the view that a one percent increase in the general US pricelevel leads to a one percent increase in the price of gold. There was a positiverelationship between gold price movements and changes in US inflation, US inflationvolatility and credit risk. The study also found a negative relationship betweenchanges in the gold price and changes in the US dollar trade-weighted exchangerate and the gold lease rate.

OBJECTIVES OF THE STUDY

As discussed in the review of literature that the results of interrelationship amongvarious important variables are varied and mixed. Reasons of these results couldbe due to time period of study and the time series modeling technique used by thestudies. Hence, it is imperative to verify the relationship periodically withsophisticate techniques. The paper explores the extent of linkages of crude oil price,stock market returns price and exchange rate on gold prices using vectorautocorrelation and cointegration technique with the more recent data. The objectiveof the study is to validate the relationship systematically.


VARIABLES AND DATA DESCRIPTIONS

The study has taken gold daily price in dollar and various other currencies data fromworld gold council, S&P500 from yahoo finance website, daily crude oil price Cushing,OK WTI Spot Price FOB (Dollars per Barrel) and Europe Brent Spot Price FOB (Dollarsper Barrel) and Trade Weighted Exchange from Thomson Reuters, Major Currencies(DTWEXM) as a proxy of exchange rate from Federal Reserve Bank of St. Louis’swebsite5. As gold prices in various currencies are in index with second January 2001asbase year we converted oil prices as in index by taking the same base period. In theseries taken there were some missing data due to holidays and other reasons, thesemissing values were filled by simply forecasting using Microsoft excel.

The variations on index is calculated by taking first difference of two successivedays i.e. Vt = Pt – Pt-1, where Vt is the variation at time t and Pt and Pt-1 are the priceat time t and t-1 respectively. The time period of this study is form 2nd January 1998to 5th June 2011, constituting 3485 observations. Appendix-3 presents the descriptivestatistics which shows that there is high value for standard deviation in all thevariables indicating variability. High Jarque-Bera shows that the series is normal.

1. Methodology

In order to examine the impact of oil price, exchange rate and stock market on goldprice Vector Autoregression (VAR) has been used. In this model all the variablesare considered to be endogenous and each endogenous variable is explained by itslagged or past values and the lagged values of all other endogenous variablesincluded in the model. There are no exogenous variables in the model and hence,by avoiding the imposition of a priori restriction on the model the VAR addssignificantly to the flexibility of the model.

The vector autoregression (VAR) is commonly used for forecasting systems ofinterrelated time series and for analyzing the dynamic impact of randomdisturbances on the system of variables.

The VAR approach sidesteps the need for structural modeling by modelingevery endogenous variable in the system as a function of the lagged values of all ofthe endogenous variables in the system.

The mathematical form of a VAR isyt = A1yt–1 + ... + Apyt–p + Bxt + εt

where yt is a k vector of endogenous variables, xt is a d vector of exogenous variables,A1,... Ap, and B are matrices of coefficients to be estimated, and εt is a vector ofinnovations that may be contemporaneously correlated with each other but areuncorrelated with their own lagged values and uncorrelated with all of the right-hand side variables.

Since only lagged values of the endogenous variables appear on the right-handside of each equation, there is no issue of simultaneity, and OLS is the appropriate


estimation technique. Note that the assumption that the disturbances are not seriallycorrelated is not restrictive because any serial correlation could be absorbed byadding more lagged y’s.

