143 EVALUATION OF GOLD MARKET IN INDIA AND ITS PRICE DETERMINANTS S.Venkata SESHAIAH

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143

EVALUATION OF GOLD MARKET IN INDIA AND ITS PRICE DETERMINANTS S.Venkata SESHAIAH^*,

I.R.S.SARMA**, Aviral kumar TIWARI^***1 Abstract

The study assesses the influence of Oil prices, Exchange rate, trade deficit, and fiscal deficit on the prices of gold in India. Using the Johansen's cointegraion, variance Decomposition and Granger causality test for the period 1994 - 1995 to 2014 -2015 with monthly observations, the study found that all variables are stationary at first difference and that there are two cointegration relationships between Gold prices, crude oil prices, exchange rate, Trade Deficit and Fiscal deficit. This implies that the variables under consideration have a long relationship. The results revealed that the variation in gold prices explained by gold itself by 93.4% where as the other variables’

influence on the variation of gold prices under consideration is negligible. Gold prices and trade deficit contributes 9.43% and 7.92% respectively in the variance of crude oil prices, where as the gold prices and trade deficit contributes 9.73% and 12.22%

respectively in the variance of the prices of exchange rate. The variance in the trade deficit was explained by variance in gold price is as high as 11.31% compared all the other variables under consideration.

JEL Codes: P23, O53

Keywords: Gold Market, India I. Backdrop of the paper:

Indian economy facing a lot of challenges in many directions such as it may be policy paralysis, rising inflation, rising oil prices, growing demand for gold, trade deficit, fiscal deficit, currency depreciation etc. All these variables became a cause of concerns to the consumers, investors, policy makers and business persons. In India gold is considered to be the most prestigious commodity as well as a best instrument to hedge against inflation.. Normally rising prices of gold indicates the recession and falling stock market, but in India it is not the case, the root cause for rise in the gold prices are as follows.

1. India is predominantly dominated by unorganized or informal sector, till today 66% of the population living in rural area are away from banking facilities and hence they buy gold which creates the demand for gold

* Professor and Director I/C, IBS-Hyderabad , A Constituent of IFHE (Declared as Deemed to be University under section 3 of the UGC Act, 1956), venkatas@ibsindia.org

** Assistant professor, IBS-Hyderabad , A Constituent of IFHE (Declared as Deemed to be University under section 3 of the UGC Act, 1956)

*** Assistant professor, IBS-Hyderabad , A Constituent of IFHE (Declared as Deemed to be University under section 3 of the UGC Act, 1956)

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2. In India gold is treated as a symbol of goddess and hence they gift to the daughters, close friends, son in-laws etc., in the form of gold during festivals, marriages etc.

3. Tradeoff between Stock market and Gold prices and gold is considered as best instrument to hedge against inflation.

4. Black Money in the form of Gold: As per the study by Ambit Capital Research (http://indiatoday.intoday.in/story/black-economy-shrinking-still-exceeds-

thailands-gdp-ambit/1/684025.html), the size of the India’s black economy is about USD 460 billion (over Rs 30 lakh crore), which is larger than the emerging markets like Thailand and Argentina. The large portion of money in the form of black money invested in high value assets such as gold and real estate.

5. When the people are constructing houses they keep some gold when they are laying the foundation. Even at the time of performing obsequies a small amount of gold will be used. Together they also contribute to demand of gold though it may not be that significant contributor.

6. World Gold Council (WGC) report pointed out that the income growth is the most significant factor for gold demand in India, and demand responds more to income than to changes in price. The gold industry lobby said that its econometric analysis of data from 1990 to 2015 revealed that everything else being constant, a 1% rise in income boosts gold demand by a similar per cent. At same time everything else being constant, a Rs.1000/10gm rise in price declines gold demand by 3.12gms for the same period. There are some other short term factors which affect the gold demand like Taxes, Rainfall, Gold Prices, Inflation, wind fall gains.

7. India is the second largest jewelry market in the world after china. The domestic jewelry demand trend in India is stable for the last six year period (Figure-5) with minor seasonal fluctuations. India’s Gold Jewelry exports have been increased and the trend is rising at an increasing rate for the last 25 years at an increasing rate (Figure-4).

Though Government of India increased the exercise duty on the imports of the gold, the demand for gold (though the price of gold increased) in India has not declined due the aforesaid reasons. Hence the high demand for gold contributing to weakening the rupee. Hence the imports become costlier there by increasing the burden of subsidy on the government leading to rise in fiscals deficit, trade deficits etc. High fiscal deficit leads to inflation which may lead to increase in interest rates. Due to high interest rates and inflation stock market will take huge hit further contributing the demand for gold to hedge against inflation.

