1. INTRODUCTION

The Financial market has experienced a few financial disasters in the last three decades, and as the level of volatility change frequently there is more need to hedge against these risks to make the financial market attractive for investment. In the financial literature, there are many measures of forecasting volatility for e.g. historical measures, Time series measures especially-GARCH and E-GARCH models, stochastic volatility models and implied standard deviations which measure through option prices, but the informational content of these forecasting measures varies with the sample forecast, type of asset and with the different time horizons (Poon and Granger, 2003). The academics and practitioners are indulged in forecasting volatility, as it is widely used in the asset pricing, portfolio and risk management, security valuation and monetary policy making. To devise its monetary policy, the Federal Reserve in the United States, consider the volatility of various financial assets (Nasar, 1992). The efficient market hypothesis supports the unpredictable behaviour of the market to forecast volatility, therefore to predict future realized volatility, the role of implied volatility as a forecasting tool in financial markets has been gaining much importance in the field of research. Apparently, an accurate forecasting of volatility may generate profit and minimise the risk (Carr and Wu, 2006) In theory and practice as well, implied volatility is calculated through two methodologies, one is model-based which is calculated through the option pricing model i.e. Black-Scholes model and another is model-free methodology. Implied volatility as the name suggest, it is based on what the market is “implying” the expected volatility of the stock will be in future, based on price changes in an option. It is the expected volatility of future returns over the remaining period of options expiration and also it is forward looking measure since it is based on options prices instead of historical measures of volatility which are based on the past data. The option traders’ focus on implied volatility than historical volatility to take the investment decision, as implied volatility is better and unbiased predictor of future realized volatility (Latane and Rendleman, 1976). The inspiration to develop the implied volatility index emerged soon after the beginning of exchange traded options in 1973. Since, the notion was primarily developed by Gastineau (1997) followed by Cox and Rubinstein (1985), Brenner and Galai (1989) and Whaley (1993). But, at the outset in 1993 CBOE (Chicago Board of Options Exchange) launched its own volatility index, i.e. VIX based on the work of Whaley (1993), which is a model based calculation of implied volatility through Black and Scholes option pricing model of eight S and P 100 index options. It provides a forward looking estimate of market determined volatility of nearly one-month.

Demeterfi et al. (1999) propounded a new methodology to construct the implied volatility index by using the variance swaps and the same methodology was adopted by CBOE in 2003 to construct VIX. The methodology suggested by Demeterfi et al. (1999) is a model-free approach of calculating the future value of variance by pricing variance swaps. The new approach uses two nearby maturities of S and P 500 call and put options prices calculated through weighted average sums of both OTM and ATM across all available strikes to calculate the real- time market volatility.

Moreover, the Volatility Index measures the investors’ expectation of volatility or the expected uncertainty over 30-day period implied by option prices. It tends to raise when the prices of the financial asset move sharply up and down and it declines as the prices become stable or the volatility subsides. As the movement of the index is correspondingly based on the demand for the options prices i.e. call and put options on the underlying index. But above all the spike in the index is witnessed when there is high demand of OTM and ATM puts by the risk averse investors or hedgers so as to insure their portfolio from market turmoil. Therefore, it also termed as investor fear gauge. This index is calculated through the order book of options as an annualised percentage (NSE, 2010). The purpose behind the proposed index was to reflect the attitude of investors about future volatility by measuring it through the options which replicate the sentiments of the investor. From the beginning, it has become the tool for both investors and traders to measure the future volatility which helps to take the investment decision. The present study aims at exploring the relevance and informational content of the implied volatility index through reviewing empirical and conceptual literature for a wide set of volatility indices and also to give future directions for research.

