INTRODUCTION:

Volatility index usually has been considered as a fear gauge, a measure of market sentiment and risk.VIX is a forward looking index. Volatility is said to be affected most often by external factors which can be classified into political, economic, technological, and social. Uncertainty could certainly be an important factor in driving business cycles(Bloom, Floetotto, Jaimovich, Saporta-eksten, and Terry, 2013).Chicago Board Options Exchange (CBOE) holds the trademark of volatility ‘VIX’. NSE has been granted license from Standard and Poor (SandP), which in turn has reserved permission from CBOE, for usage of such mark in the name of the India VIX.

India VIX is constructed on the NIFTY Index Option prices and takes into account the best bid-ask prices of NIFTY Options contracts to derive a volatility figure (%) which demonstrates the expected volatility in the market over the next 30 calendar days. In India, scholars have used techniques like GARCH analysis to model the conditional volatility of market returns(Slivka, Gao, and Ren, 2015).

Gold has been usually well-thought-out as a safe bet for a long time. India has been a major importer of gold, largely in the form of gold Dore bars. India’s fascination with the glittering yellow metal, Gold, has always attracted people whether it’s for the purpose of liquidity, security or portfolio. Gold import in India reached 955.16 tonnes in 2017 (India, 2018). It is seen as a safe buy during periods of economic slump and mayhem. Imports are contributing towards widening of current account deficit which has become an enormous matter of concern for the Government of India. Thereby, Sovereign gold bonds are often as a means of controlling the current account deficit (Tilve and Uma, 2016)

India, the flourishing economy, which overtook Japan, to be the world’s third-biggest oil importing nation, after the U.S. and China, imports around 81% of its oil requirements (Chaudhary, 2016).Crude oil consumption (in thousand barrels per day) has increased by 52.42% since 2008(BP, 2018). Low elasticity of import demand of crude oil with respect to oil prices in international markets further corroborates the statistics (Goldar and Mukhopadhyay, 1990). In the international markets too, Gold and Crude Oil are the most trading commodities (Manu and Agarwal, 2018).

LITERATURE REVIEW:

In the past, much research has been done on volatility index. Six year data for India VIX has been taken into consideration from year 2011. It was found out that India VIX which has become a popular instrument to quantify market sentiments has been consistent for the past 6 years (2011-17). People fear investing in stocks when markets are falling but unless stocks become sensitive to multiple external factors, VIX can’t be a popular instrument (Dr. T. Dulababu, 2016). On the contrary, a research aimed at finding the correlation between India VIX and NIFTY arrived at the conclusion that India VIX can be very crucial to decide strategies in stock market trading(Bantwa, 2017). It was further discovered that stock market instability in India was due to political instability in Middle East and related areas (Chaudhuri and Ghosh, 2015). It is also indicated that India VIX is an unquestionable winner to measure volatility and can act as a viable risk management tool (Thenmozhi and Chandra, 2013). Another study revealed that India VIX represents all the important concepts such as volatility clustering, mean reversion and positive relation with trading volumes. More importantly, market and India VIX returns are negatively related when market goes down but not when market goes up(Kumar, 2010). Volatility Index (VIX) is the latest indicator to gauge investor panic (Whaley, 2008).

Research has been done to find the cross sectional relationship amongst volatility, oil prices and gold prices. ARCH/GARCH model are extensively used to measure volatility and are considered as risk management tools, especially used for portfolio optimization (Engle, 2001). Higher volatility is responsible for increasing the risk of assets and higher conditional correlations increase the risk in portfolios. ARCH/GARCH is a model of time-varying volatilities and is important to determine risk factor (Matteson and Ruppert, 2011). Further, because of use of conditional variance, these models are considered to be effective in predicting several economic phenomenon (Bollerslev, 1986)

This paper is an extension of the work of B. Ghosh where a credit crash model was constructed in the German context (Ghosh, 2017). It is seen that higher the market volatility, lower tends to be the prices of stock (Whaley, 2008). Further, stock market volatility affects the capital formation because volatile market conditions do not support fresh equity issues (Chaudhuri and Ghosh, 2015). A significant correlation is said to exist between commodity and stock markets. The link is further strengthened by the financial crisis of 2007-08. Increasing presence of commodities like gold in the portfolio highlights investors’ mindset(Creti, Joëts, and Mignon, 2012). Many studies have rather found a pretty strong evidence of returns in case of oil and gold futures being affected by implied volatility. Adding to that, investors successfully forestall changes and do not have to ‘catch-up’ on using commodities as a way to safeguard themselves (Jubinski, Lipton, and Joseph, 2013).