All the above said factors together contribute to the rise in gold prices and it indicates that Indian tradition is the root cause for rise in gold prices than the rise in oil prices, fiscal deficit, trade deficit and exchange rates. These phenomena may be observed from figure-1 to figure -3. Hence it is felt that the linkage between gold prices, oil prices, fiscal deficit, trade deficit and exchange rate will help the policy makers and investors to take appropriate decisions especially in the current scenario, where the

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government of India demonetized the high denomination currency of INR 1000 and INR 500.

It is observed from the table-1, that the growth in the prices of gold is almost two times greater than the growth in crude oil price and exchange rate. It is also observed that the volatility in fiscal deficit as well as Trade deficit is greater than the other variables under the present study. Based on the Jarque - Bera test it is observed that except for the exchange rate the null of normality is rejected for all other variables under consideration. Finally it may concluded that irrespective of the fluctuation in Exchange rate, Trade deficit, Fiscal deficit and oil prices, the growth in the prices of gold is greater indicating the tradition and sentiment as root cause for the rise in gold prices.

Why this Linkage is Important?

Oil Prices: Compared to most of the countries, India is the heavy importer of oil which is one of the important energy resources. An increase in global oil prices hurts the rupee which gets reflected in the current account deficit and the Indian economy as a whole. But once it comes to gold market, interestingly rise in oil prices has not influenced the gold prices as it may be observed from Figure 1

Exchange Rate: Across the globe, the gold prices are derived based on the fluctuations in the dollar price. Due to high increase in the imports of gold as well as heavy imports of the oil, the trade deficit, fiscal deficits in India is very high due to high demand for dollar. Hence India is facing continuous depreciation of rupee theoretically whenever the price of dollar increases the demand for gold should decrees vice versa. If the dollar price increases the investor buy dollar denominated assets to get profit where as if the dollar price decreases the investor lose the confidence in dollar assets and part of funds will be diverted to buy gold in order to preserve and increase profit. However in case of India irrespective of the fluctuation in exchange rate the gold price increased, these phenomena may be observed from figure 2.

Trade deficit, Fiscal Deficit: Indian economy is facing the problem of twin deficit.

Twin deficits do not contribute to the growth of the economy. Gold forms an essential part of every India’s investment portfolio whenever prices false/ festivals roll by. Since India produces negligible amount of gold and hence it has to depend on imports and gold prices widen the import bill. In fact it puts the economy on a danger position in the form of currency depreciation, higher inflation, out flow of both foreign portfolio investment as well foreign direct investments which will have direct impact on gold market. It may be observed from figure 3 that irrespective of the fluctuations in the trade and fiscal deficits, the gold prices have continuously increased.

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2 3 4 5 6 7 8

94 96 98 00 02 04 06 08 10 12 14 LOG(GOLD) LOG(CRUDE)

Figure 1

3 4 5 6 7 8

94 96 98 00 02 04 06 08 10 12 14 LOG(GOLD) LOG(EXRATE)

Figure 2

-30 -20 -10 0 10 20 30

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

LOG(GOLD) FDEFICIT TRADE

Figure 3

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147 2. Literature Review

Kolluri (1981), in his paper estimated the relationship between the returns on gold and expected or anticipated inflation. He has used Cochrane – Orcutt approach to find the variants of inflation using monthly data for the period 1968-1980. The author concluded that gold investment as good hedge for inflation. Koutsoyiannis (1983), concluded that the world wide economic conditions does not influence the movement of gold price movement in USA, the USA economy itself influence the gold price movement in USA. The author also found the negative relationship between the gold price movement and US Dollar. Sherman (1983) and Moore (1990) in his study concluded unexpected inflation and gold price movement has positive relationship where as Moore (1990) found the significant positive relationship between gold price movement and inflation. The author concluded that gold priced movements can be predicted by a leading indicator of inflation. Adrangi et al. (2003) as against to the conclusion of Sherman (1983) concluded that gold prices has positive relationship with expected inflation not with unexpected inflation.

Table-1 Descriptive Statistics

Log(GOLD)Log(CRUDE) Log(EXRATE)Log(FDEFICIT) TRADE Mean 6.352274 3.746473 3.801998 1.096520 -5.212305 Median 6.058889 3.799524 3.809712 1.106183 -2.385000 Maximum 7.511251 4.941642 4.185175 3.096407 0.390000 Minimum 5.544396 2.417698 3.445629 -3.486451 -20.21000 Std. Dev. 0.634965 0.670509 0.172134 1.132184 5.524972 Skewness 0.403808 -0.071494 -0.078702 -0.882542 -0.873596 Kurtosis 1.615032 1.576132 3.020291 4.254287 2.487304 Jarque-Bera 27.41741 21.84368 0.268669 45.12928 35.36574 Probability 0.000001 0.000018 0.874297 0.000000 0.000000 Sum 1626.182 959.0972 973.3115 253.2961 -1334.350 Sum Sq. Dev. 102.8110 114.6436 7.555653 294.8235 7783.957

Observations 256 256 256 231 256

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Ghosh et al. (2002), in their paper concluded that the gold prices influenced by the US inflation rate, interest rates and Dollar exchange rates. Ranson and Wainwright (2005) , in their paper concluded that among the available resources gold as best hedge against inflation. They also observed the gold prices increase on an average two to three times than that of increase in inflation. As per their analysis if the investor invests 18% of amount in gold constitutes optimal portfolio to hedge against inflation

Levin and Wright (2006), assessed the long relationship between the price of gold and the average price level in the US for the period 1975-2006 and found the long run relation between price of gold and average price level in the US. They observed that if the average price level in the US increases by 1% the Gold price also increases by 1%.