2. APPLICATIONS OF VOLATILITY INDEX:

The volatility index is called as a barometer of investor fear, which describes the investor sentiment towards the market. VIX as investor fear gauge is reported by Whaley (2000), as the high degree of market turmoil leads to high VIX levels. From this observation, one can diversify their risk by investing in volatility class assets as both are negatively associated and also the asymmetric relation exist shows that the downside risk is more pronounced in the volatility index. Simon (2003) discussed how the market is pessimistic, which could lead to a subsequent rally in stock prices in the future. If the VIX rank is high, then the investors are pessimistic, which gives a boost to stock prices in the future. On the other hand, if IV rank is low, then investors are optimistic towards the market, which makes fall in stock prices in the future. But, the research conducted by Copeland and Copeland (1999) analysed it as a market timing tool, on the basis of size and style which helps in asset allocation strategy. The forecasting of Volatility index to predict future returns can also be analysed by examining the oversold and overbought market conditions. Through the levels of volatility index, one can identify the buying and selling opportunities so that to take the long or short position in the market to make profitable investments (Giot, 2005). IV index derivatives can be considered as an underlying for hedgers to hedge the risk in investment (Brenner et al., 2006). It serves as a hedging instrument, as it has a negative relationship with the contemporaneous returns and volatility derivatives can be used to hedge market risk in which the volatility index is an underlying asset. In this context, On March 26, 2004 CBOE launched the futures on VIX and on February 24, 2006 list the options on VIX. The prime aim of the paper is to endow with a comprehensive literature review of all widely and academically accessible volatility indices, which are well thought-out the most important tool to forecast future realized volatility, but also for asset management and portfolio construction as well.

The layout of remaining paper is as follows. After discussing the introduction and applications in section 1 and section 2 respectively, section 3 contains the objective and methodology of the study. Section 4 explains the theoretical framework by dint of a figure elaborating a brief mapping which reviews the literature on the informational content of implied volatility indices. Section 5 contains the results and discussion of the paper by explaining it through a summary table and section 6 illuminates the implications, following by section 7 which describes areas for future research to further explore the concept.

3. OBJECTIVES AND METHODOLOGY:

The primary objective of this paper is to provide an in-depth understanding of the informational content and applications of volatility index. Data have been secondary in nature and research studies published in various national or international journals have been used. To examine the existing body of literature on implied volatility, covering the majority of the literature on informational efficiency and predictive ability of the implied volatility index, an information search was made on popular database (e.g., Google scholar, Research gate, academia.edu, Knimbus, Scopus, EBSCO, Proquest, emerald-insight, Science Direct, SSRN and so on) together. In line with the objective of the study, all those published studies specifically dealing with implied volatility index from the period 1964 to 2017 were selected. In general, almost all important studies on implied volatility were covered in order to gain an understanding of the stylized facts of the volatility index and to find the lacunae in the extant literature.

4.1 Informational Content of Implied Volatility to Predict Future Realized Volatility:

Implied volatility could be considered as the market’s expectation of volatility at a particular instant in time. It can approximate the market expectation of the average volatility (Latane and Rendleman, 1976) and also outperforms as compared with the other historical models. Therefore, considered it as a forecast of future realized volatility and also evaluate how sound it can really forecast future volatility.

Latane and Rendleman (1976) is one of the first to investigate the informational efficiency of implied volatility to predict future realized volatility. They concluded that implied volatility, which is an ex-ante volatility, offer a better estimate of variability in future return than the ex-post measure of standard deviations calculated from historical data. They have initially conducted the study on cross-sectional weighted average implied volatility for 24 companies using closing prices whose options were traded on the CBOE. They found that the weighted average implied volatilityis significantly associated with the actual variability of stock price and also it better forecasts the future realized volatility than the historical measures of volatility.