Gold has often been seen as a safe bet during times of turmoil (as seen in the 1987 stock market crash in US and Canada)(Baur and Mcdermott, 2010)which is further confirmed by Beckmann, Berger and Czudaj(Beckmann and Berger, 2017). Bhunia found that there is a tendency to move to gold in moments of financial crisis(Bhunia, 2013). Gold’s panic buy effect is mostly observed in the stock markets of developed countries(Baur and Mcdermott, 2010). Gold prices are said to granger cause stock market returns and vice versa in India(S. K. Mishra, 2010). Gold is used as a proxy to forecast stock returns in Chinese economy (El Hedi Arouri, Lahiani, and Nguyen, 2013).Gold investment is used as a hedge against inflationary conditions in the market, however it depends upon the rate of inflation and gold price(Worthington and Pahlavani, 2006). Using the GMM model, it was discovered that asset transfer to gold is seen during volatility in the case of BRICS and UK(Ghosh, Roux, and Ianole, 2017).

However, Mukhuti and Bhunia (2013) found out that there existed no cointegration relationship between price of gold and stock market indices in India using bivariate cointegration test, but then again, multivariate cointegration test indicates that there is a steady relationship between the two (Mukhuti and Bhunia, 2013). Another study attempted to find that correction in gold prices do not have a major impact on financial system as nature of the ownership and bubble state of gold are the factors that can help determine it (R. N. Mishra, 2012).

Many studies have been done to find the causal link between oil price and uncertainty in Indian market. Co-movements between VIX and crude oil series have also been detected (Zhang, Chevallier, and Guesmi, 2017). However, Montasser et al (2014) used Granger causality and established that oil price changes and economic uncertainty in India are not correlated. Numerous empirical studies in this context helped substantiate a strong link between oil prices and economic indicators(Ready, 2013). Owing to parameter instability, Rossi’s (2005) test which is robust to instability was further conducted and the results were pretty interesting. It shows that there is no causal link between Brent Crude Oil price and economic uncertainty in India, however, Crude oil ETF volatility does have an impact on uncertainty in Indian market (Montasser and Clark, 2014).A study on Istanbul (crude oil importing nation) showed that investors must not see oil prices as a factor to determine uncertainty in the market using a quantile-regression approach (Tekin, 2017). Also, an extensive research shows the link between oil price and stock market, where oil tremors affect stock index returns. Stock index returns have an impact on oil futures markets too(Ciner, 2002). European evidence suggests that aggregate demand oil price shocks affect both current and future volatility conditions in the market, however, supply side dynamics do not influence volatility(Degiannakis, Filis, and Kizys, 2014). There is said to be a negative impact of oil prices on stock markets of all developing countries(Raza, Jawad, Shahzad, Kumar, and Shahbaz, 2016). VAR-GARCH method has been applied and it was revealed that global oil market plays a crucial role in making forecasts in Chinese equity market(Dutta, 2018). Moreover, it has been found out that volatility in oil price increases risk for future cash flows which eventuallyposes a negative impact on stock market returns (Dupoyet and Shank, 2018). Uncertainty in oil prices has a major impact on investment decisions; this is often observed in net oil importing nations where equity markets are at nascent stage (Raza et al., 2016). However, a study conducted on stock markets in Turkey proved that oil prices are not a dynamic factor affecting the BIST index(Tekin and Hatipoolu, 2017).

VARIABLES:

The dependent variable is IVC (close of India VIX). Here, we are predicting India VIX with the help of external variables like GC (Gold Close Price) and COC (Crude Oil close Price). Since all the variables are satisfying the comparability matrix, cross sectional analysis can be performed using ARCH/GARCH methodology. Logarithm of IVC (LIVC) has been taken to reduce the standard error.

Here, kurtosis is 3.148278 which clearly depicts that India VIX is highly volatile and risky in nature.

Also,

H_O: Log of India VIX is not showing a Gaussian distribution i.e. the distribution is not normal.

H_a: Log of India VIX is showing a Gaussian distribution.

As the probability value is 0, the null hypothesis has been rejected; alternative hypothesis has been accepted. Therefore, the distribution is normal or Gaussian in nature, though the intensity is low.

ARCH/GARCH:

Least square assumes that the expected value of residuals is the same when squared (Homoskedasticity). If the expected value of residuals is not the same when squared (Heteroskedasticity). Deficiencies of least squares are rectified in ARCH/GARCH and the prediction of variances is computed for residuals. This is the focus of ARCH/GARCH models(Engle, 2001).

Table 2: ARCH/GARCH Results

Variable	Coefficient	Std. Error	z-Statistic	Prob.
C	3.213665	0.012531	356.4644
COC	0.006389	8.47E-05	75.46829	0.0000
GC	-0.000608	8.72E-06	-69.71217	0.0000
C	-0.000892	0.003208	-0.278106
RESID(-1)^2	1.003873	0.0095462	10.51599	0.0000
GARCH(-1)	-0.016185	0.030036	-0.538849	0.5900
LIVC	0.002615	0.001002	2.610322	0.0090
COC	6.82E-07	1.07E-05	0.063468	0.9494
GC	-3.10E-06	1.03E-06	-3.013364	0.0026
Akaike Info Criterion		-0.513751
Schwarz Criterion		-0.491280
Hannan-Quinn Criter.		-0.505558

Modelling volatility; Source: Compiled by the authors in EViews 9

Probability Statistics:

Testing the correlation between GC and LIVC and linking the objective with hypothesis.