STATIONARITY OF VARIABLES

A stationary time series is significant to a regression analysis based on the timeseries, because useful information or characteristics are difficult to identify in anonstationary time series. Therefore, a nonstationary time series would lead to aspurious regression. However, most economic time series are nonstationary inpractice. Hence, time series should be made stationary after differencing. Usefulinformation or characteristics can still be identified in the time series afterdifferencing. A time series is said to be stationary if its mean and variance areconstant and, the covariances depend on upon the distance of two time periods.The unit root test is used to test stationarity of variables and the order of integration.The Dicky-Fuller unit root test (DF), Augmented Dicky-Fuller unit root test (ADF)(Dicky and Fuller, 1979) and the Phillips-Perron unit root test (PP) (Phillips andPerron, 1988) are often used to test stationarity. For the VAR estimation all thevariables included in the model should be stationary. Table 1 presents AugmentedDickey-Fuller (ADF) and Phillips-Perron(PP) tests at level. The result of ADF test ispresented with lag 4 and PP test is conducted with lag 8 suggest by Newey-West.However, several other lags were also selected and the result is invariant. It is clearthat none of the values are more than Mckinnon critical values in absolute termshence we conclude that there is unit root present in the series. Table-2 presents theunit root test with first difference and the results shows that all the index dataseries are not stationary at the level but stationary after the first difference. In otherwords all the data series are I(1) which denotes that the time series is integrated atthe first difference level.

Table 1Unit Root Test with Level Data

Stock index

ADF with Level PP with level

Intercept Intercept and Intercept Intercept andTrend Trend

Gold price($) 1.81(4) -0.80(4) 1.95(8) -0.74(8)S&P 500 Index -2.18(4) -2.19(4) -2.17(8) -2.17(8)Exchange rate -0.57(4) -2.17(4) -0.52(8) -2.19(8)Oil price index (WTI) -1.17(4) -2.86 (4) -1.08(8) -2.74(8)Oil price index (BRENT) -0.57(4) -2.40(4) -0.57(8) -2.42(8)

Note: *, **, *** represents the McKinnon critical values for ADF and PP at 1%, 5%, 10% levelsrespectively. The values in the parenthesis are lags. For ADF the lag augmentation is on thebasis of AIC. For PP test (Newey-West suggests: 8).


Table 2Unit Root Test with first difference

Stock index

ADF with Level PP with level

Intercept Intercept and Intercept Intercept andTrend Trend

Gold price($) -25.92(4)* -26.05(4)* -59.53(8)* -59.63(8)*

S&P 500 Index -27.78(4)* -27.78(4)* -65.00(8)* -64.99(8)*

Exchange rate -26.14(4)* -26.15(4)* -60.56(8)* -60.56(8)*

Oil price index (WTI) -26.83(4)* -26.83(4)* -62.71(8)* 62.70(8)*

Oil price index (BRENT) -25.24(4)* -25.25(4)* -58.47(8)* -58.47(8)*

Note: *, **, *** represents the McKinnon critical values for ADF and PP at 1%, 5%, and 10% levelsrespectively. The values in the parenthesis are lags. The lag augmentation is on the basis ofAIC. The values in the parenthesis are lags. For ADF the lag augmentation is on the basis ofAIC. For PP test ( Newey-West suggests: 8).

There are at least two advantages when using the first difference data series toexplain the impulse response function. Firstly, it focuses more on the increase ordecrease trend rather than the actual change. Because the first difference data seriesis the increase or decrease between every two consecutive dates, a strengthening orweakening of the trend will be detected by the impulse response function. Secondly,it captures more information on the shocks of gold prices, because the first differencedata shows the changes in the past two days while the level data shows the changesin one day in impulse response function.

SELECTION OF OPTIMAL LAG

One of the important aspect of VAR model is to select the optimal lagged term.Traditional way of selecting the lag length was by repeating VAR model by reducinglag length from a large lag term until 0. In each of these models, the smallest valueof the Akaike information criterion and the Schwarz criterion are used to select theoptimal lag length (Grasa, 1989; DeJong et al., 1992; Maddala and Gujarati, 2003). Inthis study however, five criteria: Sequential modified LR test statistics (LR), Finalprediction error (FPE), Akaike information criterion (AIC), Schwarz criterion (SC)and Hannan-Quinn information criterion (HQ), which have been introduced byLutkepohl (1993) were inspected. Similarly, the smallest value of these 5 criteriapoints to the optimal lag length.