Using cointegration and error correction models they have assessed the long run relationship and short run dynamics respectively. They have concluded that the main drivers of gold prices in the short were changes in US inflation; US trade weighted exchange rates, fluctuation in inflation and credit risk.

Sjaastad (2008) found the dominance of European money market on gold price movement up to 1990 and after that the dominance of US Dollar. Though, previous studies concluded that gold was no more be treated as store of value where as in this study the author concluded that gold has store of value. Whereas Sjaastad and Scacciallani (1996), in their paper concluded that the appreciation and depreciation of European currency has a significant impact on the gold prices using the data for the period 1982-1990. . They also observed the minor influence of Dollar in the variations of the gold price.

Joscha and Robert Czudaj (2012), using data for four major economies such as the USA, the Euro Area and Japan assessed whether gold provides the ability of hedging against inflation from a new perspective. For this purpose they have for nonlinearity and discriminate between long - run time varying short run dynamics. Using markov - Switching vector error correction model (MS - VECM) approach for a sample period of January 1970 to December 2011, concluded that gold partially hedge against future inflation in the long run. They also concluded that the ability gold has more ability to hedge against inflation for the USA and the U.K. compared to Japan and Euro Area.

Finally, we present the studies which closely related to our topic analyzed the relation between Gold price, oil price and exchange rate. Zang et al. (2010) observed significant positive correlation between oil prices and gold prices. They also observed the long run equilibrium between gold market and oil market and found that change in oil price linearly Granger causes the volatility of gold price. K.S. Sujit and B. Rajesh kumar (2011), concluded that exchange rate highly affected by gold price and oil price, whereas stock market has a fewer role in affecting the exchange rate. Ashraf (2005), examined five cases and concluded that gold -oil ratio coincide with falling yield spreads, a falling dollar, peak fed funds rate and falling growth. Pravit (2009), observed the influence of Australian Dollar, Japanese Yen, US Dollar, Canadian Dollars, EU currency, Oil prices and gold future prices on Thai gold prices.

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149 3. Data and methodology

The present study employs monthly data on Gold prices, Crude Oil prices, Exchange Rate, Trade Deficit and Fiscal Deficit over the period 1994:01 to 2014:12. Before conducting static and dynamic analysis, certain pre-estimations like unit root and cointegration tests are required without which, conclusions drawn from the estimation may not be valid. Therefore, in the first step we have carried out a unit root test by applying Ng and Perron (hereafter NP) (2001) test and Kwiatkowski–Phillips–

Schmidt–Shin (hereafter KPSS) (1992) stationary test. (Detailed methodology presented in the appendix-I, Session-A ) After confirming from the unit root and stationary tests that all the variables are nonstationary in their levels form and stationary at first difference, i.e., (I), we proceed for co-integration analysis. For cointegration analysis, we have adopted Johansen and Juselius (1990) method which employs a VAR system to test for numbers of cointegration vectors. (Appendix-I, Session-B ) Further, we also used Granger-causality tests described in Lütkepohl (1991) namely, tests for Granger-causality and tests for instantaneous causality.

(Appendix-I, Session-C) 4. Results and Discussion:

We have used KPSS and Ng-Perron test statistics tests to find existence of unit, based on the unit root test results reported in table 2 and 3, we have performed Johnson’s cointegration to see whether any combination of the variables are cointegrated. The results are reported in table 4. It may observe from the table 4 the Gold prices exhibit long run relationship with chosen variables under the study.

Table-2. KPSS Test

Variable Level First Difference

Gold 1..773764 0.269954*

Crude oil 1.852889 0.135440*

Trade deficit 1.787446 0.062958*

Exrate 1.408803 0.067217*

Fdeficit 1.995893 0.140386*

* Rejection of null hypothesis Non stationary at 1% level of significance

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After checking the long run relationship among the variables questioned, we proceeded further to verify the short run relationship between the chosen variables. we have examined with a lag of 10 and 20 lag periods hoping such a period would be adequate to get the effects one variable on other. The results are reported in table 5 and table 6.

It may be observed from the table 5 that there exists a unidirectional influence on gold market and exchange rate, gold market and fiscal deficit, gold and trade deficit, crude and fiscal deficit, crude and traded deficit. It is also observed the bidirectional relationship between exchange rate and trade deficit, fiscal deficit and trade deficit.