In a related vein, similar conclusions were given by, Fleming et al. (1995) for future market indices; Christensen and Prabhala (1998) for stocks were the first to document that VXO is an outperformed predictor and Giot (2003) for agricultural commodities. Consequently, the implied volatility of future option markets was compared with the historical volatility as a predictor of realized volatility for commodity market, concluded implied volatility as a better predictor and, also demonstrated that GARCH forecast was not superior to those of implied volatility (Szakmary et al., 2003). Similarly, Chiras and Manaster (1978); Schmalensee and Trippi (1978);Siriopoulos and Fassas (2009); Ferris, Kim and Park (2010); Kumar (2012); Thenmozhi and Chandra (2013); and Bentes (2015) compared the implied volatility to the historical volatility measures and concluded that implied volatility is superior forecast as compared to the historical volatility to predict realized volatility and also used as a forward-looking measure to make trading strategy. While many other studies documented the informational efficiency of the implied volatility index, Mourax et al. (1999) for VX1, VIX and VDAX; Blair et al. (2001) for VXO; Claessen and Mittnik (2002) for VDAX; Jiang and Tian (2005, 2007); Banerjee et al. (2007) for US VIX ; Areal (2008) for UK market VFTSE; Hung et al. (2009); Li and Yang (2009) for Australian market; Mcaleer and Wipthananthakul (2010); Tzang et al. (2011) and Yang and Liu (2012) for Taiwan market; Kumar (2012) for Indian market; Ryu (2012) for Korean market provided evidence that the VIX has informationally efficient to predict the future realized volatility compared to the alternative volatility models. But the result of the study is contrary to the hypothesis of options market efficiency, as the predictable volatility leads to a profitable trading strategy (Chiras and Manaster, 1977).

Contrarily the results found by some authors i.e. Day and Lewis (1992), Lamoureux and Lastrapes (1993), Dowling and Muthuswamy (2005), González and Novales (2009), Dixit, Yadav and Jain (2010) reported that the index is an inefficient to forecast future volatility as compared to GARCH and other historical time series models. The one-week ahead time horizon was considered by Day and Lewis (1992) while Lamoureux and Lastrapes (1993) predict one day ahead horizon. Also, Dowling and Muthuswamy (2005) studied at the daily and weekly frequency and concluded that the Australian implied volatility index is an inefficient predictor of future realized volatility. In contrast to their study on Australian data Frijns et al. (2010) concluded that the volatility index is a better predictor for both in the sample and out of the sample.

Dixit, Yadav and Jain (2010) conducted the study on CNX nifty index options and analyzed the informational efficiency of India VIX as compared to GARCH and E-GARCH and evidenced that implied volatility as compared to other volatility models is not superior to predict realized volatility, but Jorion (1995), Fleming et al. (1995) and Corrado and Miller (2005) empirically proved that yet it is biased forecast of future volatility but still better than historical measures. The academic volatility index for the Greek market proposed by Skiadopolous (2004) using FTSE/ASE 20 returns concluded that GVIX cannot be considered as informationally efficient indicator of the volatility of underlying stock market but contrarily the results for the Greek market given Siropolous and Fassas (2012) that implied volatility indices is good predictor of future realized volatility.

A different angle was taken by Day and Lewis (1992) who compared the information content of implied volatility with the weekly volatility estimates from the GARCH and E-GARCH models and lagged standard deviation. They conducted forecast within sample and out-of-sample in which they found evidence that both implied volatility and GARCH/E-GARCH are having informational content for future realized volatility, but implied volatility is not informational efficient and also historical volatility measures include the incremental information for future realized volatility. This was supported by Lamoureux and Lastrapes (1993) who examined the implied volatility calculated through stochastic volatility option pricing model. They compared the GARCH and historical volatility with one-day-ahead at-the-money call options volatility and concluded that the hull and white class of stochastic volatility option pricing model are biased for seven of the ten stocks in favour of GARCH model.

Consistent with above results some more studies of Becker et al. (2006, 2007), Corrado and Troung (2007), Becker and Clements (2008), Konstanitinidi et al. (2008) have been conducted. Still, other papers like Bartunek and Chowdhury (1995) examined the informational efficiency of implied volatility and GARCH volatility and concluded that statistically none of them is better than to forecast. With that Canina and Figlewski (1993) concluded that implied and realized volatility are not related to each other. But all of above the recent study conducted by Kourtis et al. (2016) study the predictive ability of implied volatility index on 13 equity markets and compare them internationally at daily and monthly horizon with HAR model and GJR- GARCH. They found that Implied volatility better predict the realized at monthly horizon instead HAR at daily levels. Hence the above studies suggested that even if implied volatility measures may be biased, they do a superior job than the historical realized volatility measures.