Table 3 shows the summary results of VAR lag order selection criterion. Thefirst left hand column shows the model for which the lag length has been selectedusing The LR, FPE, AIC, SC and HQ criterion. The numbers are the smallest valuein each of criteria. Before selecting the lag length, one must consider that too shorta lag length in the VAR may not capture the dynamic behaviour of the variables(Chen and Patel, 1998) and too long a lag length will distort the data and lead to a


decrease in power DeJong et al. (1992). Based on the results, the study chose fourlag to be appropriate.

Table 3Lag-Length Selected by Different Criteria

Model for lag length with I(1) LR FPE AIC SC HQ Lag length selectedfor this study

Gold($), EXCHRATE, 30 21 21 4 8 4S&P500, WTI

Gold($), EXCHRATE,S&P500, BRENT

GOLD(Euro), EXCRATE, 29 17 17 4 8 4S&P500, BRENT

GOLD(Euro), EXCHRATE, 30 21 21 4 8 4S&P500, WTI

Included observations: 3454, LR: sequential modified LR test statistic (each test at 5% level), FPE:Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion, HQ:Hannan-Quinn information criterion

ORDERING OF THE VARIABLES

The ordering of the variables is another crucial aspect in VAR estimation. Properordering shows that current innovations in the variable that is placed first affectthe rest of the variables. At the same time, the current innovations in variablesplaced towards the end are not expected to affect the variables in the beginning ofthe order. The study selected the ordering of the variables by conducting pair-wiseGranger causality tests with the lag length selected by the criteria. The followingorders were selected for this study.

1. Gold ($), WTI, Exchange rate, S&P………………………..(Model-1)2. Brent, exchange rate, WTI, Gold(euro) ……………………( Model-2)

ESTIMATION OF VAR

The coefficients obtained from the estimation of the VAR model may not be properto interpret directly. Hence, both impulse response functions and the variancedecomposition are used. Impulse response functions are used to trace out thedynamic interaction among variables. It shows the dynamic response of all thevariables in the system to a shock or innovation in each variable. In other words, itfocuses more on the increase or decrease in trend rather than the actual value of thevariable. On the other hand, variance decomposition is used to detect the causalrelationships among the variables. It shows the extent to which a variable isexplained by the innovations or shocks in all the variables in the system.

The result of model 1 is presented in figure 1 and table 5. The impulse responseof model 1 shows the response to one standard deviation shock in the error terms


of other variables. The X axis shows the time period and the Y shows the shock inthe movement trend. The positive symbol does not mean an increase in index. Itmeans an increase in movement trend is strengthened. In short, a positive symbolmeans a favorable effect on index and a negative symbol means an adverse effect.It can be seen that gold in $ has positive favorable impact to a shock in WTI indexwhere as all remaining impacts are marginal. Similarly, the response of WTI to ashock in Gold($) has favorable effect and it lasts for more days with lots of variations.Response of Exchange rate to a shock in gold price index is unfavorable as it startsfrom negative side. Similar unfavorable results can be seen for the response ofexchange rate to a shock in WTI and S&P as well. Response of S&P index to a shockin WTI is favorable. It is clear from impulse response function that the shock lastsfor few days only and the intensity of response is weak.

The intensity of response can be seen from Variance Decomposition table 5.Innovations in WTI can explain around 2.4% variations in gold price index in $. Allother innovations explain below one percent of the variation in gold price index.

