Gold as an alternative Investment Instrument during Uncertainty

Gold as an alternative Investment Instrument during Uncertainty Dr. Bibhu Prasad Sahoo Assistant Professor, Sri Guru Tegh Bahadur Khalsa College, University of Delhi, Delhi 110007 Email id: bibhusahoo2000@yahoo.co.in Mobile No. +919810829720 Ankita Gulati Assistant Professor, Sri Guru Tegh Bahadur Khalsa College, University of Delhi, Delhi -11000709 Key Words: Gold, Investment, Market and Inflation JEL Classification: E22, A11, F31, L64

Gold as an alternative Investment Instrument during Uncertainty Abstract Gold, among the various asset classes, is considered to be the most attractive investment by an investor. It is a symbol of supremacy and prosperity. It plays an important role in the social and economic life of people. It offers an effectual hedge against inflation and change in interest rates. Many global investors invest in gold as it is appreciating over the years and is also used to diversify their risk due to global economic unanticipated changes. With the feature of being a liquid asset it also has the most efficient store of value. It is used as a medium that can be pledged easily during difficult times for securing financial accommodation. There is a sudden rise in the demand for gold in India over the last few years. Gold is not only purchased in physical form but also in a demat form i.e. e-gold. With the growing importance of gold, the investors are interested in getting supernormal profits. This report aims to study the return pattern of gold. Also it will deal with as to how investors can earn supernormal profits by timing their investment decision. This can also be reported that if there is any seasonality in gold returns i.e. whether there is significantly higher return in some parts of the year than others.

I. Introduction Financial planning consists of allocation of given assets in such a way that a given investor increases his return appetite and is able to undertake risk. The objective of asset allocation is to plan an investment in an asset or a class of asset such that it best meets the need and objective of the investor. This allocation decision depends upon various factors. Investor who are risk averse and are looking for stable returns tend to invest in debt instruments. However, those who are risk friendly and are willing to expose their investments to higher risk will allocate their funds to equity (ownership) investments. Asset allocation involves the best blend of these instruments as per the needs and requirements of the investor. In India among all the precious metals, gold is considered to be the most popular investment. The investors use gold as an investment to diversify it. Also, gold market is subject to speculation as forward, futures and other derivatives contracts in gold are also offered. Gold price shows a long term correlation with the crude oil price and other economic indicators. The gold performance is often compared to stock prices because of their due their elementary differences. Gold is considered as a store of value with no growth by some investors. On the other hand, stock is considered to be offering high growth i.e. gains because of increase in its real price plus dividend. Thomas Sowell (2010) in his book Basic Economics argued that, in the long-term, gold is high volatile as compared to prices of stocks and bonds. The presence of seasonality in gold returns may exist due to numerous reasons. In Indian context final accounts are prepared at the end of March. So chances are there that we witness higher returns in April as compared to other months. Also, the possible reasons for seasonality to exist could be: The post-monsoon wedding season in India and Diwali, one of the country s most important festivals; Restocking by jewellery makers in advance of the Christmas shopping season in the United States; The holy month of Ramadan in the Muslim world, whose end in late September is marked by a period of celebration and gift-giving. In the weddings gold is considered to be the most desirable gift item. Also, gold is considered to be a significant investment due to continuous high return it offers and it can be passed as an ancestral property. II. Theoretical Background Gold and other metals unlike stocks and bonds are complicated. Stocks, currencies and other commodities depend upon the basic data of the country s physical demand and supply. If we

have the elementary data about these stocks and commodities, even a raw investor can understand its movement in prices. However, pricing of gold becomes difficult because it depends upon the value of other assets. Also, it is valued by the relative U.S data and the rest of the world. This makes the fundamental drive to learn about gold price movement a difficult task even for expert market players. Some of the analyst built their argument to buy or sell gold on the basis of current demand and supply in the market. Some of them base their argument on the explosion of public debt or on real interest rates. Since it is a tedious task to predict about gold price these analysts sometimes say the presence of manipulation done in the market. However, in India the gold loan market has shown rapid changes. Initially gold loans were provided by money lenders and other intermediaries to people from all walks of life. But now there is a shift of gold loan lenders from the unorganised to a more organised sector such as banks or specialised non-banking financial institutions. With rapid increase in the number of institutions involved in offering gold loans, the gold loan market has reached new dimensions. Factors affecting the gold prices in India There exist indirect pricing of gold as it depends on the demand and supply of other commodities. The price of gold also depends upon the level of inflation in the U.S. The public debt and trade imbalances with the U.S affect the gold pricing in India. The monetary policy undertaken by the RBI is also one of the drivers affecting its prices such as printing of notes or gold purchases or sale. The real interest rates in the US also affect the determination of gold price. The international price of gold has increased exponentially in the last few decades. Since 2000, the global gold prices have grown at compound annual growth rate of 16.3%. The domestic gold prices have stirred in an upward direction with international gold prices in recent years. Instability in international gold prices in these years is positively skewed. This implies that it offers fewer losses and greater gains. The reason of increase in gold demand in recent years is because of rising investment bin gold at global level. The continuous rise in the prices of gold prices could not reduce the demand of gold in India making it one of the largest importers of gold in the world, implying that gold demand in India is becoming price inelastic. Due to rising prices of gold and its import demand the trade balance of the country gets adversely affected. The worsening of current account deficit (CAD) is due to huge gold imports. This CAD has to be financed which in turn lead to decrease in the foreign exchange reserves and become a haul on the external debt.