4.2 Association Between Implied Volatility Index and Underlying Stock Index Returns:

This section discusses the relationship of implied volatility indices and underlying assets return, a topic that is highly documented in the financial literature. The past literature recognised the negative relationship between stock returns and ex-post volatility.

The foundation stone of asset pricing theory in the literature of finance was propounded by Sharpe (1964), Lintner (1965) and Black (1972), popularly known as CAPM (Capital Asset Pricing Model) which explains the relation between expected stock returns and volatility. The relation between expected returns and volatility is positive but, the model offers a framework documented a negative relation between stock returns and volatility as in order to have higher future returns expected with high volatility in past accompanied with the drop in contemporaneous stock prices with the change in volatility. Black (1976) explained this relation through leverage effect, while Christie (1982) and French et al. (1987) documented it negative as well as asymmetric.

Schwert (1990) explained the asymmetric relationship as the forecasted volatility increases more with a fall in prices than the downfall in expected volatility with a similar size gain in prices. In the literature, this asymmetric relationship is explained by two explanations, i.e. volatility feedback hypothesis and leverage effect, but it is not easy to disentangle the fact from the given literature. The same conclusion verifying this relationship has been presented by Bollen and Whaley (2004), Low (2004), Dennis (2006) and Hibbert et al. (2008).

This relationship is also widely explained in the literature of the implied volatility index. The implied volatility, which is an ex-ante forecast of volatility calculated through options also proved this negative asymmetric relationship with underlying index returns. Initially, this relationship is documented by Whaley (2000) for the US volatility index, popularly known as the VIX. The VIX index shows the negative and asymmetric relationship with underlying index returns, which can be used as a hedging instrument by the asset and portfolio managers. Fleming (1995) and Carr and Wu (2006) also supported the results by empirically giving the evidence of the negative and asymmetric relationship between VIX and S andP 500 returns. Simon (2003) confirms the same relationship between VXN and underlying equity indices. Giot (2002, 2005a, 2005b) analysed the relationship between the VIX and VXN indices and their underlying index and also confirms the asymmetry in the market as in the low volatility regime the response is more pronounced to negative stock returns.

For the US market, Whaley (2009) and Simlai (2010) also verified the relationship exists between VIX and S and P 500 return. Similarly, for the European market indices, this negative and asymmetric relationship between VFTSE and FTSE 100 returns documented by Siripolous and Fassas (2008). Skiadopoulos (2004) examined the same relationship for the Greek market. Furthermore, there are many studies documented this negative and asymmetric relationship in various developed and developing markets i.e. Giner and Morini (2004) and Gonzales and Novales (2007) for the Spanish market; Gonzales and Novales (2009) for VDAX-NEW, VSMI and VIBEX-NEW; Frijns (2010) for the Australian market; Mcaleer and Wiphathananthakul (2010) for Thailand; Ederington and Guan (2010) for US market; Ryu (2012) for the Korean market; Kumar (2012); Dhananjhay (2012); and Shaikh and Padhi (2016) for India. The results found by Ederington and Guan (2010) depicts the asymmetric exist but clearly explained that this relationship became difficult in the case of different stock markets, forecast horizon, and data periods.

The literature in favour of asymmetric volatility relation (e.g. Schwert, 1989, 1990; Bates, 2000; Poteshman, 2001; Pan, 2002; Giot, 2005; Bollerslev and Zhou, 2006; Frijns et al., 2010).Sarwar (2012) evaluated this relationship in developing markets, i.e. Brazil, Russia, India and China also compare it with the benchmark index, i.e. VIX of the US market and documented a contemporaneous negative relationship between VIX and US market. Narwal and Chhabra (2017) examined the relationship between the implied volatility indices and its underlying asset in context of developed and developing markets (like U.S., Japan, Germany, and China). The empirical findings report the asymmetric behaviour which indicates that a larger impact on implied volatility indices are from negative return shocks as compared to positive returns.