Table 4Pair-wise Granger Causality Tests

Null Hypothesis F-Statistic Probability

SP does not Granger Cause GOLDEURO 0.53224 0.71206EXCH does not Granger Cause GOLDEURO 0.62378 0.64554EXCH does not Granger Cause GOLDEURO 0.62378 0.64554EXCH does not Granger Cause SP 0.63549 0.63717WTI does not Granger Cause GOLDEURO 0.91018 0.45694GOLDUS does not Granger Cause SP 1.15388 0.32927WTI does not Granger Cause SP 1.18158 0.31676BRENT does not Granger Cause GOLDEURO 1.31903 0.26036GOLDEURO does not Granger Cause SP 1.40133 0.23087EXCH does not Granger Cause WTI 2.31420*** 0.05521GOLDUS does not Granger Cause EXCH 2.44346** 0.04462BRENT does not Granger Cause GOLDUS 3.88093* 0.00378SP does not Granger Cause GOLDUS 3.98387* 0.00315GOLDUS does not Granger Cause BRENT 4.26925* 0.0019GOLDUS does not Granger Cause WTI 6.30267* 4.70E-05SP does not Granger Cause WTI 6.60052* 2.70E-05EXCH does not Granger Cause GOLDUS 6.67577* 2.40E-05WTI does not Granger Cause EXCH 7.07346* 1.10E-05GOLDEURO does not Granger Cause WTI 7.85588* 2.70E-06GOLDEURO does not Granger Cause EXCH 10.6839* 1.30E-08GOLDEURO does not Granger Cause BRENT 13.0458* 1.50E-10SP does not Granger Cause EXCH 14.3701* 1.20E-11WTI does not Granger Cause GOLDUS 21.8512* 0


Similarly, innovations in gold price index in $ explains around 4 to 5% variations inWTI index. It is clear from this that both gold index and WTI index explains eachother and the percentage of variation is less.

One of the interesting finding of Variance Decomposition is about exchangerate which is largely explained by innovations in gold index (10%), WTI (3%) andS&P (1%). This can also be seen from the impulse response function discussed above.In case of S&P index the innovation in WTI index explains around 2% of thevariations in S&P index.

In model 2 the study used a different ordering of variables and replacing WTIindex with Brent index and instead of gold index in $ the model used gold priceindex in euro. The result is more or less similar. Innovations in Brent index explainaround 6 to 7% variations in Exchange rate. S&P index explains around 1.5% andgold index in euro explains just 1% variations in exchange rate. This can also beseen from the trend in impulse response figures mentioned in figure 2.

Engle and Granger (1987) pointed out that a linear combination of two or morenon-stationary series may be stationary. If such a stationary, or I(0), linearcombination exists, the non-stationary (with a unit root), time series are said to becointegrated. The stationary linear combination is called the cointegrating equationand may be interpreted as a long-run equilibrium relationship between the variables.The study further investigated whether or not the variables in our model arecointegrated? For this the study used Johansen’s (1991) maximum likelihoodmethod. The result of cointegration test is presented in the below mentioned table.However, the study could not find any cointegration among variables in the firstmodel where as in the second model there is just one cointegrating equation showingweak long run relationship among variables.

CONCLUSION AND DISCUSSIONS

Gold historically combated losses that occurred during the period of inflation, socialunrest and war. When stock prices fell financial advisors were expected to adviseinvestors to maintain a position in gold during the period. Conversely during boomtimes, gold investments often decreased in value as stock prices increased like in1990s. Some investors believed that gold prices had no portfolio risk aversion valueand can be treated like any other commodity whose price changes were strictlydetermined by supply and demand. During times of oil price uncertainty, oilinvestments emerged as a risk deterrent in the context of inverse relationship withstock market movement. In the currency market, exchange rates are often predicatedon the health of a country’s economy. If the economy is robust and growing, theexchange rates for their currency reflect that in higher value.The simple relationshipbetween currencies through a single common commodity does not exist and theinterconnection between gold prices, exchange rates and oil prices are all complexin nature. There are many factors on which the prices of gold and crude oil may


depend upon: government policies; budget, inflation, economic and politicalcondition of the country etc.

This paper aims to establish and validate the dynamic relationship ofcommodities prices involving gold and crude oil with exchange rate and stockindex. The paper uses daily time series data to explore the impact of fluctuationsand interrelationship among crude oil price, stock market returns and TradeWeighted Exchange Index which is computed by taking major Currencies(DTWEXM) and gold price. The link between variables which determine the oiland gold prices variation and their relation with economic activity have beenexplored in empirical research. But studies involving the relationship between oilprices, gold prices, exchange market and stock market returns are limited. Thispaper aims to demonstrate systematically the dynamic relationship among goldprice, oil price, exchange rate and stock market returns using Vector auto regressive(VAR) technique.