III. Literature Review The literature on gold is concentrated on its price movements and its volatility. Researches are done to find out the factors affecting the gold returns and prices. Also, how gold can be used a hedge against inflation is understood. In addition to these a the univariate asymmetric power GARCH (APGARCH) model to analyse the asymmetric volatility of gold. In the previous research Tandon and Urich (1987) concluded that the PPI announcements that US government makes and the unexpected components of money supply has a significant impact on the gold prices. Bailey (1988) found that the increasing gold volatility is due to the unexpected weekly increase in the announced level of money supply. Kitchen (1996) studied how the federal deficit projections changes with the changes in domestic and international financial variables. Over the period 1981 1994 the researcher found a positive and significant impact of these changes on the announced changes in federal deficit projections. The gold is also considered to play the role of hedge against inflation. According to Lawrence (2003), there does not exist any significant relation between in certain macroeconomic variables such as inflation, GDP, interest rates and golf returns. Sjaastad and Scacciavillani (1996) concluded that gold can be used store of value against inflation. Baker and Van-Tassel (1985) related the price of gold with expected inflation rates. Sherman (1983) reported the log of gold price is directly linked to expected and unexpected inflation. According to Kaufmann and Winters (1989) the changes in the US inflation rates is one of the important factors affecting gold prices Ball, Torous et al. (1982) examine the morning and afternoon price of gold in the London metal exchange over the period of 1975-1979. They found little indication of either a daily seasonal or a Monday effect (negative). This analysis is independent of whether Monday returns are measured as Friday AM Monday AM or Friday PM Monday PM. McKenzie et al. (2001) applied the univariate power ARCH (PARCH) model to precious metals futures contracts which are traded at the London Metal Exchange (LME). He found that there are no asymmetric effects present, and the model also could not provide sufficient explanation of the data. Tully and Lucey (2007) explored the univariate asymmetric power GARCH (APGARCH) model to analyse the asymmetric volatility of gold. They established that the gold s volatility is due to exchange rate fluctuations in addition to some other macroeconomic variables. Batten and Lucey (2007) used the univariate GARCH model to examine the volatility of gold futures contracts which are traded on the Chicago Board of Trade (CBOT) with the intraday and interday data. They also used Garman and Klass (1980) nonparametric volatility static model to provide further understanding of volatility dynamics of gold. By using both the measures they concluded that there are significant variations within and between consecutive time gaps. Also,

they found that there exist low correlation between the volume and volatility of gold futures contracts. Christie-David et al. (2001) examined that how monthly macroeconomic news release affect the gold and silver future markets for period of 1992-1995. They concluded that gold strongly respond to announcements regarding CPI, GDP and personal income. IV. Research Methodology To examine the gold return seasonality in India, first we measure return of Gold as given below: R t = [ln(p t ) ln(p t-1 )] * 100 Where R t is the log return in period t, P t and P t-1 are the monthly (daily) closing prices of the Nifty at time t and t-1 respectively. Logarithmic returns are often used by academics in their research. The main advantage is that the continuously compounded return is symmetric, while the arithmetic return is not: positive and negative percent arithmetic returns are not equal. Arithmetic and logarithmic returns are not equal, but are approximately equal for small returns. The difference between them is large only when percent changes are high. It is also important to test stationarity of a series lest OLS regression results will be spurious. Therefore, we will first test whether Nifty return is stationary by Augmented Dickey Fuller (ADF) model. Dickey-Fuller test involve estimating regression equation and carrying out the hypothesis test. The simplest approach to testing for a unit root is with an AR(1) model. Let us consider an AR(1) process: Y t = C + ρy t-1 + ε t Where c and ρ are parameters and is assumed to be white noise. A white noise process is a random process of random variables that are uncorrelated, have mean zero, and a finite variance.if 1< ρ< 1, then y is a stationary series while if ρ = 1, y is a non-stationary series. If the absolute value of ρ is greater than one, the series is explosive. Therefore, the hypothesis of a stationary series is involves whether the absolute value of ρ is strictly less than one. The test is carried out by estimating an equation with Y t-1 subtracted from both sides of the equation: ΔY t = C + γy t-1 + ε t Where γ = ρ 1, and the null and alternative hypotheses are H0: γ= 0 H1: γ< 0