The results reported in table 6 for a 20 lag period exhibits unidirectional relationship between gold market and exchange rate, fiscal deficit and gold market, crude and trade deficit. It is also observed the bidirectional relationship between fiscal deficit and exchange rate, trade and exchange rate, Trade and fiscal deficit.

Variance Decomposition:

We have reported variance decomposition results in Table-7 in the Appendix. Based on the results reported in Table-7, it is observed that crude oil prices are contributing 4.65% to the variance in the gold market and 93.54% variance in the gold market explained by gold market itself. The contribution of gold and trade deficit in the variance of crude oil market is 9.43% and 7.91% respectively. The variance in the exchange rate market explained by gold market and trade deficit are 9.73% and 12.23% respectively. Gold market, exchange rate market and trade deficit contributing to the variance in fiscal deficit by 1.77%, 2.75% and 2.45% respectively where as

Table-3 Ng-Perron test statistics

Variable Levels

MZa MZt MSB MPT

Gold 1.41177 1.44834 1.02591 79.2655

Crude oil -0.72872 -0.49340 0.67708 24.7772

Trade deficit -1.96543 -0.74787 0.38051 10.0989

Exrate 1.41177 1.44834 1.02591 79.2655

Fdedicit 2.26329 1.92251 0.84943 65.1009

First Difference

MZa MZt MSB MPT

Gold -124.704* -7.89519* 0.06331* 0.19839*

Crude oil -41.3318* -4.35384* 0.10534* 1.12065*

Trade deficit -198.425* -9.93998* 0.05009 0.15173

Exrate -124.704 -7.89519 0.06331 0.19839

Fdeficit 0.00727 0.01887 2.59678 338.075

* Rejection of null hypothesis of Non stationary at 1% level of significance

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92.42% % of the variance in the fiscal deficit explained by fiscal deficit itself. The variance in the trade deficit explained by gold market, crude oil market, exchange rate market and fiscal deficit are 11.32%, 5.29%, 4.40% and 1.68% respectively. It is also observed that the gold market contribute more (11.32%) to the variance in trade deficit.

It may be observed from table -8 the influence of oil on the movement of gold prices and no influence of Exchange rate, Fiscal deficit and trade deficit. However based on the chi-squire statistics, we have observed that overall the gold price was not influenced by the variables under consideration. The oil prices are influenced by gold prices as well as by trade deficit. It is also observed based on the overall Chi-Squire test that the exchange rate was influenced by all the variables under study. Based on the overall chi-Squire test it is also observed that both fiscal deficit and Trade deficit was influenced by all the variables under consideration.

Table 6 Pair wise Granger Causality Tests Lags: 20

Null Hypothesis: Obs F-Statistic Prob.

LOG(CRUDE) does not Granger Cause LOG(GOLD) 236 1.11025 0.3415 LOG(GOLD) does not Granger Cause LOG(CRUDE) 1.37994 0.1358 LOG(EXRATE) does not Granger Cause LOG(GOLD) 236 1.00271 0.4608 LOG(GOLD) does not Granger Cause LOG(EXRATE) 2.50060 0.0007 FDEFICIT does not Granger Cause LOG(GOLD) 236 1.66321 0.0422 LOG(GOLD) does not Granger Cause FDEFICIT 1.36379 0.1444 TRADE does not Granger Cause LOG(GOLD) 236 1.48682 0.0892 LOG(GOLD) does not Granger Cause TRADE 2.34383 0.0016 LOG(EXRATE) does not Granger Cause LOG(CRUDE) 236 0.80824 0.7019 LOG(CRUDE) does not Granger Cause LOG(EXRATE) 0.86357 0.6334 FDEFICIT does not Granger Cause LOG(CRUDE) 236 0.87056 0.6246 LOG(CRUDE) does not Granger Cause FDEFICIT 1.01479 0.4465

TRADE does not Granger Cause LOG(CRUDE) 236 1.11516 0.3365 LOG(CRUDE) does not Granger Cause TRADE 2.23559 0.0027 FDEFICIT does not Granger Cause LOG(EXRATE) 236 1.47258 0.0945 LOG(EXRATE) does not Granger Cause FDEFICIT 1.73447 0.0307 TRADE does not Granger Cause LOG(EXRATE) 236 2.92976 7.E-05 LOG(EXRATE) does not Granger Cause TRADE 2.78264 0.0002 TRADE does not Granger Cause FDEFICIT 236 1.78548 0.0244 FDEFICIT does not Granger Cause TRADE 3.06446 3.E-05

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Table 8

VEC Granger Causality/Block Exogeneity Wald Tests Dependent variable: LOG(GOLD)

Excluded Chi-sq df Prob.