The study used two models to show the dynamic relationship. The first modeltakes gold index in US dollar and in second model gold index in euro. In order toadd variety we have taken WTI [Cushing, OK WTI Spot Price FOB (Dollars perBarrel)] in the first model and Brent [Europe Brent Spot Price FOB (Dollars perBarrel)] in the second model. The ordering of variables is done on the basis ofGranger causality test. The result shows that exchange rates have a direct influenceon gold prices; oil prices and stock market index. The variance decompositionimplies that the largest portions of total variations in exchange rate comes frominnovation in Gold index, WTI and S&P stock market index. The dynamic effects ofthe impulse response also suggest the same in terms of the relationship of exchangerate with respect to gold prices, oil prices, stock market index returns and inflation.

It is clear from the analysis that fluctuations in gold prices are largely dependenton gold itself rather than oil and other indices. But gold price fluctuation affects theWTI index. Most of the variables taken in this study affects exchange rate in someway or the other. Out of which gold plays an important role with largest variationof 10%. However, gold price in euro turned out to be less affected by the indicestaken.

Gold prices are typically denominated in US dollars and this implies that theexposure gained from buying /selling gold is influenced by changes in the exchangerate for US dollars. Changes in exchange rate through changes in costs and revenueswill have direct impact on profits and thus impact stock returns. However, goldindex in euro fails to show similar effects on exchange rate as the shock in goldprice in euro explains just 1% of the variations in exchange rate.

It is often observed that with higher oil prices, the currency of oil exportingcountries rise in value and that of oil importing countries decrease in value. Themost profitable trades are those between that of a country that exports oil vs acountry that depends on oil. Canada is among the largest oil exporting nations.


The increasing oil exports can be compared with the strengthening of Canadiandollars over a period of time. Similarly Japan’s reliance on Oil imports make itvulnerable to oil price fluctuations which would lead to drop in yen value. Theresult of this study shows that a shock in WTI and Brent, used as a proxy of oilprice, causes 3% and 6-7% fluctuation in exchange rate respectively.

The study also verified the presence of cointegration among variables and foundthat there is one cointegrating equation in second model. This shows that there isweak long run relationship among variables. (See Appendix 2).

Notes1. Eric J. Levin & Robert E. Wright, Short run and Lon run determinants of the price of gold ,

World Gold council Research Study No. 32 , 2006.

2. Larry A. Sjaastad, Fabio Scacciavillani, “The Price of Gold and the Exchange Rates,” Journal ofInternational Money and Finance, December, 1996.

3. http://research.stlouisfed.org

ReferencesAi Han, Shanying Xu, Shouyang Wang (2008), Australian Dollars Exchange rate and Gold Prices :

An Interval Method Analysis , 7th International Symposium on Operations Research and itsApplications (ISORA’08) , Lijiang China, Oct 31-November 3.

Amoateng, Kofi A. and Kargar, Jovad, (2004), “Oil and Currency Factors in Middle East EquityReturns”, Managerial Finance, Vol. 30 (3), 2004, 3-16.

Ariovich, G., (1983), The Impact of Political Tension on the Price of Gold, Journal for Studies in Economicsand Econometrics, Vol. 16, pp. 17-37.

Asharaf Laidi, (2005), Gold, Oil and Dollar Repercussions, Futures, December, page 36-38.

Baker, S. A., and van Tassel, R. C., (1985), Forecasting the Price of Gold: A Fundamentalist Approach,Atlantic Economic Journal, Vol. 13, pp. 43-51.

Basher, Syed A. and Sadorsky (2006), Perry, “Oil Price Risk and Emerging Stock Markets”, GlobalFinance Journal, Vol. 17 (2), 224-XXX.

Chappell, D. and Dowd, K., (1997), A Simple Model of the Gold Standard, Journal of Money, Credit andBanking, Vol. 29, pp. 94-105.

Chen, M. C. and Patel, K. (1998), House Price Dynamics and Granger Causality: An Analysis ofTaipei New Dwelling Market, Journal of the Asian Real Estate Society, 1(1), pp. 101-126.