We will next conduct a test for seasonality in gold returns. We use a month-of-the-year dummy variable for testing monthly seasonality. The dummy variable takes a value of unity for a given month and a value of zero for all other months. We specify an intercept term along with dummy variables for all months except one. The omitted month, that is January, is our benchmark month. Thus, the coefficient of each dummy variable measures the incremental effect of that month relative to the benchmark month of January. The existence of seasonal effect will be confirmed when the coefficient of at least one dummy variable is statistically significant. Our initial model to test the monthly seasonality is as follows: R = α + β 1 X February + β 2 X march + β 3 X april + β 4 X may + β 5 X june + β 6 X july + β 7 X august + β 8 X September + β 9 X october + β 10 X november + β 11 X December + error Where R means returns on Nifty 50 X February takes value 1 if month is February, 0 otherwise X march takes value 1 if month is March, 0 otherwise X april takes value 1 if month is April, 0 otherwise X may takes value 1 if month is May, 0 otherwise and so on Hypothesis For the first model the hypothesis are as follows H o : β 1 = β 2 = β 3 = β 4 = β 5 = β 6 = β 7 = β 8 = β 9 = β 10 = β 11 = 0 H 1 : atleast one of them is different than 0. V. Empirical Results and Analysis Gold Price As it was mentioned gold was considered to be the most lucrative investment so far for an Indian investor. It has gone through various ups and downs. The gold price per 10gm over the last 30 years is shown. The figure explains the rising price of gold from the year 1971 to 2013.

Figure 1: Trends of Gold Price during 1971-2013 We can see that there are various peaks in the prices of gold. Initially gold prices were not too high but with course of time it has increased. The first was during the early 1980 s and then the price of gold is showing an increasing trend. During the year 1995-96 again a substantial increase in the prices can be seen. There was a sharp increase in the price during 2006-07. However the price of gold crossed over Rs.20000 after 2010. Currently we are witnessing gold price around Rs.30000. Gold returns Graphical Analysis The graph below plots the log returns in percentage terms for gold over the period from 1971 to 2013. The graph below shows that the returns series is stationary one. Figure 2: Log returns for gold during 1971-2013

We can see clearly that the series is stationary in the sense that the mean value for the returns is near 0 and the variance above and below the mean level is constant. In other words the fluctuation in the return of gold is deviated around a straight line. As the series is stationary we can proceed to do further econometric analysis to derive our regression results. Table1: Regression Analysis SUMMARY OUTPUT Regression Statistics Multiple R R Square Standard Error 0.123883 71 0.015347 17 0.021708 81 Observations 516 ANOVA

df SS MS F Significance F Regression 11 0.004 0.000 0.714 0.725 Residual 504 0.238 0.000 Total 515 0.241 Coefficie nts Standard Error t Stat P-value Intercept 0.008 0.003 2.460 1% Feb -0.003 0.005-0.568 57% Mar -0.008 0.005-1.699 9% Apr -0.008 0.005-1.623 11% May -0.003 0.005-0.556 58% Jun -0.004 0.005-0.774 44% Jul -0.004 0.005-0.875 38% Aug -0.003 0.005-0.544 59% Sep 0.002 0.005 0.497 62% Oct -0.003 0.005-0.629 53% Nov -0.004 0.005-0.924 36% Dec -0.003 0.005-0.723 47% Source: Authors own estimation At 10% level of significance the p value of the intercept i.e. for the month of January and March is less than the significance level. Therefore we can say that returns are significantly different from 0 in the month of January and March. The overall fit of the model as given by the adjusted R-square is on the lower side and the F statistic for the model is not significantly different from 0.

As expected in the Indian gold market this effect has been witnessed in the month of March. A possible explanation to this could be that India follows the financial year trend and March is the month of payment of taxes. The result of regression analysis of log monthly returns on monthly dummy variables hence proves that seasonality does exists in the Indian gold market. Hence the investors can benefit by timing their entry in the Indian markets accordingly. Average Monthly returns Figure 3: Average Monthly Returns We can see in the graph above that the returns for gold as an asset class are low in the months of March and April. However, the month of September is showing the highest demanddue to marriage season and Diwali which comes to an end and hence are significantly different from 0. Also the returns are abnormally high in the month of January. Descriptive Statistics Following is the descriptive statistics for the entire period and each month. The return in each month varies widely. Return in September is the highest while lowest in March. The return in entire series is largely deviated as shown by large values of standard deviation and variance. In the month of January the returns are highly deviated while lowest in December.

