On the relationship between implied volatility index and equity index returns
11 Jan 2016-Journal of Economic Studies (Emerald Group Publishing Limited)-Vol. 43, Iss: 1, pp 27-47
TL;DR: In this article, the authors analyzed the asymmetric contemporaneous relationship between implied volatility index (India VIX) and equity index (S & P CNX Nifty Index) in the form of day-of-the-week effects and option expiration cycle and found that the changes in India VIX occur bigger for the negative return shocks than the positive returns shocks.
Abstract: Purpose – The purpose of this paper is to analyze the asymmetric contemporaneous relationship between implied volatility index (India VIX) and Equity Index (S & P CNX Nifty Index). In addition, the study also analyzes the seasonality of implied volatility index in the form of day-of-the-week effects and option expiration cycle. Design/methodology/approach – This study employs simple OLS estimation to analyze the contemporaneous relationship among the volatility index and stock index. In order to obtain robust results, the analysis has been presented for the calendar years and sub-periods. Moreover, the international evidenced presented for other Asian markets (Japan and China). Findings – The empirical evidences reveal a strong persistence of asymmetry among the India VIX and Nifty stock index, at the same time the magnitude of asymmetry is not identical. The results show that the changes in India VIX occur bigger for the negative return shocks than the positive returns shocks. The similar kinds of result...
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TL;DR: In this article, the authors investigated the relationship between implied volatility changes and the corresponding underlying equity index returns and found that there is significant integration with respect to market participants' expectations about future uncertainty.
Abstract: An implied volatility index reflects the market expectations for the future volatility of the underlying equity index. This study tests and documents the information content, regarding both the realized volatility and the returns of the underlying equity index, of all publicly available implied volatility indices across the world. The empirical findings suggest that implied volatility indices include information about future volatility beyond that contained in past volatility. In addition, we show that there is a statistically significant negative and asymmetric contemporaneous relationship between implied volatility changes and the corresponding underlying equity index returns. Furthermore, this study contributes to the international equity market integration studies by investigating the linkages among major stock exchanges; the basis of the integration analysis is the implied volatility of each market, as proxied by the corresponding implied volatility index and the findings suggest that there is significant integration with respect to market participants’ expectations about future uncertainty.
37 citations
TL;DR: In this article, the authors test and document the information content of all publicly available implied volatility indices regarding both the realized volatility and the returns of the underlying asset and find a significant contemporaneous relationship between implied volatility changes and underlying returns, but at the same time, they show that implied volatility in commodities, bonds, currencies and volatility react differently to underlying price changes compared to equities.
35 citations
TL;DR: In this article, an alternative option pricing model is introduced that performs better than existing ones, especially during a financial crisis, where the unconditional volatility of the original asset is increasing during a certain period of time.
Abstract: Purpose
Option pricing is an integral part of modern financial risk management. The well-known Black and Scholes (1973) formula is commonly used for this purpose. The purpose of this paper is to extend their work to a situation in which the unconditional volatility of the original asset is increasing during a certain period of time.
Design/methodology/approach
The authors consider a market suffering from a financial crisis. The authors provide the solution for the equation of the underlying asset price as well as finding the hedging strategy. In addition, a closed formula of the pricing problem is proved for a particular case. Furthermore, the underlying price sensitivities are derived.
Findings
The suggested formulas are expected to make the valuation of options and the underlying hedging strategies during a financial crisis more precise. A numerical application is provided for determining the premium for a call and a put European option along with the underlying price sensitivities for each option.
Originality/value
An alternative option pricing model is introduced that performs better than existing ones, especially during a financial crisis.
12 citations
TL;DR: In this paper, the authors examined the asymmetric inter-temporal relationship between India volatility index (NVIX) and stock market returns (Nifty S&P 50, 100, 200 and 500) in the Indian securities markets.
Abstract: This study examines the asymmetric inter-temporal relationship between India volatility index (NVIX) and stock market returns (Nifty S&P 50, 100, 200 and 500) in the Indian securities markets. The work is based on the daily prices of volatility index and stock indices for the period ranging from 2009–2015. Our results suggest a strong negative correlation between daily change in the NVIX and stock returns. This relation is more prominent when NVIX is higher and more volatile. The results show that there is an asymmetry among India NVIX and the stock returns and the magnitude of asymmetry is not identical. Due to this asymmetry NVIX is more of a gauge of investors’ fear, and portfolio insurance price than investor positive sentiment. The impact of changes in the stock returns on India NVIX is more when there are negative returns as compared to positive returns. These results have potential implications for the portfolio diversification, volatility traders and options trading-timing in the equity markets. DOI: http://dx.doi.org/10.5755/j01.ee.29.1.14966
10 citations
Cites background from "On the relationship between implied..."
...Studies also show that stocks’ returns are highly negatively correlated to implied volatility index (Fleming et al., 1995; Giot, 2005a,b; Sarwar, 2012a,b; Shaikh & Padhi, 2016 and Shaikh, 2017)....