Chua, J., Sick, G. and Woodword, R., (1990), Diversifying with Gold Stocks, Financial Analysts Journal,Vol. 46, pp. 76-79.

DeJong, D. N., Nankervis, J. C., Savin, N. E. and Whiteman, C. H. (1992), The Power Problems of UnitRoot Test in Time Series with Autoregressive Errors, Journal of Econometrics, 53(1-3), pp. 323-343.

Diba, B. and Grossman, H., (1984), Rational Bubbles in the Price of Gold, NBER Working Paper: 1300.Cambridge, MA, National Bureau of Economic Research.

Dicky, D. A. and Fuller, W. A. (1979), Distribution of the Estimators for Autoregressive Time Serieswith a Unit Root, Journal of the American Statistical Association, 74(336), pp. 427-431.

Dooley, M. P., Isard, P. and Taylor, M. P., (1995), Exchange Rates, Country-specific Shocks and Gold,Applied Financial Economics, Vol. 5, pp. 121-129.

http://research.stlouisfed.orgReferences


El-Sharif, Idris; Brown, Dick; Burton, Bruce; Nixon, Bill; and Russell, Alex,(2005), “Evidence on theNature and Extent of the Relationship between Oil Prices and Equity Values in the UK”, EnergyEconomics, Vol. 27 (6), 2005, 819-XXX.

Engle, R. F., & C. W. J. Granger (1987), Cointegration and Error-correction Representation, Estimationand Testing. Econometrica, Vol. 55, pp. 251-276.

Eric J. Levin & Robert E. Wright, (2006), Short Run and Lon Run Determinants of the Price of Gold ,World Gold Council Research Study No. 32.

Flood, Robert, and Marion, Nancy, (2006), “Stock Prices, Output, and the Monetary Regions”, OpenEconomies Review, Vol. 17 (2), 147-173.

Ghosh, D. P., E. J. Levin, P. Macmillan and R. E. Wright and (2004), Gold as an Inflation Hedge?,Studies in Economics and Finance, Vol. 22, No. 1, pp. 1-25.

Global Market Research Report September 2010, Deutsche Bank.

Grasa, A. A. (1989), Econometric Model Section: A New Approach. Kluwer Academic, Boston.

Gujarati, D. N. (2003), Basic Econometrics. Gary Burke, New York.

Hondroyiannis, George and Papapetrou, Evangelia, (2001), “Macroeconomic Influences on the StockMarket”, Journal of Economics and Finance, Vol. 25 (1), 2001, 33-50.

Ismail Yahya, Shabri, (2009), Forecasting Gold Prices using Multiple Linear Regression Method,American Journal of Applied Sciences 6(8), 1509-1514.

Janabi, Mazin A. M., Hatemi. J., Abdul Nasser, Irandoust, Manucheh (2010), International Review ofFinancial Analysis, Jan 2010, Vol. 19, Issue 1, p. 47-54.

Johansen, S. (1991), “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian VectorAutoregressive Models,” Econometrica, Econometric Society, Vol. 59(6), Page 1551-80.

Kaufmann, T. and Winters, R., (1989), The Price of Gold: A Simple Model, Resources Policy, Vol. 19,pp. 309-318.

Kolluri, B. R., (1981), Gold as a Hedge against Inflation: An Empirical Investigation, Quarterly Reviewof Economics and Business, Vol. 21, pp. 13-24.

Koutsoyiannis, A., (1983), A Short-Run Pricing Model for a Speculative Asset, Tested with Data fromthe Gold Bullion Market, Applied Economics, Vol. 15, pp. 563-581.

Larry A. Sjaastad, Fabio Scacciavillani (1996), “The Price of Gold and the Exchange Rates”, Journal ofInternational Money and Finance, Dec. 1996; reprinted in Meher Manzur (ed), Exchange Rates,Interest Rates and Commodity Prices, Edward Elgar, 2002 and in Moonjoong Tcha (ed), Goldand the Modern World Economy, Rouledge 2003.

