Implied Volatility Indices – A Review

Siriopoulos, Costas; Fassas, Athanasios

doi:10.2139/ssrn.1421202

Cited by 27 publications

(24 citation statements)

References 16 publications

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“…Similar results are reported in previous research focusing on the US market (See Giot, 2005;Simon, 2003;and Whaley, 2008). 34 The results are consistent with Whiley (2000) and Siriopoulos and Fassas (2009). against market risk. Given time-varying behavior of volatility and equity markets, determination of adequate hedge ratios becomes an important empirical question for investors.…”

Section: Ols Vs Quantile Regression Hedge Ratiossupporting

confidence: 60%

“…For example, Fassas (2009) andWhaley (2000) report lack of evidence for strong asymmetric relation between VIX, RVX, VNX and their respective stock market indices. 6 In Europe, Alexander (2008) and Siriopoulos and Fassas (2012) both report an asymmetric negative relation for VFTSE and FTSE whilst Gonzalez and Novales (2009) report lack of asymmetric negative relation between VDAX, VSMI, and their respective stock market indices.…”

Section: Association Of Returns and Implied Volatilitymentioning

confidence: 99%

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European ‘Fear’ Indices – Evidence before and during the Financial Crisis

Aussenegg

Götz²,

Jelic

2013

SSRN Journal

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We document a negative and asymmetric contemporaneous relation of European stock and implied volatility returns. The negative relation is significantly more pronounced at the highest quantile of the stock market return distribution (i.e. largest price decrease). The relation between stock returns and implied volatility exhibits differences consistent with European institutional and cultural clusters. For example, German stock market tends to be more responsive to changes in implied volatility compared to UK stock market. In addition, the volatility spread for these two markets persist for a longer period compared to other European volatility spreads. The degree of integration between the leading European (UK, Germany and France) volatility markets, however, is very high and shocks on the implied volatility spread die out within a few days. Our Markov switching model distinguishes three volatility regimes.Large changes in both, implied volatility and stock returns increase the probability that volatility enters a higher (from low to middle and from middle to high) volatility regime. Factor loadings obtained by principal component analysis (PCA) of volatility returns are also regime dependent. Compared to US, the changes in European implied volatility tend to be more driven by tilts and non-linear movements of the volatility term structure. Our findings lend support to the behavioral explanation of the stock return-implied volatility relation and have implications for risk management.JEL classification: C32, G13, G15

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Section: Ols Vs Quantile Regression Hedge Ratiossupporting

confidence: 60%

Section: Association Of Returns and Implied Volatilitymentioning

confidence: 99%

European ‘Fear’ Indices – Evidence before and during the Financial Crisis

Aussenegg

Götz²,

Jelic

2013

SSRN Journal

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“…Siriopoulos and Fassas [18] tested the relationship between stock market returns and implied volatility by a regression analysis of daily changes of the volatility index against the daily positive and negative returns of the corresponding underlying stock index. Giot [5] classified implied volatility levels with respect to their 2-year rolling history and then used the dummy variables to assess the relationship between implied volatility and forward looking stock index returns.…”

Section: Empirical Analysismentioning

confidence: 99%

“…Following Siriopoulos and Fassas [18], we compute the squared return without mean-reversion assumption. We also use non-overlapping observations and compute the realized volatility, RV m , separately for each calendar month.…”

Section: Datamentioning

confidence: 99%

“…Similarly, Siriopoulos and Fassas [8] suggested that VFTSE included information about future volatility beyond that contained in past volatility. Interested readers are referred to Siriopoulos and Fassas [18] for the comprehensive review on the incremental information content of all the publicly available implied volatility indices across the world to realized volatility of the corresponding underlying equity markets.…”

Section: Introductionmentioning

confidence: 99%

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The Information Content of OVX for Crude Oil Returns Analysis and Risk Measurement: Evidence from the Kalman Filter Model

Chen

2015

Ann. Data. Sci.

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Crude oil volatility index (OVX) is a new index published by Chicago BoardOption Exchange since 2007. In recent years it emerged as an important alternative measure to track and analyze the volatility of future oil prices. In this paper we firstly model and analyze the dynamic relationship between OVX changes and future crude oil price returns with time-varying coefficients, modeled using the Kalman filter, in the regression models. Empirical results show a weak negative relationship between OVX changes and future crude oil price returns movement, and extremely high/low levels of OVX cannot predict future positive/negative returns well. Secondly, this paper explores whether OVX can predict future realized volatility of crude oil price returns . The empirical findings suggest that OVX serves as an unbiased but not an efficient estimate of the future realized volatility and it includes information of the future realized volatility. Finally the incorporation of information of OVX in measuring market risk is analyzed. The empirical result indicates that Kalman filter based model provides the improved performance than the linear regression model in terms of forecasting accuracy for realized volatility prediction and the reliability for VaR estimate. B Kaijian He

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Who drives the dance? Further insights from a time‐frequency wavelet analysis of the interrelationship between stock markets and uncertainty

Amar

Carlotti

2020

Int. J Fin Econ

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Using multivariate continuous wavelet tools this paper empirically investigates the strength and the magnitude of the relation between two measures of uncertainty, namely economic policy uncertainty index and implied volatility index, and stock market returns in Europe and United States. The multiple wavelet coherency not only highlights a significant contagion effect between the US and the European stock markets in times of crisis, but also indicates that the intensity of the interdependence between the stock markets and uncertainty indices is more persistent in times of stress and crisis periods. The results also indicate that, both in the U.S. and Europe, when partial coherencies are significant, the partial phase‐differences consistently highlight a negative co‐movement between the stock markets returns and their corresponding implied volatilities, with the implied volatility indices leading the stock markets for most of the time span and across all frequency bands at which the stocks and the fear indices have co‐moved significantly. The wavelet partial gain suggests that the European stock market is relatively more resilient than the U.S. stock market.

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Implied Volatility Indices – A Review

Cited by 27 publications

References 16 publications

European ‘Fear’ Indices – Evidence before and during the Financial Crisis

European ‘Fear’ Indices – Evidence before and during the Financial Crisis

The Information Content of OVX for Crude Oil Returns Analysis and Risk Measurement: Evidence from the Kalman Filter Model

Who drives the dance? Further insights from a time‐frequency wavelet analysis of the interrelationship between stock markets and uncertainty

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