LOG(CRUDE) 5.872909 2 0.0531

LOG(EXRATE) 2.558495 2 0.2782

FDEFICIT 0.581254 2 0.7478

TRADE 4.213428 2 0.1216

All 11.23292 8 0.1888

Dependent variable: LOG(CRUDE)

LOG(GOLD) 8.783193 2 0.0124

LOG(EXRATE) 0.586361 2 0.7459

FDEFICIT 0.086397 2 0.9577

TRADE 7.687307 2 0.0214

All 11.35977 8 0.1821

Dependent variable: LOG(EXRATE)

LOG(GOLD) 3.607376 2 0.1647

LOG(CRUDE) 1.163443 2 0.5589

FDEFICIT 0.207609 2 0.9014

TRADE 28.53154 2 0.0000

All 32.42916 8 0.0001

Dependent variable: FDEFICIT

LOG(GOLD) 16.05949 2 0.0003

LOG(CRUDE) 2.026418 2 0.3631

LOG(EXRATE) 12.01506 2 0.0025

TRADE 4.986728 2 0.0826

All 69.63890 8 0.0000

Dependent variable: TRADE

LOG(GOLD) 31.78045 2 0.0000

LOG(CRUDE) 8.124363 2 0.0172

LOG(EXRATE) 15.61743 2 0.0004

FDEFICIT 3.246859 2 0.1972

All 87.65786 8 0.0000

5. Conclusion and Discussion:

The purpose of this paper is investigating the relationship between gold market, crude oil market, trade deficit, exchange rate market and fiscal deficit. The results are very interesting indicating the growth in the prices of gold almost two times greater than the growth in crude oil price and exchange rate and based on VEC Granger Causality/Block Erogeneity, Wald Tests no influence of trade deficit, exchange rate,

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fiscal deficit and the crude oil prices on the prices of gold. Exchange rate, trade deficit, fiscal deficit was influenced by all the variables under consideration. Based on the variance decomposition analysis, it may be concluded that the variation in gold prices explained by gold itself by 93.4% where as the other variables influence on the variation of gold prices under consideration is negligible. Gold prices and trade deficit contributes 9.43% and 7.92% respectively in the variance of crude oil prices, where as the gold prices and trade deficit contributes 9.73% and 12.22% in the variance of the prices of exchange rate. The variance in the trade deficit was explained by variance in gold price is as high as 11.31% compared all the other variables under consideration.

6. Current Scenario and policy suggestion:

Current Scenario: The government of India on 8th November, 2016 demonetized the high denomination currency of INR 1000 and INR 500 to curb the block money in India. As per the study by Ambit Capital Research, The large portion of money invested in high value assets such as gold and real estate. Given that India’s GDP in calendar year 2016 is expected to be $2.3 trillion, the size of India’s black economy is about USD 460 billion (over Rs 30 lakh core), which is larger than the stated GDP of emerging markets like Thailand and Argentina,”. The black money made the cost of capital outside the formal banking channels as high as 34%. The study said the size of the India’s black economy expanded rapidly over the 1970s and 1980s, but since then had been contracting at a gradual pace and is now estimated at around 20 per cent of the country’s GDP. Based on the discussion in our paper it is evident that none of the variables under consideration are influencing the rise in the prices of gold and gold demand in India. Moreover the large portion of black money is invested in Gold and real estate market. Though the government of India has come up with the following policies, unless government India take steps which are discussed in policy suggestion it may be difficult to achieve the targets.

Buying Gold: Buying gold not more than 500grams for wife, 250 grams for each son and daughter, it is not clear about the size of the family. Anything more than INR.250000/- transaction to buy gold the individual have to show the evidence of Income. The government of India should cap the limit of members constituting a family unit otherwise the demand for gold in India cannot be controlled. This coupled with the policy initiatives on real front should ward of the black money evil.

Entire transaction to be through digital: It is felt that the government of India should look at infrastructure facilities especially electricity and telecommunication in the rural India. The government has to come up with a policy to mitigate the problem otherwise the demand for gold will not decrease as well as it will be difficult control rise in the prices of gold. If the government of India utilizes the tax collected on black money through the disclosure scheme and the government utilizes the tax collected for the development of said rural infrastructure the problem may be mitigated to the large extent.

Policy Suggestion:

It is suggested that government of India should cap the limit of members constituting a family unit otherwise the demand for gold in India cannot be controlled. This coupled with the policy initiatives on real estate front should ward of the black money evil.

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Recent statistics released by the World Bank highlight (http://www.thehindu.com/opinion/blogs/blog-datadelve/article7130386.ece). 19% of the population is not having access to Banking and most of the accounts under financial inclusion are dormant. The World Bank also highlighted that "these accounts had no cash deposits or withdrawals, but also that it had no electronic wage deposits and no electronic payments or purchases”, the paper said. India, As per the statistics till in rural India 35.73% of the villages are not having access to electricity or without electricity. Based on the statistics the telecom penetration in India is poor especially in rural areas. Unless the government of India takes steps to mitigate the said problems, demonetization as a move to cashless or digital economy becomes a dream.

References:

Adrangi, B., Chatrath, A., Raffiee, K.,(2003), “Economic activity, inflation and hedging: The case of gold and silver investments”, The Journal of Wealth Management 6, 60-77.