While all the aforementioned studies confirmed the asymmetrical negative contemporaneous relationship, but except Dowling and Muthuswamy (2005) found no asymmetric exist between the volatility index and underlying indices. Badshah (2009) studied this relationship by considering this relationship four volatility indices of developed market, i.e. VIX, VXN, VDAX-NEW, VSTOXX and their underlying assets. Consistent with the previous results, the findings of the study show significant improvement, but his argument doesn’t support the above-mentioned explanations of leverage and volatility feedback. His findings could be explained by investor sentiment and through behavioural explanation and also supports the volatility index as a fear gauge index and as a sentiment indicator.

4.3 Informational Content of Implied Volatility to Predict Future Realized Returns:

The VIX has been called as the ‘investor fear gauge’ (Whaley 2000), as the extremely high indicator levels of the VIX accorded with high degrees of market turmoil. The role of VIX as an investor fear gauge and an indicator of portfolio insurance price have strengthened in periods of high market anxiety and turbulence. The rationale is that high levels signify that one is witnessing periods of pecuniary turmoil where investors are believed to be over-reacting and hence selling arbitrarily their financial assets to lift up cash and bound losses Giot (2002). From this observation, it can be used as a technical analysis indicator to predict future realized returns. As a technical analysis indicator, it indicates the oversold and overbought market conditions which help traders to take a long/short position in the market. These findings are paradoxical to the concept of market efficiency, but otherwise, it can be regarded as a risk factor in financial asset pricing since a number of researchers found that the market price of volatility risk is negative (Ting, 2007; Whaley, 2009). As the price and volatility are negatively correlated, hence the risk-averse investors may find attractive to invest when VIX is at high levels since it is considered as a signal of high-risk premium. In other words, the increase in future expected volatility of the asset increases the investor expectation for risk premium therefore considered the measure of expected volatility positively related to the future expected returns. As Giot (2005) examined if high levels of VIX indicate an oversold market and found that as the level of VIX is high (low), future returns are always positive (negative) which suggest that extremely high level of VIX may signal attractive buying opportunities. Another explanation is that the average and moderately high levels of implied volatility leads to unfavourable returns, but when the fear gets overwhelmed and the sentiments overcome the risk indicating by extremely high levels, give opportunities to long traders to enter an oversold market at extremely high levels of the VIX.

The findings of Giot (2005) were duly supported by Cipollini and Manzini (2007) and they observed that there is positive relationship exist between implied volatility index and future realized returns but the study was conducted on the portfolio of different stock characteristics like not for the entire index as in Giot (2005). Similar findings were reported by Rubbaniy et al. (2014) who examined the forecasting efficiency of volatility indices by taking VIX to forecast future returns classified on the basis of sectors and portfolios and they found that there is positive relationship between the current level of implied volatility and future market returns and the relation is stronger for high beta stocks of 20 and 60 day returns. The relation between them is also significant when the Fama and French control factors are used in a regression model to predict portfolio returns.

Bagchi (2012) also discussed the relationship between the IVIX (Indian volatility index) with the future return of the portfolio and documented that IVIX had a significant and positive relationship with future portfolio returns of 30 and 40 days. The high implied volatility levels predict the future returns and the asymmetric relationship exist between them and also signal the oversold market, but on the condition of the timing of current return (Hsiao and Li, 2010). Contrarily, Mo et al. (2015) conducted the study on implied volatility smirk and future stock returns considering different expiration period and moneyness range and concluded that there is no significant relation and the implied volatility is informationally efficient. Banerjee et al. (2007); Cipollini and Manzini (2007); Shaikh and Padhi (2014) concluded that VIX has positively related to the future portfolio returns. Banerjee et al. (2007) concluded high beta portfolios have strong relation with volatility index and also having higher returns than a low beta portfolio. The result presented by Cipollini and Manzini (2007) revealed that when the implied volatility is high the signal is loud and clear or even better when it spikes. Contrarily, the model is less efficient at low levels of implied volatility and it also dominates the long-only strategy on the index. On the same ground, Shaikh and Padhi (2014) reported that IVIX is the fear indicator of investors, as when there is a downfall in the market; they demand more hedge funds to protect their portfolios and that results in increases in implied volatility.