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...…2015; Chin et al., 2016) on the Bond Yield and Stock Returns, Stock Price Forecasting and Volatility Modeling, there are limited attempts that discuss the information content of implied volatility (e.g. Padhi & Shaikh, 2014) and investors’ fear and stock returns (e.g. Shaikh & Padhi, 2014; 2016)....
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TL;DR: In this article, the authors studied the relationship between the two trading vehicles and increase the market awareness based on high order moment models which are used to mimic the behavior of these index options.
Abstract: Integration of financial markets due to globalization generates new paradigms of financialization. And with HFT i.e. the high-frequency trading, financialization has distorted the relations of financial markets. HFT is based on highly complex financial products such as Index Options which are linked with volatility and it‘s forecasting. After the introduction of Volatility, VIX Index of Chicago Board of Options Exchange becomes the effective benchmark for stock market volatility now a day. Although VIX Index is a volatility measure derived from Standard and Poor 500 Index (SPX) option prices, traders are unaware of the inverse relationship between these markets. This study purpose is to understand the relationship between the two trading vehicles and increase the market awareness based on high order moment models which are used to mimic the behavior of these index options. It also explains the logic versus perception perspective in option pricing theory to develop theoretical foundations and consider it in future theory erection. Finding shows that SPX index is negatively correlated with VIX Index and financial markets have an inverse relationship between them.
6 citations
References
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TL;DR: The authors analyzes the relation of stock volatility with real and nominal macroeconomic volatility, economic activity, financial leverage, and stock trading activity using monthly data from 1857 to 1987, finding that stock return variability was unusually high during the 1929-1939 Great Depression.
Abstract: This paper analyzes the relation of stock volatility with real and nominal macroeconomic volatility, economic activity, financial leverage, and stock trading activity using monthly data from 1857 to 1987. An important fact, previously noted by Officer (1973), is that stock return variability was unusually high during the 1929-1939 Great Depression. While aggregate leverage is significantly correlated with volatility, it explains a relatively small part of the movements in stock volatility. The amplitude of the fluctuations in aggregate stock volatility is difficult to explain using simple models of stock valuation, especially during the Great Depression. ESTIMATES OF THE STANDARD deviation of monthly stock returns vary from two to twenty percent per month during the 1857-1987 period. Tests for whether differences this large could be attributable to estimation error strongly reject the hypothesis of constant variance. Large changes in the ex ante volatility of market returns have important negative effects on risk-averse investors. Moreover, changes in the level of market volatility can have important effects on capital investment, consumption, and other business cycle variables. This raises the question of why stock volatility changes so much over time. Many researchers have studied movements in aggregate stock market volatility. Officer (1973) relates these changes to the volatility of macroeconomic variables. Black (1976) and Christie (1982) argue that financial leverage partly explains this phenomenon. Recently, there have been many attempts to relate changes in stock market volatility to changes in expected returns to stocks, including Merton (1980), Pindyck (1984), Poterba and Summers (1986), French, Schwert, and Stambaugh (1987), Bollerslev, Engle, and Wooldridge (1988), and Abel (1988). Mascaro and Meltzer (1983) and Lauterbach (1989) find that macroeconomic volatility is related to interest rates. Shiller (1981a,b) argues that the level of stock market volatility is too high relative to the ex post variability of dividends. In present value models such as Shiller's, a change in the volatility of either future cash flows or discount rates
3,094 citations
TL;DR: In this paper, the authors examined two alternative models of the process generating stock returns: calendar time hypothesis and trading time hypothesis, and found that returns are generated only during active trading and the expected return is the same for each day of the week.
1,980 citations
TL;DR: In this article, the authors examined the joint time series of the S&P 500 index and near-the-money short-dated option prices with an arbitrage-free model, capturing both stochastic volatility and jumps.
1,638 citations
"On the relationship between implied..." refers background in this paper
...The literature in favor of asymmetric relation (e.g. Bates, 2000; Bollerslev and Zhou, 2006; Dowling and Muthuswamy, 2005; Dennis et al., 2006; Ederington and Guan, 2010; Fleming et al., 1995; Giot, 2005; Frijns et al., 2010a, b; Poteshman, 2001; Pan, 2002; Schwert, 1989, 1990)....
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...The earlier studies (e.g. Bates, 2000; Dennis et al., 2006; Poteshman, 2001; Pan, 2002) they find good degree of negative correlation between returns and implied volatility....
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Abstract: Financial market volatility is an important input for investment, option pricing, and financial market regulation. The emphasis of this review article is on forecasting instead of modelling; it compares the volatility forecasting findings in 93 papers published and written in the last two decades. Provided in this paper as well are volatility definitions, insights into problematic issues of forecast evaluation, data frequency, extreme values and the measurement of "actual" volatility. We compare volatility forecasting performance of two main approaches; historical volatility models and volatility implied from options. Forecasting results are compared across different asset classes and geographical regions.
1,551 citations
TL;DR: In this paper, the authors examined two alternate explanations: stochastic volatility and jumps, and fitted them to S&P 500 futures options data over 1988-1993 and found that the stochassy volatility model requires extreme parameters (e.g., high volatility of volatility) that are implausible given the time series properties of option prices.
1,476 citations