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

Ghosh, D., Levin, E.J., Macmillan, P. and Wright, R.E. (2002). “Gold As An Inflation Hedge?”, University of St. Andrews, Department of Economics, Discussion Paper Series.

Jocha Beckman, Robert Czudaj (2012), RUHR Economic Papers, Ruhr - Universitat Bochum (RUB), Department of Economics, Germany.

Kolluri, B.R.,( 1981), “ Gold as a hedge against inflation: an empirical investigation”.

Quarterly Review of Economics and Business 21, pp. 13–24

Koutsoyiannis, A. (1983). “A Short-Run Pricing Model for a Speculative Asset, Tested with Data from the Gold Bullion Market, Applied Economics, 15, 563–581

K.S. Sujit and B. Rajesh Kumar (2011), Study On Dynamics Relationship Among Gold price, Oil Price, Exchange Rate and Stock Market Returns, International Journal of Applied Business and Economic Research, Vol.9, No.2, 145-165.

Levin, E.J., Wright R.E., 2006, Short run and long run determinants of the gold price.

World Gold Council, London

Moore,G.H., (1990), gold prices and a leading index of inflation, Challenge33, 52-56 Pravit Khaemusunun (2009), Forecasting Thai gold price, http://www.wbiconpro.com/3-pravit.pdf

Sjaastad, L.A., (2008), The Price of Gold and the Exchange Rates: Once Again, http://www.business.uwa.edu.au/school/disciplines/economics/?A=98660

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

Sherman, E.J.,1983, A gold pricing model, Journal of Portfolio Management 9, 68-70.

Zhang, Yue-Jun, Wei Yi-Ming (2010), The Crude Oil market and The gold market:

Evidence for Cointegration, Causality and price Discovery, Sep, Vol.35, Issue 3, P.168-177

Appendix on line at the journal Website: http://www.usc.es/economet/eaat.htm

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155 Appendix-1 : Methodology

Session-A: Unit Root Tests

We have carried out a unit root analysis by applying Ng and Perron (hereafter NP) (2001) test and Kwiatkowski–Phillips–Schmidt–Shin (hereafter KPSS) (1992) stationary test. In all cases, we will test the unit root (or stationary) property of the variables by employing the model suggested by the graphical plot of the study variables. To avoid the problem of serial correlation appropriate lag length is chosen based on Schwarz Information Criteria (hereafter, SIC). The null hypothesis for NP tests is that the series is nonstationary that is series has a unit root and if critical value exceeds the calculated value in absolute terms (less negative terms) null hypothesis will not be rejected implying that that series is nonstationary. However, the null hypothesis of KPSS test is that the series is stationary.

Session-B : Cointegration Tests

After confirming from the unit root and stationary tests that all variables are nonstationary in their larval form and stationary at first difference, i.e., (I), we proceed for co-integration analysis. For cointegration analysis, we have adopted Johansen and Juselius (1990) method which employs a VAR system to test for numbers of cointegration vectors. Its estimation procedure is based on Maximum Likelihood (ML) method. Following Johansen and Juselius (1990) VAR representation of column vector Xt can be written as follows:

t i t k

i i t

t Bz X

X    _ 



 ⁽ ⁾ 1

)

( …… (1)

where Xt is a column vector of 4 endogenous variables (i.e., gold price, crude oil pirce, Trade deficit, Exchange rate and Fiscal deficit), z is a (4×1) vector of deterministic variables, ε is a (4×1) vector of white noise error terms and Πi is a (4×4) matrix of coefficients. Given that our all four variables are nonstationary in their level form, VAR of such models are generally estimated in first-difference forms. Following Johansen and Juselius (1990), the first differencing of the equation 1 in form of VECM specification, can be specified as follows:

t i t i

t k

i i t

t Bz X X

X      

 _ _





⁽ ⁾

1 )

( ………

(2)

where













1

1 k

i i

i and I

k

i

i









1

Equation 2 differs from standard first-difference version of a VAR model only by the presence of ΠXt-k term in it. This term contains the information about the long run equilibrium relationship amongst the variable in Xt. Where, ∆xt are all I(0) endogenous variables, ∆ indicates the first difference operator, Ψi is a (4×4) coefficient matrix and

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Πi is a (4×4) matrix whose ranks determines the number of cointegrating relationships.

The Johansen and Juselius (1990) cointegration test is to estimate the rank of the Π matrix (r) from an unrestricted VAR and to test whether we can reject the restrictions implied by the reduced rank of Π. And if the rank of Π is reduced, even if all variables are individually I(1), the level-based long-run component would be stationary. The appropriate modelling methodology here is VECM. Further, in case of reduced rank of Π i.e., (0 < r < 4) then there exists (4×r) matrix of α and β such that:

Π = αβ^T ……… (3)

where r represents the number of cointegrating relationships amongst the endogenous variables (i.e., gold price, crude oil pirce, Trade deficit, Exchange rate and Fiscal deficit)) included in Xt, α is a matrix of error correction parameters that measures the speed of adjustment in ∆Xt. Which indicates the speed with which the system responds to last period’s deviations from the equilibrium relationship and β is the matrix of long run coefficients which contains the element of r cointegrating vectors and has the property that the elements of β’Xt are stationary.

