Examining Contemporaneous Relationship between Return of Nifty Index and India VIX

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
Prasenjit Chakrabarti
Keyword(s):  
Implied Volatility ◽  
Stock Index ◽  
Quadratic Effect ◽  
Asymmetric Building ◽  
Volatility Index ◽  
India Vix ◽  
The Relationship

The study examines the contemporaneous relationship between Nifty returns and India VIX returns. Literature documents that the relationship between them is negative and asymmetric. Building on this, the study considers the linear and quadratic effect of stock index return (CNX Nifty) and examines the changes in implied volatility index (India VIX). The study finds both linear and quadratic CNX Nifty index returns are significant for changes in the level of India VIX. Findings suggest that India VIX provides insurance both for downside market movement and size of the downside movement.

On the relationship between implied volatility index and equity index returns

2016 ◽  
Vol 43 (1) ◽  
pp. 27-47 ◽  
Author(s):  
Imlak Shaikh ◽  
Puja Padhi
Keyword(s):  
Implied Volatility ◽  
Stock Index ◽  
Financial Assets ◽  
Content Type ◽  
Market Opening ◽  
Volatility Index ◽  
India Vix ◽  
High Level ◽  
Equity Index ◽  
Japan And China

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 results are recorded for the Japan and China volatility index. Particularly, the analysis also supports that India VIX holds seasonality, on the market opening VIX observed to be at its high level, and on the subsequent days it remains low. The results on the options expiration unfold the facts that India VIX remains more normal on the day of expiration. Practical implications – The asymmetric relation and seasonal patterns are quite useful to the volatility traders to price the financial assets when market trades in the high- and low-volatility periods. Originality/value – There is a lack of studies of this kind in the context of emerging markets like India; hence, this is an attempt in this direction. The study provides an insight to the NSE to launch some derivative products (i.e. F & Os) on India VIX that can generate more liquidity in the market for the volatility traders.

An Examination of Asymmetric Relation between Implied Volatility Index and Its Underlying Asset

GIS Business ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. 10-22
Author(s):  
Karam Pal Narwal ◽  
Purva Chhabra
Keyword(s):  
Implied Volatility ◽  
Negative Relationship ◽  
Stock Index ◽  
Investment Risk ◽  
Underlying Asset ◽  
Volatility Index ◽  
Asymmetric Relation ◽  
Asymmetric Behaviour ◽  
The Relationship ◽  
Stock Index Returns

The volatility index is the measure of 30-day expected volatility. Its association with stock index returns provides an insight to the volatility traders to launch derivatives products so that it can be used as a hedging tool. The aim of the present study is to empirically examine 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. This evinced that the investors and traders respond highly to negative returns in low volatile period by demanding more options at high premium which makes the implied volatility high. Therefore, the negative relationship between IVIX and stock index returns makes the index relevant for investors to diversifying their portfolio so that they can mitigate the investment risk associated with the volatility.

Implied volatility smirk and future stock returns: evidence from the German market

2015 ◽  
Vol 41 (12) ◽  
pp. 1357-1379
Author(s):  
Di Mo ◽  
Neda Todorova ◽  
Rakesh Gupta
Keyword(s):  
High Frequency ◽  
Implied Volatility ◽  
Stock Index ◽  
High Frequency Data ◽  
Frequency Data ◽  
European Options ◽  
German Market ◽  
Content Type ◽  
The Relationship ◽  
Option Market

Purpose – The purpose of this paper is to investigate the relationship between option’s implied volatility smirk (IVS) and excess returns in the Germany’s leading stock index Deutscher-Aktien Index (DAX) 30. Design/methodology/approach – The study defines the IVS as the difference in implied volatility derived from out-of-the-money put options and at-the-money call options. This study employs the ordinary least square regression with Newey-West correction to analyse the relationship between IVS and excess DAX 30 index returns in Germany. Findings – The authors find that the German market adjusts information in an efficient way. Consequently, there is no information linkage between option volatility smirk and market index returns over the nine years sample period after considering the control variables, global financial crisis dummies, and the subsample test. Research limitations/implications – This study finds that the option market and the DAX 30 index are informationally efficient. Implications of the findings are that the investors cannot profit from the information contained in the IVS since the information is simultaneously incorporated into option prices and the stock index prices. The findings of this study are applicable to other markets with European options and for market participants who seek to exploit short-term market divergence from efficiency. Originality/value – The relationship between IVS and stock price changes has not been investigated sufficiently in academic literature. This study looks at this relationship in the context of European options using high-frequency transactions data. Prior studies look at this relationship for only American options using daily data. Pricing efficiency of the European option market using high-frequency data have not been studied in the prior literature. The authors find different results for the German market based on this high-frequency data set.