Laughlin, J. Laurence, Gold and Prices Since (1887), Quarterly Journal of Economics, Apr987, Vol. 1,Issue 3, p. 319-355.

Laurent, R. D., (1994), Is There a Role for Gold in Monetary Policy? Economic Perspectives, 18, 2-14.

Lutkepohl, H. (1993), Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin.

Maddala, G. S. and Kim, I. N. (1998), Unit Root, Cointegration, and Structural Change. CambridgeUniversity Press, United Kingdom.

Mahdavi, S. and S. Zhou, (1997), Gold and Commodity Prices as Leading Indicators of Inflation:Tests of Long-Run Relationship and Predictive Performance, Journal of Economics and Business,Vol. 49, pp. 475-489.

Max Gillman, Anton Nakov, (2004), Monetary Causality of Oil and Gold Prices, Central EuropeanUniversity Working Paper.

Moore, Geoffrey H. (1990), “Gold Prices and a Leading Index of Inflation”, Challenge, Vol. 33 (4),1990, 52-57.


Mu-Lan Wang, Ching-Ping Wang Tzu-Ying Huang, (2010), Relationships among Oil Price, GoldPrice, Exchange Rate and International Stock Markets International Research Journal of Financeand Economics, Issue 47 Euro Journals Publications.

Phillips, P. C. B. and Perron, P. (1988), Testing for a Unit Root in Time Series Regression, Biometrica,75(2), pp. 335-346.

Pindyck, R. S., (1993), The Present Value Model of Rational Commodity Pricing, Economic Journal,Vol. 103, pp. 511-530.

Pravit Khaemusunun (2009), Forecasting Thai Gold Prices, http://www.wbiconpro.com/3-Pravit-.pdf

Ranson, D., (2005a), Why Gold, Not Oil, Is the Superior Predictor of Inflation, London, World GoldCouncil.

Ranson, D., (2005b), Inflation Protection: Why Gold Works Better Than “Linkers”, London, WorldGold Council.

Sherman, E. J., (1982), New Gold Model Explains Variations, Commodity Journal, Vol. 17, pp. 16-20.

Sherman, E., (1986), Gold Investment: Theory and Application, New York, Prentice Hall.

Sherman, E. J., (1983), A Gold Pricing Model, Journal of Portfolio Management, Vol. 9, pp. 68-70.

Sjaastad, L. A. and F. Scacciallani, (1996), The Price of Gold and the Exchange Rate, Journal of Moneyand Finance, Vol. 15, pp. 879-897.

Zhang, Yue-Jun, Wei Yi-Ming (2010), The Crude Oil Market and the Gold Market: Evidence for cointegration, Causality and Price Discovery, Resource Policy, Sep, Vol. 35 Issue 3, p. 168-177, 10p.

Appendix 1

Figure 1: Impulse Response of Model 1

http://www.wbiconpro.com/3-Pravit-.pdf


Table 5 (Model-1)Forecast Error Variance Decomposition (%)

By Innovations in

Variables explained Steps DGold($) D(WTI) D(EXCH) D(SP)

1 100 0 0 0DGold($) 2 97.00953 2.408574 0.393806 0.188092 4 96.98881 2.421412 0.39538 0.1944 6 96.98757 2.422423 0.395554 0.194452 10 96.98756 2.422431 0.395555 0.194453

1 4.670554 95.32945 0 0D(WTI) 2 4.663751 94.67627 0.050133 0.609842 4 5.062611 94.27301 0.053657 0.610724 6 5.063766 94.27158 0.053687 0.610964 10 5.063767 94.27158 0.053689 0.610968

1 9.983431 2.23718 87.77939 0 2 9.781681 2.787204 86.1912 1.239914D(EXCH) 4 9.941507 2.785671 85.99585 1.276976 6 9.941585 2.785948 85.99544 1.277026 10 9.941587 2.78595 85.99544 1.277026

