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The financial markets are well known for information sensitiveness, which has been greatly influenced by the development of information technology and policy of free trade in the modern world. Information generated by these markets guides the allocation of the resources and helps in placing the risk where it is more suited. The wider coverage and least time involved in processing the information are some of the important parameters to measure the reliability of market information, and it is true in the case of stock market. The development of Information and Communication Technology (ICT) has provided the necessary infrastructure to enable flow of information seamless and borderless, instantaneous and almost costless. These developments gave impetus to the integration of various financial markets in the post-WTO global world. The recent past has also witnessed the interests of academicians and practitioners to conduct empirical studies examining the mechanism through which stock market movements are transmitted around the world (Joshi, 2011).
Many studies in the past have focused to evaluate how the news generated in one international stock market influences the volatility process of other markets. The stock market is always sensitive to both good and bad news. This particular nature of the market plays an important role in arbitrage trading strategies, portfolio management, risk management and framing regulatory policies. Thus, understanding the linkages between different financial markets can provide valuable insights to portfolio managers and financial institutions regarding returns’ predictions and an opportunity for exploiting various trading strategies (Lo and MacKinlay, 1990). The measurement of second moment has been the focus of empirical finance. Volatility, as measured by the standard deviation or variance of returns, is often considered as a crude measure of the total risk of financial assets (Brooks, 2002). Therefore, while referring to international stock market integration, researchers not only analyze the return causality linkages, but also measure the volatility spillover effects.
Volatility spillovers, also termed as ‘contagion’, refer to the spread of market disturbances from one country to another and to the co-movements in the stock prices, exchange rates, or capital flows (Dornbusch et al., 2000). The wide range of linkages has attracted the interest of market participants to study the financial disorder spreading from one financial market to another, generating the ‘contagion’ phenomenon (Xiao and Dhesi, 2010). The recent episodes of ‘contagion’ witnessed by the world echoed in the form of the Asian stock market crisis in 1997, the Russian default in 1998 and the subprime mortgage financial crisis of 2007. These episodes propagated and affected not only the countries and their neighboring parts from which these originated, but also distant markets all over the world. The salient feature of this phenomenon is the closer alignment of markets during the period of financial distress. Therefore, the analysis of financial market integration, co-movement and degree of correlation between the financial assets reveals important gradients which help in making many financial decisions for various market participants.
In financial markets, the returns are always accompanied by risks, which create fear in the minds of the investors. Therefore, the information on risk measured in terms of volatility becomes an important input for making investment decisions. The present study aims at analyzing the spillover phenomenon between India and selected world markets. A distinct dimension has been given to this study by analyzing the relationship of Indian financial markets with the world stock markets in bivariate framework. The spillover phenomenon is measured by using a new measure of risk popularly known as ‘implied volatility index’.
Implied volatility is the level of dispersion in the asset price changes that is embedded in the market prices of option contracts written on that asset. As such, it represents market participants’ consensus on the expected volatility, or uncertainty regarding future returns, over the remaining life of the options. In 1993, Chicago Board Options Exchange proposed a proxy measure for the aggregate stock market implied volatility, known as volatility index, the VIX. This index soon became a benchmark for measuring the future volatility of American markets. Thus, seeing the utility and characteristics of this index for the investors, many stock exchanges around the world introduced their own implied volatility index. Each stock market of different countries has their own index with a different name. For instance, volatility index of France is VCAC, Switzerland is VSMI, Germany is VDAX-NEW, India is India VIX (henceforth IVIX), Korea is VKOSPI and Hong Kong is VHSI. Eurex has a
market index covering 50 stocks from major markets in 12 Eurozone countries known as EURO STOXX 50; they have developed VSTOXX as a benchmark for measuring European market volatility.
In April 2008, the National Stock Exchange of India launched India VIX, which is based on the option prices of the Nifty index. This index depicts the expected market volatility over the next 30 calendar days, i.e., the higher the India VIX values, the higher the expected volatility and vice versa.
There are a few studies which have investigated the linkages of Indian markets mainly with the American and Japanese markets using only the stock index returns. These studies suggest that information flows from US and Japanese markets towards the Indian markets. The studies led by Rao and Naik (1990), Kumar and Mukhopadhyay (2002), Hansda and Ray (2002 and 2003), Nair and Ramanathan (2002), Nath and Verma (2003), Kaur (2004), Mukherjee and Mishra (2006) and Kiran Kumar and Mukhopadhyay (2007) have mainly focused on the interdependence patterns among the various financial markets using the stock prices, exchange rates and other macroeconomic variables. They have explored the linkages using the causality tests, cointegration and univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. These authors have not taken into account the interactions in terms of volatility among the markets. The present study makes a novel attempt to explore the interdependence patterns between Indian and international financial markets using a new representative of risk, i.e., implied volatility index.
In the past few decades, the focus of researchers has gradually shifted towards the implied volatility indices of various financial markets. They are interested in studying its characteristics, properties and their association with similar kind of indices of various financial markets. Aboura (2003) initiated the research on international volatility transmission by using implied volatility indices (VX1, VDAX and VIX). They captured the interactions between implied volatility of different markets using the multivariate-GARCH framework and mean-reverting jump diffusion model. They found a strong correlation between the American and French markets. Wagner and Szimayer (2004) investigated the transmission of shocks for the US and German implied volatility indices using mean reversion model that allows for Poisson jumps. Äjiö (2007) presented the stock market integration by examining the implied volatility term structures between the VDAX, VSMI and VSTOXX volatility indices. In 2011, Badshah examined the dynamic implied volatility transmission across the volatility indices (VIX, VXN, VDAX and VSTOXX) using the Granger causality, generalized impulse response functions and the variance decomposition method. Turhan and
Çevik (2009) investigated the effect of CBOE VIX on 15 emerging stock markets using the GJR-GARCH model. They found that these markets have leverage effect in conditional variance and bad news increases the volatility in the market. Sarwar (2012) analyzed that VIX acts as an investor fear gauge for the equity markets of US, Brazil, India and China.
Peng and Ng (2012) analyzed that correlations of stock indices and volatility indices increase during the periods of financial crisis and their results support the existence of financial contagion.
The above discussion points out that the academicians have started analyzing the volatility spillovers using this new measure of risk for developed countries. Adequate attention in the area of implied volatility index for the developing countries like India is lacking. Thus, the present study contributes to the literature on volatility contagion in several aspects: firstly, in this study, a new index, i.e., implied volatility index, is used for measuring the future uncertainty, volatility spillover and transmission phenomenon. Secondly, the authors have focused on studying the implied volatility spillovers using the volatility indices as they can help in understanding how option prices of one asset tend to move from one market to another. Finally, the study of correlation structure will help in determining the correlation of future option prices.
On the basis of the above discussion, the interdependence patterns between India and world markets are measured by analyzing the implied volatility transmissions. For this purpose, the Indian implied volatility index and six international implied volatility indices (VIX, VCAC, VDAX-NEW, VSMI, VXJ and VSTOXX) are used (countries’ details mentioned in Table 1). Further, the Granger causality, generalized impulse response functions and variance decompositions techniques are used for empirical analysis. The volatility spillover phenomenon is studied with the volatility indices, i.e., IVIX and VXJ, VIX, VCAC, VDAX-NEW, VSMI and VSTOXX, by utilizing the synchronized daily observations. The bivariate GARCH framework is employed to quantify the volatility spillover effects related to Indian market.
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