4.4 Volatility Spillovers and Contagion in Implied Volatility Index:

In integrated markets, the expectation of volatility in one market should be reflected in the respective expectations in other markets. Therefore, in asset allocation decisions the integration and interdependence between these markets should be examined. Ex-post returns data are used by researchers to examine volatility spillovers in the equity market, i.e. King and Wadhwani, 1990 and Hamaoet al., 1990 for USA, UK and Japanese stock market; Lin et al., 1994; Koutmos and Booth, 1995; Bekaert and Harvey, 1997; Cifarelli and Paladino, 2005. Longin and Solnik, 1995 studied with seven OECD countries and evidenced the relationship exists between the USA and other countries during the studied period. Recently, most of the studies examined the spillovers effect using VAR plus GARCH model. These studies include Baele (2005) in the USA and European stock market, Baur and Jung (2006) for USA and Germany. The IMF survey conducted on spillovers effect by Beirne et al. (2009) documented that the relationship between countries had increased from the US to Asian markets during the last decades. Some researchers conducted an investigation of spillover effects after the sub-prime crises of 2008 to examine the effect of the developed market on developing markets such as Tong and Wei (2009), Sun and Zhang (2009) and Dimitriou et al. (2013). It can be investigated by considering the interactions of implied volatility indices between various equity markets as implied volatility can be considered as an ex-ante measure of realized volatility. Implied volatility can be reflected as the pre-eminent estimate of uncertainty in the market (Merton, 1976).

The volatility indices as an ex-ante measure of volatility initially used by Gemmill and Kamiyama (1997) to investigate the volatility spillovers and found the spillovers from S and P options to the FTSE and Nikkei options. The interactions of implied volatility indices are also examined by such other authors as Nikkinen and Sahlstrom (2004) conducted a similar analysis, between various equity markets and concluded that U.S., U.K. and German have a high degree of correlation and U.S. is leading source of volatility transmission to other markets and in European context volatility is transmitted from German to other European markets. For the Greek and US volatility indices, Skiadopoulos (2004) found contemporaneous spillover between the two equity markets. Aboura (2003);Wagner and Szimayer (2004); Aijo (2008); Badshah (2009); Jiang et al. (2012); Siriopoulos and Fassas (2012); Narwal et al. (2012); Kumar (2012); and Kenmoe and Tafou (2014), examined the integration between various international volatility indices and transmission between the indices and found that all the markets are integrated and the price volatility in one market lead the volatility of asset in another market. Recently, Narwal et al. (2017) investigated the dynamic implied volatility linkages between the emerging (Indian) and developed economies (Japan, America, Germany, France, Switzerland and Eurozone markets) and found bidirectional cross-shock spillover effects between the India-America, India-Germany, India-Switzerland and India-Eurozone markets.Worldwide the primary source of uncertainty is the US implied volatility index i.e. VIX and correspondingly for the European markets, VSTOXX plays a major role (Siriopoulos and Fassas, 2009).