Johansen and Juselius (1990) have demonstrated that the β matrix which contains the cointegrating vectors can be estimated as the eigenvectors associated with the r largest eigenvalue of the following equation:

0 /

)

( ₀ ₀₀ 

 S S S S_kk _k _ko

 …….. (4)

where S00 contains residuals from a least square regression of ∆Xt on ∆Xt-1,…, ∆Xt-k+1, Skk is the residual matrix from the least square regression of Xt-1 on ∆Xt-k+1, and S0k is the cross-product matrix. These eigenvalues can be used to construct a Likelihood Ratio (LR) test statistic in order to find the number of cointegrating vectors.

JJ test provides two Likelihood Ratio (LR) test statistics for cointegration analysis.

First test is trace (λtrace) statistics and the second one is maximum eigenvalue (λmax) statistics. These are specified as follows:











N

r i

i

trace r T

1

ˆ) 1 ln(

)

( 

 …….. (5)

and ˆ ) 1 ln(

) 1 ,

max(r r T 



_r_₁



……. (6)

where r is the number of cointegrating vectors under the null hypothesis and ˆ_iis the estimated value for the i^th ordered eigenvalue from the matrix Π. The trace statistics tests the null hypothesis that the number of cointegrating relations is r against of k cointegration relations, where k is the number of endogenous variables. The maximum eigenvalue test, tests the null hypothesis that there are r cointegrating vectors against an alternative of r+1 cointegrating vectors.

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To determine the rank of matrix Π, the test values obtained from the two test statistics are compared with the critical value from Mackinnon-Haug-Michelis (1999) which differs slightly from those provided by Johansen-Juselius (1990). For both tests if the test statistic value is greater than the critical value, the null hypothesis of r cointegrating vectors is rejected in favor of the corresponding alternative hypothesis.

Once the cointegration is established, we moved to test the Granger-causality analysis using the VECM framework which, for two variables X and Y, may be explained as follows: (1) write the VECM based equation for two variables as follows:

 

 



    









k

i

k

i

t x t x i

t i x i

t i x x

t ECT

1 1

, , ,

,  



 ….. (7)

 

 



    









k

i

k

i

t y t y i

t i y i

t i y y

t ECT

1 1

, , ,

,  



 ... (8)

The null hypothesis (H0) for the equations (7) is



^

k

i i

H₀: x_, 0suggesting that the lagged terms of ∆Y do not belong to the regression i.e., it do not Granger cause ∆X.

Conversely, the null hypothesis (H0) for the equations (8) is



^

k

i i

H₀ : y_, 0, suggesting that the lagged terms of ∆X do not belong to regression i.e., it do not Granger cause ∆Y. The joint test of these null hypotheses has been tested through Wald Chi-square (χ²) test. This Wald Chi-square (χ²) test gives us an indication of the

‘short-term’ causal effects or strict exogenity of the variables.

If the coefficients of



_x,_iare statistically significant, but



_{y ,}_i are not statistically significant, then X is said to have been caused by Y (unidirectional). The reverse causality holds if coefficients of



_{y ,}_i are statistically significant while



_x,_iare not. But if both



_{y ,}_iand



_x,_iare statistically significant, then causality runs both ways (bidirectional). Independence is identified when the



_x,_i and



_{y ,}_i coefficients are not statistically significant in both the regressions. The coefficient associated with

t

ECTx_, test the long-run causality from Y to X and coefficient associated withECT_y_,_ttests the short-run causality from X to Y. Further, non-significance of any of the ‘differenced’ variables which reflects only the short-term relationship, does not involve a violation of theory because, the theory typically has nothing to say about short-term relationships. Since in our case lag interval is (1, 1) therefore, Wald Chi- square (χ2) test is not needed and significance of the variables can be tested through the t-test only.

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Session-C: Granger-causality and tests for instantaneous causality

Further, we also used Granger-causality tests described in Lütkepohl (1991) namely, tests for Granger-causality and tests for instantaneous causality. We will discuss both tests very briefly here. For both types of tests the vector of endogenous variables is divided in two subvectors,y₁_tand y₂_twith dimensions K₁and K₂, respectively, so thatK K₁K₂. The subvector y₁_tis said to be Granger-causal for y₂tif it contains useful information for predicting the latter set of variables. For testing this property, a model of the form

is considered. In this model setup, y₁_tis not Granger-causal for y₂_tif and only if ,

, 0

21_i 

 i 1,2,...,p.