1 0.008423 2.075613 0.038329 97.87763 2 0.018852 2.315957 0.05435 97.61084 4 0.135892 2.360765 0.080636 97.42271D(SP) 6 0.136756 2.36091 0.080687 97.42165 10 0.136758 2.360919 0.080688 97.42164

Figure 2: Impulse Response Function of Model 2


Table 5Forecast Error Variance Decomposition (%)

By Innovations in

Variables explained Steps DBrent D(EXCH) D(SP) D(Gold euro)

1 100 0 0 0D(Brent) 2 98.62662 0.000708 0.875632 0.497042 4 98.53315 0.025586 0.887106 0.554161 6 98.50023 0.025581 0.886875 0.587312 10 98.49773 0.025582 0.886853 0.58984

1 7.020575 92.97942 0 0D(EXCH) 2 6.859108 90.87158 1.482127 0.787188 4 6.862712 90.65788 1.513882 0.965527 6 6.859721 90.6117 1.513137 1.01544 10 6.859481 90.60431 1.513019 1.023191

1 0.849647 0.006429 99.14392 0 2 0.872523 0.020691 99.00461 0.102177D(SP) 4 0.874946 0.024881 98.95489 0.145287 6 0.87506 0.024898 98.94581 0.15423 10 0.875116 0.024898 98.94366 0.156322

1 0.005985 0.477452 0.048912 99.46765 2 0.337606 0.323116 0.034116 99.30516D(Gold euro) 4 0.527258 0.317051 0.035738 99.11995 6 0.545922 0.311951 0.036908 99.10522 10 0.552228 0.311683 0.036944 99.09915

Appendix 2

Model-1 Eigen value Null Hypothesis LR Statistics Critical value(trace Statistic) 5% 1%

With linear deterministic 0.005249 r=0 33.73766 47.21 54.46trend in data 0.002991 r≤1 15.42402 29.68 35.65

0.001341 r≤2 5.000186 15.41 20.049.47E-05 r≤3 0.329490 3.76 6.65

No deterministic 0.004646 r=0 31.13455 39.89 45.58trend in data 0.002370 r≤1 14.92975 24.31 29.75

0.001870 r≤2 6.673186 12.53 16.314.56E-05 r≤3 0.158786 3.84 6.51

Model-2With linear deterministic 0.034052 r=0 133.8935* 47.21 54.46trend in data 0.002477 r≤1 13.32966 29.68 35.65

0.001076 r≤2 4.699122 15.41 20.040.000274 r≤3 0.953587 3.76 6.65

No deterministic 0.029008 r=0 117.5548* 39.89 45.58trend in data 0.002812 r≤1 15.11391 24.31 29.75

0.001522 r≤2 5.312087 12.53 16.310.0000032 r≤3 0.011303 3.84 6.51

In model-1 L.R. rejects any cointegration at 5% significance level. In model-2 L.R. test indicates 1cointegrating equation(s) at 5% significance level


Appendix-3Descriptive Statistics

BRENT WTI GOLDUS GOLDUK EXCH SP

Mean 206.3528 181.0425 208.0798 185.8706 88.8861 1189.999Median 170.014 151.7772 153.23 125.59 85.9542 1187.7Maximum 621.4621 538.0254 568.42 515.33 113.0977 1565.15Minimum 38.8391 38.82636 92.72 86.7 68.2405 676.53Std. Dev. 124.5562 103.6308 122.6771 114.2451 12.0476 181.5427Skewness 0.808646 0.783732 1.082158 1.38956 0.207448 -0.16822Kurtosis 2.971085 3.001649 3.054802 3.717136 1.799332 2.387342

Jarque-Bera 379.9334 356.7687 680.63 1196.195 234.329 70.94109Probability 0 0 0 0 0 0

Observations 3485 3485 3485 3485 3485 3485

STUDY ON DYNAMIC RELATIONSHIP AMONG GOLD PRICE, OIL PRICE ...library.imtdubai.ac.ae/Faculty...

Documents

Transcript of STUDY ON DYNAMIC RELATIONSHIP AMONG GOLD PRICE, OIL PRICE ...library.imtdubai.ac.ae/Faculty...