5. RESULTS AND DISCUSSION:

The study summarises the applications and informational content by reviewing the up-to-date literature on volatility indices. By reviewing existing studies dealing with a broad set of volatility indices, some conclusions can be drawn. First, the informational efficiency of volatility indices outperform to predict future realized volatility based on historical volatility measures or conditional volatility models. Secondly, it is negatively associated with the underlying index that can be used by investors to mitigate the investment risk by diversifying their portfolio. By reviewing the existing literature, the negative relationship suggests that the regulators can trade future and options on the volatility index so that can be used as a risk management tool. It can be considered as a technical analysis indicator to predict future realized returns; evidence has been reported for the US, Germany, Greece, Spain, Switzerland, the UK, Korea, India and Australia. As a predictor of future realized returns, one can make market timing strategies to take positions and also identify oversold and overbought market conditions. Third, there is evidence of volatility contagion between the US, European and Asian stock markets based on the recently created indices VXJ (Japan) and the India VIX (India). Moreover, a volatility spillovers effect from a leading market to an emerging market is documented, where the VIX is used as a proxy for leading market while IVIX is used as a proxy for emerging market. The current stage of the research on volatility indices and the increasing number of them leaves room for new lines of future research. Examining in depth the role of volatility indices constructing volatility indices over longer forecast horizons, so as to estimate volatility term structures for different markets are some directions of future research with appealing implications for portfolio managers.

Table 1 about here:

Table 1: Summarized Review of studies on Implied Volatility Indices calculated using the VIX model-free methodology.

Implied volatility indices	Underlying asset	Exchange	Country	Studies on the information content of the volatility index
VIX, VXD, RVX, VXN, VXV	S and P 500, DJIA, Russell 2000, Nasdaq 100, S and P 500	CBOE	US	Simon (2003), Aboura (2003), Szakmary et al. (2003), Wagner and Szimayer (2004), Nikkinen and Sahlstrom, (2004), Corrado and Miller (2005), Giot (2005), Cipollini and Manzini (2007), Banerjee et al. (2007), Simlai (2010), Jiang et al. (2012), Whaley (2009), Badshah (2009), Ferris, Kim and Park (2010), Hsiao and Li (2010), Kumar (2012),Siriopoulos and Fassas (2012), Sarwar(2012), Rubbaniyet al. (2014), Shaikh and Padhi (2014), Kenmoe and Tafou (2014),Bentes (2015).
S and P/TSX 60 VIX	S and P/TSX 60	MVX Montreal Exchange	Canada	Unknown
VDAX-NEW	DAX	Deutsche Borse	Germany	Aboura (2003), Wagner and Szimayer (2004), Nikkinen and Sahlstrom (2004), Aijo (2008), Jiang et al. (2012), González and Novales (2009), Badshah (2009), Siriopoulos and Fassas (2012), Rubbaniy et al. (2014).
VSMI	SMI	SWX Swiss Exchange	Switzerland	Aijo (2008), González and Novales (2009)
VSTOXX	Dow Jones EURO STOXX 50	Dow Jones	Eurozone	Aijo (2008),Jiang et al. (2012), Badshah (2009)
VAEX	AEX	NYSE Euronext	Netherlands	Unknown
VBEL	BEL 20	NYSE Euronext	Belgium	Unknown
VCAC	CAC 40	NYSE Euronext	France	Aboura (2003), Jiang et al. (2012)
VFTSE	FTSE 100	NYSE Euronext	UK	Nikkinen and Sahlstrom (2004) and Kumar (2012)
VIBEX-NEW	IBEX 35	Unofficial	Spain	Giner and Morini (2004), González and Novales (2997), González and Novales (2009)
GRIV	FTSE/ATHEX-20	Unofficial	Greece	Skiadopoulos (2004), Siriopoulos and Fassas (2012)
VKOSPI	KOSPI 200	Korea Exchange KRX	Korea	Ting (2007), Ryu (2012), Bentes (2015)
India VIX	NIFTY	NSE	India	Dixit, Yadav and Jain (2010),Dhananjhay(2012), Bagchi (2012), Kumar (2012), Narwal et al. (2012), Sarwar(2012) Thenmozhi and Chandra (2013), Shaikh and Padhi (2014), Bentes (2015).
VHSI	HIS	Hang Seng Indices Company Limited	Hong Kong	Bentes(2015)
VXJ	Nikkei 225	VXJ Research Group (unofficial)	Japan	Kumar (2012)
S and P/ASX 200 VIX	S and P/ASX 200	Australian Securities Exchange	Australia	Dowling and Muthuswamy (2005), Li and Yang (2009), Hung et al. (2009), Frijns et al. (2010).
New SAVI	FTSE/JSE Top40	Johannesburg Stock Exchange	South Africa	Kenmoe and Tafou (2014)