Table 2: Descriptive Statistics Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total Mean 0.8% 0.5% 0.0% 0.1% 0.6% 0.5% 0.4% 0.6% 1.0% 0.5% 0.4% 0.5% 0.5% Standard Error 0.5% 0.3% 0.4% 0.3% 0.3% 0.3% 0.3% 0.3% 0.4% 0.2% 0.3% 0.3% 0.1% Median 0.00% 0.04% 0.25% -0.20% 0.34% 0.45% 0.35% 0.60% 0.50% 0.41% 0.16% 0.02% 0.24% Standard Deviation Sample Variance 3.43% 1.82% 2.58% 1.67% 1.88% 2.15% 2.18% 2.02% 2.42% 1.62% 1.92% 1.71% 2.16% 0.12% 0.03% 0.07% 0.03% 0.04% 0.05% 0.05% 0.04% 0.06% 0.03% 0.04% 0.03% 0.05% Kurtosis 10.68 1.28 2.79 0.20 0.12 2.56 3.66 0.97 2.24 5.02 0.65 4.19 7.52 Skewness 2.82 0.68-0.21 0.36 0.62 0.98 1.13-0.09 0.70 1.59 0.68 1.81 1.38 Range 19.4% 9.1% 14.9% 7.5% 7.7% 11.3% 12.2% 10.0% 13.1% 9.0% 8.5% 8.3% 23.7% Minimum -2.9% -3.4% -7.2% -3.1% -2.8% -4.8% -3.7% -4.8% -5.0% -2.1% -2.8% -2.0% -7.2% Maximum 16.5% 5.7% 7.8% 4.4% 4.8% 6.5% 8.5% 5.2% 8.1% 6.9% 5.7% 6.3% 16.5% Sum 35% 24% 1% 2% 24% 19% 17% 24% 45% 22% 16% 20% 251% Count 43 43 43 43 43 43 43 43 43 43 43 43 516 Source: Authors own estimation

VI. Conclusion The paper shows that gold returns show a seasonal behaviour by outperforming in the months of January and March, hence investors can earn abnormal returns by timing their entry or investments in gold. It is well known that the demand for gold in India is influenced by many social, economic and cultural factors. The price of gold, rural income distribution, quantum of black money, rate of return on alternate financial assets and the general price level are major driving factors for gold demand in India. The performance of gold against other comparable domestic assets over the last few years is suggestive of the shift towards gold in India also. Changes in the manner of how one can invest in gold may also have an impact on the seasonal nature of gold returns that was just explored in the paper. Up till now, gold prices have been majorly driven due to physical demand of the asset i.e. buying and selling by the stockists. But recently in the wake of sharply rising prices, we are witnessing the rise of what is known as E gold and gold ETF s. These are very different from the traditional methods of buying gold where in you do not have to pay the full amount of the asset and neither do you need to hold it on physical form. It is similar like mutual funds where one can pay in small amounts, buy in small units instead of complete coins, bars or biscuits. These new modes of investment will bring in new retail investors and also increase the demand (physical and investment) manifolds. This may have a two way impact either it may strengthen the seasonal nature of returns as people may invest only in lean seasons and cashing when prices tend to rise or it may spread the investments throughout the year in the form of regular small amounts and there by removing any anomalies or seasonality from the gold returns. This remains to be seen but as of now gold has out shined various asset classes and people will continue to invest till the time the market offers them to make safe seasonal returns. VII. References Basic Economics: A Common Sense Guide to the Economy (4th ed.). Cambridge, Mass: Perseus Books Group. ISBN 978-0-465-02252-6. Garman M.B., M.J. Klass, 1980, On the Estimation of Security Price Volatility from Historical Data, Journal of Business, 1980, Vol. 53 (No. 1), 67-78. Ball, C.- Torous, W. Tschoegl, A. (1982), "Gold and the Weekend Effect", Journal of Futures Markets, Vol. 2, No.2, pp.175-82. Sherman, E.J., 1983. A gold pricing model. J. Portfolio Manage. 9, 68 70. Baker, S. A., van Tassel, R.C., 1985, Forecasting the Price of Gold: A Fundamentalist Approach, Atlantic Economic Journal, vol. 13, pp. 43-51.

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