H₀: GC can’t determine LIVC

H_a: GC can determine LIVC

The probability value for ‘GC’ is 0. Null hypothesis has been rejected; alternative hypothesis has been accepted. Therefore, GC can predict LIVC.

And, Testing the correlation between COC and LIVC and linking the objective with hypothesis.

H₀:COC can’t determine LIVC

H_a’: COC can determine LIVC

The probability value for ‘COC’ is 0. Null hypothesishas been rejected; alternative hypothesis has been accepted. Therefore, COC can predict LIVC.

Robustness of the Model:

Akaike info criterion (AIC), Schwarz criterion (SC) and Hannan-Quinn criterion (HQ) are penalties that reduce errors. Low AIC score here depicts that less penalty can reduce errors to a large extent.Here, Akaike info criterion, Schwarz criterion and Hannan-Quinn criterion are closer to 0 (less than 5). Therefore the model is accurate and sustainable, in other words, the results are consistent and can be predicted.

Table 3: MAPPING OF EVENTS

	Dates	Events
1	18^th November, 2008	Global financial crisis of 2008
2	26^th March, 2009	Quantitative easing
3	6^th- 26^th May, 2010	Flash Crash
4	5^TH October, 2011	Moody downgrades SBI and European debt crisis
5	27^th March, 2012	RBI optsfor repo rate cut by 50 basis points first time in three years
6	29^th August, 2013	Rupee hit a record low, fear of subsidy burden after food bill passage
7	26^th May, 2014	Narendra Modi sworn in as 14^th prime minister of India:Market confidence high with stable govt.
8	25^th August, 2015	Sharp selloffs by foreign investors, rupee fell, concern about Chinese stalling economy led to global fall in index
9	12^th February, 2016	Fear of global slowdown, fall in profits of banking, power and realty sector in India
10	24^th- 27^th June, 2016	BREXIT result
11	16^th November, 2016	Demonetisation
12	4^th July, 2017	GST rollout

Major events from November 2008 to July 2017 correlated with Conditional S.D. Graph; Source: Compiled by the authors

Source: Compiled by the authors in EViews 9

Figure 2: Conditional S.D. graph showing brief periods of volatility

Analysis of Equation:

Estimation Equation:

LIVC = C(1) + C(2)*COC + C(3)*GC

GARCH = C(4) + C(5)*RESID(-1)^2 + C(6)*GARCH (-1) + C(7)*LIVC + C(8)*COC + C(9)*GC

Substituted Coefficients:

LIVC = 3.21366533278 + 0.00638867147815*COC - 0.000607819849959*GC---- (1)

GARCH = -0.000892040426376 + 1.00387268638*RESID(-1)^2 - 0.0161847055462*GARCH(-1) + 0.00261535361026*LIVC + 6.81622179553e-07*COC - 3.10144311444e-06*GC-----(2)

Equation (1) gives us an understanding of the relationship among India VIX, crude oil price and gold price. Analysis of the equation reveals that crude oil price has positive correlation with India VIX, while Gold price is negatively correlated with India VIX.

Equation (2) reveals the presence of volatility in Indian market. Intensity of correlation of crude oil price and gold price is really low. Further, the volatility of India VIX is explained by the lag and residual of India VIX itself.

CONCLUSION:

Forecasting volatility has gained importance in financial markets. For this reason, GARCH methodology has been employed, although due to the trade-off between generality and feasibility, it is difficult to estimate GARCH models on large data sets (Perry, 2015).

This paper is an endeavour to examine the volatility using Gold and Crude Oil price (Closing). The intensity of log of India VIX (LIVC) is low; however, the model to predict India VIX using Gold and Crude Oil prices is robust in nature. The main take away from this paper is that though gold is considered as a safe bet in tumultuous time, it’s intensity on volatility is almost negligible. Similarly, crude oil also has a very low impact on India’s volatility index. It is worth noting that this runs counter intuitive to our understanding of gold and crude as a proxy for indication of fear. This is a case of “Reverse Anchoring”(Ghosh et al., 2017). Hence it is not possible to uncover the complex volatility that persists in the Indian market by taking into account only Gold and Crude Oil prices. On one hand, some studies show that the relation between stock market and macroeconomic variables might not be fairly strong (Saeed, Chowdhury, and Akhter, 2006). On the contrary, the other side believes in the efficient market hypothesis which explains that if the market is efficient, it responds to the vital macroeconomic information releases (Srinivasan, 2017). Therefore, volatility has been mapped with various macro-economic factors like political stability across the world and movement of Rupee in the financial markets.

Thus, the results of this paper could have an impact on investor’s understanding of Gold and Crude Oil as proxy to measure risk, and can play an important role to safeguard against wrong assumptions and anchoring bias.

Further scope for research exists on Rupee futures and its correlation with volatility index.

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Received on 21.05.2019 Modified on 27.06.2019

Asian Journal of Management. 2019; 10(3): 161-166.

DOI: 10.5958/2321-5763.2019.00026.X