scholarly journals The Behavior of Option’s Implied Volatility Index: a Case of India VIX

2015 ◽  
Vol 16 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Imlak Shaikh ◽  
Puja Padhi
Keyword(s):  
Implied Volatility ◽  
Negative Impact ◽  
Practical Implication ◽  
Stock Market Volatility ◽  
Put Options ◽  
Monday Effect ◽  
Volatility Index ◽  
Significant Negative Impact ◽  
Day Of The Week ◽  
India Vix

The aim of this paper is to investigate the behavior of implied volatility in the form of day-of-the-week, year-of-the-month and surround the expiration of options. The persistence of volatility is modeled in ARCH/GARCH type framework. The empirical results have shown significant effects of the day-of-the-week, month-of-the-year and day of options expiration. The positive significant Monday effect explains that India VIX rises significantly on the initial days of the market opening, and the significant negative Wednesday effect shows that expected stock market volatility fall through Wednesday-Friday. Moreover, the study reveals the fact on options expiration, the evidence shows that India VIX fall significantly on the day of expiration of European call and put options. The March and December months have reported significant negative impact on the volatility index. Certainly, this kind of results holds practical implication for volatility traders, and helps to the market participant in hedging and pricing of options.

scholarly journals Linkage between India Implied Volatility Index and Stock Index Returns

2017 ◽  
Vol 07 (04) ◽  
pp. 929-938
Author(s):  
Palamalai Srinivasan ◽  
R. D. Vasudevan
Keyword(s):  
Implied Volatility ◽  
Stock Index ◽  
Volatility Index ◽  
Stock Index Returns

scholarly journals On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index

2017 ◽  
Vol 9 (9) ◽  
pp. 133 ◽  
Author(s):  
Jying-Nan Wang ◽  
Hung-Chun Liu ◽  
Lu-Jui Chen
Keyword(s):  
Implied Volatility ◽  
Option Price ◽  
Garch Model ◽  
Predictive Performance ◽  
Stock Index ◽  
Additional Information ◽  
Volatility Index ◽  
Volatility Models ◽  
Rolling Window ◽  
The Garch Model

This paper aims to propose four volatility measures: The first is the GARCH model advocated by Bollerslev (1986); the second is the GARCHVIX model which extends the GARCH model by including the volatility index (VIX) as explanatory variable for volatility; the last two are HS20D and HS252D, which represent the historical volatilities generated by traditional rolling window technique with 20- and 252-day historical index returns data, respectively. We examine the price information on VIX to improve the predictive performance of GARCH model for valuing TAIEX stock index call options (TXO) over the period from January 2014 to May 2015. Empirical results firstly indicate that both the GARCH and GARCHVIX models consistently perform better than the historical volatility models for forecasting call value of TXO under different moneynesses. Secondly, the GARCHVIX model significantly outperforms the GARCH model for most cases, indicating that the GARCH-based option price forecasts can be effectively improved with the additional information contained in VIX. Finally, the use of GARCHVIX model can greatly reduce model mispricing especially for out-the-money TXO option case. Thus, volatility index is crucial for option traders to efficiently predict TXO option value with GARCH model.

scholarly journals The Forecasting Power of the Volatility Index: Evidence from the Indian Stock Market

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Surya Bahadur G. C. ◽  
Ranjana Kothari
Keyword(s):  
Stock Market ◽  
Implied Volatility ◽  
Market Volatility ◽  
Series Data ◽  
Future Price ◽  
Stock Market Volatility ◽  
Short Term ◽  
Volatility Index ◽  
India Vix ◽  
Indian Stock Market

<div><p><em>Stock market volatility is a measure of risk in investment and it plays a key role in securities pricing and risk management. </em><em>The paper empirically analyzes the relationship between India VIX and volatility in Indian stock market. </em><em>India VIX is a measure of implied volatility which reflects markets’ expectation of future short-term stock market volatility.</em><em> It is a volatility index based on the index option prices of Nifty. </em><em>The study is based on time series data comprising of daily closing values of CNX Nifty 50 index comprising of 1656 observations from March 2009 to December 2015. </em><em>The results of the study </em><em>reveal that India VIX has predictive power for future short-term stock market volatility. It has higher forecasting ability for upward stock market movements as compared to downward movements. Therefore, it is more a bullish indicator. Moreover, the accuracy of forecasts provided by India VIX is higher for low magnitude future price changes relative to higher stock price movements. The current value of India VIX is found to be affected by past period volatility up to one month and it has forecasting ability for next one-month’s volatility which means the volatility in the Indian stock markets can be forecasted for up to 60 days period. </em></p></div>

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