They found bidirectional volatility spillovers between the BSE Sensex and the exchange rate. They divided the sample period into three phases in context of the global financial crisis, namely pre-crisis, crisis and post-crisis periods. They reported bidirectional asymmetric volatility spillovers between the two markets in the pre- and post-crisis periods and unidirectional volatility spillovers from forex market to stock market during the crisis period.

They also stated that the asymmetric spillover effect was more pronounced during the post-crisis period and attributed the impact of positive news from crisis recovery as the possible reason for the same. Majumder and Nag studied the relationship between the forex and the stock markets in India by employing a VAR-EGARCH model on daily data from April to September and found asymmetric unidirectional volatility spillovers from stock market to the forex market. By splitting the period into pre-crisis, crisis and post-crisis periods, they found bidirectional volatility spillovers between the two markets in the crisis and post-crisis periods.

The asymmetric effect was observed in the spillover from stock market to forex market, and not the other way. Jebran and Iqbal found unidirectional asymmetric volatility spillovers from stock markets to the forex market in India. Mitra found evidence of bidirectional volatility spillovers and a long-term relationship between stock prices and the exchange rate in India.

The review of the literature suggests existence of volatility spillovers between forex and stock markets in many countries including India and other emerging market economies. Our study is different from the previous studies for India in two aspects. First, we have divided the sample into various sub-periods based on the level of volatility observed in the exchange rate in order to undertake a more meaningful analysis of the spillover effects during various sub-periods.

September 1, to December 31, This period was the peak of the global financial crisis which was marked by heightened volatility in financial markets across the globe. May 23, to September 4, Fed Taper Tantrum : The global financial markets witnessed massive volatility post-announcement of tapering of quantitative easing programmes by the US Federal Reserve.

These two sub-periods are studied separately with the objective of assessing the extent of volatility spillovers during such periods, amplifying the stress in the financial markets. Second, the studies conducted for India so far have mostly used univariate approaches, except Apte and Majumder and Nag , to investigate volatility spillovers. The univariate GARCH models may not be suitable for studying this kind of interaction in view of the bidirectional linkages between the two markets as established in the empirical literature.

The development of the Indian forex and stock markets was greatly facilitated by the economic and financial sector reforms introduced since the early s. Rangarajan, constituted in the backdrop of the balance of payment crisis in , recommended several reform measures. The major reform in the forex market was the shift to market-determined exchange rate.

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Sodhani, recommended several measures with respect to trading, participation, risk management and selective interventions by the Reserve Bank of India RBI for promoting an orderly development of the Indian forex market. Consequently, the forex market underwent wide-ranging reforms starting January Regarding equity markets, the most important reform measure undertaken in the early s was the repealing of Capital Issues Control Act, , which paved the way for market-determined pricing of primary capital issues.

Subsequently, the book-building system was introduced to improve transparency and fairness in the pricing of primary issues. Over the years, several measures have been taken for strengthening equity market infrastructure, namely, replacing the open outcry system with screen-based, on-line, order-matching trading platforms; strengthening the settlement system with establishment of depositories; shortening the settlement cycle; and introduction of electronic funds transfer facilities.

Trading in derivatives such as futures and options on stock indices as well as individual stocks was introduced to provide more avenues for hedging of equity price risk.

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The above measures have improved the efficiency of the secondary market leading to greater liquidity and better price discovery. Further, the increased participation of domestic institutional investors has provided greater stability to the Indian stock market from onwards. The integration of the Indian forex and stock markets was greatly facilitated by the liberalisation of foreign portfolio investments.

FPIs were allowed to invest in Indian equities for the first time on September 14, , but with various restrictions. With gradual liberalisation over the years, only a few restrictions exist currently. The magnitude of capital flows has increased considerably over the period with highest net portfolio equity inflows recorded during As can be seen from Chart 1 , net FPI investments in Indian stock markets have picked up in a big way starting Increased FPI investments in Indian stock markets have led to their increased participation in the Indian forex market.

FPIs participate in the forex market for covering their currency exposures and also for hedging the currency risks in their investment portfolio. The increased FPI activities have facilitated synchronous movements of both markets. It shows inverse movements in stock indices and exchange rate, exhibiting a correlation of - 0. On the other hand, increased FPI participation in the Indian stock market had a negative effect in terms of rise in volatility in exchange rate and stock indices caused by sudden surge and pause in portfolio flows. The surplus rupee liquidity on account of large forex purchases by the RBI was sterilised primarily by issuing of government securities and treasury bills under the Market Stabilisation Scheme MSS.

Subsequently, with the collapse of Lehman Brothers, the exchange rate came under massive depreciation pressure and exhibited heightened volatility as the global financial crisis unfolded with full vigour in September Though the signs of the sub-prime crisis were visible since , it took the shape of a full-blown crisis after the Lehman failure. The large impact of the crisis on the Indian markets was felt from September to December During this period, global risk aversion and deleveraging led to capital flows reversal, especially portfolio investments, from the EMEs.