Therefore this null hypothesis is tested against the alternative that at least one of the

i ,

21 is nonzero. A Wald test statistic, divided by the number of restrictionspK₁K₂, is used in conjunction with an F(pK₁K₂,KT n^*)distribution for testing the restrictions. Here n^*is the total number of parameters in the system (see Lütkepohl 1991), including the parameters of the deterministic term. Of course, the role ofy₁_t andy₂_tcan be reversed to test Granger-causality from y₂_tto y₁_t.

The test is problematic if some of the variables are nonstationary (integrated). In that case the usual asymptotic distribution of the test statistic may not be valid under the null hypothesis. Therefore, the test should be performed in the VEC framework if there are integrated variables in the system of interest. Instantaneous causality is characterized by nonzero correlation of u₁_t and u₂_t. Thus the null hypothesis

0 ) ' ( : ₁ ₂

0 E u_tu _t 

H

is tested against the alternative of nonzero covariance between the two error vectors in testing for instantaneous causality. We used the Wald test described in Lütkepohl (1991, Sec. 3.6.3). If there are exogenous variables in the model, the analysis is carried out conditionally on these variables. In other words, a model

is considered and the tests are carried out on the



coefficients and the covariance ofu₁_t and u₂_tin this model.

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Seshaiah,S.V., Sarma,I.R.S., Tiwari,A.K. Evaluation Of Gold Market In India and Its Price

159

Variance Decomposition of LOG(GOLD):

Period S.E. LOG(GOLD) LOG(CRUDE) LOG(EXRATE) FDEFICIT TRADE 1 0.046379 100.0000 0.000000 0.000000 0.000000 0.000000 2 0.061501 98.57112 0.234922 0.517421 0.076328 0.600204 3 0.073924 98.42592 0.413140 0.579025 0.072358 0.509556 4 0.084316 97.95815 0.780271 0.700285 0.058605 0.502686 5 0.093661 97.43803 1.235151 0.769895 0.052958 0.503966 6 0.102218 96.80165 1.792341 0.832879 0.048490 0.524645 7 0.110242 96.07766 2.427261 0.883176 0.044635 0.567265 8 0.117867 95.28322 3.123699 0.922011 0.041107 0.629965 9 0.125180 94.43247 3.868039 0.951390 0.037757 0.710342 10 0.132244 93.53786 4.648496 0.973119 0.034604 0.805923

Variance Decomposition of LOG(CRUDE):

Variance Decomposition of LOG(EXRATE):

Variance Decomposition of FDEFICIT:

Variance Decomposition of TRADE:

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Table 4. Multivariate Cointegration (Trace Statistics) Lags interval (in first differences): 1 to 4

Unrestricted Cointegration Rank Test (Trace) at 0.05 level of significance

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.274407 140.6690 69.81889 0.0000 At most 1 * 0.140342 60.15665 47.85613 0.0023 At most 2 0.060532 22.20025 29.79707 0.2875 At most 3 0.021093 6.527501 15.49471 0.6331 At most 4 0.004677 1.176594 3.841466 0.2780

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Table 5

Pairwise Granger Causality Tests Lags: 10

Null Hypothesis: Obs F-Statistic Prob.

LOG(CRUDE) does not Granger Cause LOG(GOLD) 246 1.36031 0.2001 LOG(GOLD) does not Granger Cause LOG(CRUDE) 1.49062 0.1439 LOG(EXRATE) does not Granger Cause LOG(GOLD) 246 0.52886 0.8688 LOG(GOLD) does not Granger Cause LOG(EXRATE) 2.24956 0.0161 FDEFICIT does not Granger Cause LOG(GOLD) 246 1.35373 0.2033

LOG(GOLD) does not Granger Cause FDEFICIT 4.60192 6.E-06

TRADE does not Granger Cause LOG(GOLD) 246 1.56698 0.1177

LOG(GOLD) does not Granger Cause TRADE 4.38386 1.E-05

LOG(EXRATE) does not Granger Cause LOG(CRUDE) 246 1.15372 0.3236 LOG(CRUDE) does not Granger Cause LOG(EXRATE) 0.87634 0.5561 FDEFICIT does not Granger Cause LOG(CRUDE) 246 0.42770 0.9322 LOG(CRUDE) does not Granger Cause FDEFICIT 1.64467 0.0953 TRADE does not Granger Cause LOG(CRUDE) 246 1.41706 0.1737

LOG(CRUDE) does not Granger Cause TRADE 3.42096 0.0003

FDEFICIT does not Granger Cause LOG(EXRATE) 246 2.01058 0.0333 LOG(EXRATE) does not Granger Cause FDEFICIT 1.57823 0.1142 TRADE does not Granger Cause LOG(EXRATE) 246 4.43968 1.E-05

LOG(EXRATE) does not Granger Cause TRADE 3.66363 0.0001

TRADE does not Granger Cause FDEFICIT 246 6.84658 3.E-09

FDEFICIT does not Granger Cause TRADE 5.87805 7.E-08

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