The above table shows that the studies conducted on the informational content of volatility indices have been based on the model-free methodology duly carried out in different countries including many developed and few developing countries. The table explicitly portrays that most of these studies were conducted in the developed world based on respective volatility indices viz., US VIX, Germany VDAX-NEW, Australia ASX 200 VIX and so on. So, the review of literature, perhaps, indicates the sense of direction to the future researchers to conduct further studies in the developing countries so as to make out as to what is needed to be done in developing world and to compare it with developed market so that the empirical results may be generalized, on the whole, across the world so far as volatility indices are concerned.

6. IMPLICATIONS OF THE STUDY:

An Accurate forecast of future volatility is important to asset allocation decision as well as in option pricing theory and the forecasting efficiency of the volatility index to efficiently forecast realized volatility makes it a useful tool for investors and portfolio managers. The literature suggests the application and information content of the implied volatility index, which possesses relevant implications for portfolio managers, asset management companies, regulatory authorities and also for policy makers and most importantly for hedgers. Risk management is considered to be an important tool for hedgers and portfolio managers, indicatively the highly negative and asymmetric contemporaneous correlation exists between volatility index and their underlying makes the diversification possible so that the investors or hedgers can hedge their possible risk by taking a position in future and options on a VIX index. It can also be used as to generate more profitable strategies. The predictive power of the index makes the index a very useful tool to generate profit by taking long and short positions to take an index as a signalling device to grab the opportunity, as empirically drawn by Giot (2005) that the average and moderately high level of implied volatility lead to Abnormal returns. But when the fear gets overwhelmed by the sentiments of investor then they get insurance by buying puts at high prices which inflate the IV levels and give opportunities to long traders to enter an oversold market at extremely high levels of IV. Also the integration between the markets in the world of globalisation is highly useful for regulators and policy makers, so that the early warning signals shown by the volatility indices helps the regulators to take timely corrective measures and also helpful to the portfolio managers and option traders to incorporate them while making strategies and also to correctly price the options by correctly forecasting the realized volatility which can help them to evaluate the attractive market timing like at low volatility regime one can take long position while at the time of high volatility the short position can be considered good suggested by Hasio and Li (2010).

7. CONCLUSION AND FUTURE RESEARCH DIRECTIONS:

Prior studies show that the predictive power of the implied volatility index is still an important area of future research. As the literature suggests the contemporaneous asymmetric negative relationship between implied volatility and their underlying assets due to the leverage effect or volatility-feedback hypothesis but some authors explained this through the behavioural aspect. Therefore, there is need to validate this hypothesis by taking volatility index as a sentiment indicator and examined the relation of implied volatility index with its underlying indices. Extensive evidence based on the forecasting or informational efficiency of I.V index to predict realized volatility has been conducted so far. However, much less evidence has been known how it forecast in the Non-US market despite its having an informationally efficient predictor in the US literature (e.g. Szakmary et al., 2003). Moreover, the relationship between the implied volatility index and future index returns has been conducted, but there is still much to be investigated regarding the association between implied volatility index and future portfolio returns and also with future sector returns which help to identify the pricing of the volatility risk premium in asset pricing. In addition, there is a need to isolate the risk and sentiment component of the volatility index to predict future portfolio returns. To study the volatility contagion, for instance, a possible avenue is to investigate the spillover and transmission across the volatility indices of developed and developing countries provide the international evidence of market integration and the possibility of shift contagion during market turmoil within developing countries. Therefore, understanding the role and information content of the implied volatility index has left to future research.

8. REFERENCES:

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Received on 20.03.2018 Modified on 16.04.2018

Asian Journal of Management. 2018; 9(2):967-977.

DOI: 10.5958/2321-5763.2018.00153.1