The rupee experienced annualised daily volatility of The RBI also took a number of administrative measures, including, among others, the following: provision of a rupee-US dollar swap facility for Indian banks with branches abroad to mitigate their short-term funding pressure; special window to meet the foreign currency requirements of oil marketing companies; measures to encourage capital inflow, viz.

The above extraordinary measures by the RBI could calm down the heightened volatility in the forex market by December The next episode of very high exchange rate volatility was seen during the period after the announcement of tapering off the quantitative easing famously known as Fed Taper Tantrum by the US Federal Reserve on May 22, US bond yields spiked in response to the announcement resulting in narrower interest differentials with respect to EMEs.

This led foreign portfolio investors to pull out their investments, especially debt investments, from EMEs including India. The accelerated capital outflows coupled with the prevailing weak macroeconomic situation led to a sharp fall in the rupee by about The RBI resorted to several monetary and administrative measures to curb the excessive volatility in exchange rates Pattanaik and Kavediya, It hiked the overnight Marginal Standing Facility MSF rate by basis points; limited the overnight injection of liquidity to 0.

The strong measures taken by the RBI coupled with the forward guidance released by the US Federal Reserve in September could arrest the heightened volatility in the forex market. The stylised facts described above clearly show the two distinct phases of heightened volatility in the Indian forex market, viz. The daily returns exhibit a wide range of 2. The standard deviation is much higher in both periods compared to the others.

A similar observation can be made from Chart 3 showing these two periods with highly volatile movements in the daily exchange rate returns. There was also a spike in volatility during November-December due to the Eurozone debt crisis, but it was for a short period. The objective of this study is to examine volatility spillovers between the Indian forex and stock markets.

Accordingly, we use three variables, viz. The continuously compounded daily returns, i. Though both the indices exhibit strong correlation of 0. Therefore, we have used both the indices as a robustness check of our results. These two periods are well-known for their market-wide impacts, and, therefore, we have taken these two periods separately in this study, similar to the approach followed in Wu , Choi, Fang and Fu , Majumder and Nag , and Xiong and Han 2.

The overall sample period is divided into five sub-periods as given in Table 2. Table 3 presents descriptive statistics of all the three variables for the full sample period, which suggest that stock markets are more volatile than the forex market. Average daily returns are positive in both the markets and return distributions are positively skewed.

Positive skewness indicates that positive returns are more common than negative returns. Kurtosis, a measure of the magnitude of extremes, is substantially higher than 3 and thus leptokurtic for both stock return as well as exchange rate return. Furthermore, the Jarque—Bera test rejected the null hypothesis of normal distribution for all the variables.

All these statistics confirm that returns in both markets are not normally distributed. The descriptive statistics of the sub-samples suggest similar findings, except that the Jarque—Bera test did not reject the null hypothesis of normal distribution in sub-sample II, possibly due to small sample size.

To check stationarity, unit root tests were conducted for all the variables, both for the full sample and all the sub-samples, using Augmented Dicky—Fuller ADF and Phillips—Perron PP tests. The results confirmed stationarity of all the variables, both in the full sample and all the sub-samples, at 1 per cent level of significance 3. The dynamic linkages between stock market and foreign exchange market are examined in terms of the conditional second moments of their distributions, termed as volatility spillover.

We specify and estimate a multivariate model using daily data on stock and exchange rate returns that allows the possibilities of different types and categories of news simultaneously affecting the conditional variances. Before proceeding to estimate volatility spillovers, we employ Granger causality tests Granger, to examine the possible endogeneity between these variables 4.

This section describes the specific model employed to examine the spillovers and its econometric properties as well as the estimation strategy. The Granger causality test involves estimating the following pairs of equations in a vector autoregression VAR framework:. A standard VAR is suitable where the residual vector is assumed to be a white noise process with time invariant covariance matrix.

A variant of ARCH, i.

We estimate a variant of MGARCH model, which also takes into account the asymmetric specifications of Nelson and the multivariate extension proposed by Kroner and Ng The specification for conditional mean equation in VAR p form is:. The conditional variance equation is expressed in terms of BEKK specification, which ensures positive semi-definiteness of the conditional variance matrix and is less cumbersome to estimate, with the advantage of estimating less number of parameters:.

To incorporate asymmetric responses of volatility in the variances and covariances, the above model can be further extended as proposed by Kroner and Ng :. In the above model, the off-diagonal parameters in matrices A and G measure volatility spillover between markets while the diagonal parameters in those matrices capture the effects of their own past shocks and volatility. The diagonal parameters in matrix D measure the response to own past negative shocks while the off-diagonal parameters d ij show the response of one market to negative shocks in the other market, to be called hereafter as cross-market asymmetric responses.

A negative value of D means negative news tend to increase volatility more than the positive news.