Quantile Regression Analysis of the Asymmetric Return-Volatility Relation

Badshah, Ihsan

doi:10.2139/ssrn.1543213

Cited by 25 publications

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“…OLS as it is evaluation is based on the deviations from the mean of the distribution underestimates the extreme quantile relationships. Badshah (2012) extends the past studies using LQR to estimate the negative asymmetric return-volatility relationship between stock index return (S&P-500, NASDAQ, DAX-30,STOXX ) and changes in volatility index return (VIX, VXN, VDAX, VSTOXX) for lower and upper quantiles which give negative and positive returns . In his study Badshah (2012) found that negative returns has higher impacts than the positive returns using linear quantile regression framework.…”

Section: Introductionmentioning

confidence: 60%

“…Badshah (2012) extends the past studies using LQR to estimate the negative asymmetric return-volatility relationship between stock index return (S&P-500, NASDAQ, DAX-30,STOXX ) and changes in volatility index return (VIX, VXN, VDAX, VSTOXX) for lower and upper quantiles which give negative and positive returns . In his study Badshah (2012) found that negative returns has higher impacts than the positive returns using linear quantile regression framework. Kumar (2012), used LQR to examine the statistical properties of volatility index of India and its relationship with Indian stock market.…”

Section: Introductionmentioning

confidence: 60%

“…This relationship is mostly quantified as being asymmetric (Badshah, 2012;Dennis, Mayhew & Stivers, 2006;Fleming, Ostdiek, & Whaley, 1995;Giot, 2005;Hibbert, Daigler, & Dupoyet, 2008;Low, 2004;Whaley, 2000;Wu, 2001). Asymmetric relationship means that the negative change in the stock market has higher impact on the volatility index than a positive change or vice versa.…”

Section: Introductionmentioning

confidence: 99%

“…Figure-2, gives the quantile-quantile plots for our data, none of the data series show normality in its distribution. When the data distribution is not normal, QR can provide more efficient estimates for return-volatility relationship (Badshah, 2012). QR can not only be used linearly but can also be evaluated for non-linear relationships using Copula based models.…”

Section: Introductionmentioning

confidence: 99%

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Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression

et al. 2013

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe purpose of this paper is to examine the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using linear and non linear quantile regression approach. Our goal in this paper is to demonstrate that the relationship between the volatility and market return as quantified by Ordinary Least Square (OLS) regression is not uniform across the distribution of the volatility-price return pairs using quantile regressions. We examine the bivariate relationship of six volatility-return pairs, viz. CBOE-VIX and S&P-500, FTSE-100 Volatility and FTSE-100, NASDAQ-100 Volatility ( The discussion of the properties of the volatility series and empirical findings in this paper have significance for portfolio optimization, hedging strategies, trading strategies and risk management in general.

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Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression

et al. 2013

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“…Badshah [12] found negative and asymmetric association among implied volatility index and its corresponding stock market index of the US and Europe. Tang [13] and Frijns et al [14] found negative and asymmetric return-volatility relation in the case of Korean and Australian stock market, respectively.…”

Section: Review Of Literaturementioning

confidence: 99%

Linkage between India Implied Volatility Index and Stock Index Returns

Srinivasan¹,

Vasudevan²

2017

TEL

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The present study examines the linkage between the change in implied volatility index and the underlying stock index return in the Indian stock market. The empirical results revealed that the contemporaneous return is the most important factor that determines the changes in the current India implied volatility. Besides, the empirical evidences confirm the negative asymmetry volatility-return relation, supporting the behavioral explanations (the affect and representativeness heuristics) rather than financial leverage hypothesis.

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Examining the Return–Volatility Relation for Foreign Exchange: Evidence from the Euro VIX

Daigler

Hibbert

Pavlova

2012

Journal of Futures Markets

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We compare the return–volatility relation for the euro currency to the equivalent relation for the equity market, examining the sign, symmetry, and strength of the relation. We employ the euro‐currency exchange‐traded fund (FXE) and its associated option implied volatility index (the EVZ), whereas previous studies only employ equities and/or realized volatility. The equity studies find a negative asymmetric return–volatility relation for implied volatility, with a strong relation when large market movements occur. We find that the euro return–volatility relation can possess either a positive or negative sign, is asymmetric, and has a weaker relation. Thus, the sign and strength of the euro relation differs from the equivalent equity relation. Our quantile regressions show that both the positive and negative contemporaneous returns of the euro result in increased volatility in the extreme quantiles of the conditional distribution, with the contemporaneous effect showing a stronger relation when the euro depreciates. We also find that the volume of the euro‐currency ETF options affects the return–volatility relation for the euro ETF. Overall, the results here expand the concept originally restricted to equities, with the surprising results that the return‐implied volatility relation is weaker and the asymmetric return sometimes is positive for the euro currency. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 34:74–92, 2014

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Quantile Regression Analysis of the Asymmetric Return-Volatility Relation

Cited by 25 publications

References 0 publications

Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression

Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression

Linkage between India Implied Volatility Index and Stock Index Returns

Examining the Return–Volatility Relation for Foreign Exchange: Evidence from the Euro VIX

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