Which Parametric Model for Conditional Skewness?

Feunou, Bruno; Tedongap, Roméo

doi:10.2139/ssrn.968091

Cited by 9 publications

(17 citation statements)

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“…Thus, if RSV t+1 < 0 the distribution is left-skewed, and when RSV t+1 > 0 it is right-skewed. A theoretical justification for using RSV t+1 as a measure of skewness can be found in Feunou et al (2016). To provide more intuition on the behavior of RSV t+1 , we combine the previous asymptotic results to get…”

Section: Separating Downside From Upside Volatility: Theoretical Argumentioning

confidence: 99%

Good Volatility, Bad Volatility, and Option Pricing

Feunou¹,

Okou²

2018

J. Financ. Quant. Anal.

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Advances in variance analysis permit the splitting of the total quadratic variation of a jump-diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions. To appraise the economic gain of this decomposition, we design a new and flexible option pricing model in which the underlying asset price exhibits distinct upside and downside semivariance dynamics driven by the model-free proxies of the variances. The new model outperforms common benchmarks, especially the alternative that splits the quadratic variation into diffusive and jump components.

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Section: Separating Downside From Upside Volatility: Theoretical Argumentioning

confidence: 99%

Good Volatility, Bad Volatility, and Option Pricing

Feunou¹,

Okou²

2018

J. Financ. Quant. Anal.

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“…They find that signed jump variation, which they define as the difference between good and bad realized variance, is significantly related to expected stock returns. We notice that the relative signed jump variation considered by Bollerslev et al (2017) is a measure of skewness, as demonstrated by Feunou et al (2016). In that sense, Bollerslev et al (2017) are analyzing implications of individual firms' skewness for the cross-section of expected stock returns.…”

Section: Introductionmentioning

confidence: 85%

“…For example, Markowitz (1959) advocates the use of the downside semi-variance (namely, bad variance) as the measure of stock risk instead of the total variance, since the total variance also accounts for the upside semi-variance (namely, good variance) which measures not the risk but the potential of a stock. More recently, Feunou et al (2013), Bekaert et al (2015) and Segal et al (2015) find that expected excess returns on stocks are positively related to bad variance while they are negatively related to good variance. This suggests that, on the one hand, risk-averse investors are averse to increases in bad variance.…”

Section: Variance Risk Premium: Decomposition and Interpretationmentioning

confidence: 99%

“…Another key measure for assessing the riskiness of a stock and also related to the realized semi-variances is the realized relative semi-variance introduced by Feunou et al (2013Feunou et al ( , 2016 as a measure of asymmetry, defined as the difference between the upside and the downside semivariances, and referred to as signed jump variation by Patton and Sheppard (2015) and Bollerslev et al (2017). The signed jump variation is defined by…”

Section: Variance Risk Premium: Decomposition and Interpretationmentioning

confidence: 99%

“…Our paper also relates to the parallel literature documenting the importance of analyzing the bad and good components of volatility. Feunou et al (2013) and Bekaert et al (2015) extend the GARCH framework of Bollerslev (1986) to model these two components of market volatility and analyze their implications on market returns. Patton and Sheppard (2015) use realized semivariance measures introduced by Barndorff-Nielsen et al (2010) to predict future market volatility and find that bad volatility is a better predictor than good volatility.…”

Section: Introductionmentioning

confidence: 99%

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Variance Premium, Downside Risk, and Expected Stock Returns

Feunou¹,

Aliouchkin²,

Tedongap³

et al. 2017

SSRN Journal

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We decompose total variance into its bad and good components and measure the premia associated with their fluctuations using stock and option data from a large cross-section of firms. The total variance risk premium (VRP) represents the premium paid to insure against fluctuations in bad variance (called bad VRP), net of the premium received to compensate for fluctuations in good variance (called good VRP). Bad VRP provides a direct assessment of the degree to which asset downside risk may become extreme, while good VRP proxies for the degree to which asset upside potential may shrink. We find that bad VRP is important economically; in the cross-section, a one-standard-deviation increase is associated with an increase of up to 13% in annualized expected excess returns. Simultaneously going long on stocks with high bad VRP and short on stocks with low bad VRP yields an annualized risk-adjusted expected excess return of 18%. This result remains significant in double-sort strategies and cross-sectional regressions controlling for a host of firm characteristics and exposures to regular and downside risk factors. Bank topics: Asset pricing; Financial markets JEL code: G12 Résumé Nous décomposons la variance totale en ses éléments positifs et négatifs et mesurons les primes associées à leurs fluctuations à partir de données sur les actions et les options, données tirées d'un vaste échantillon représentatif d'entreprises. La prime liée au risque de la variance (PRV) totale représente la prime à payer pour s'assurer contre les fluctuations d'une variance négative (appelée mauvaise PRV), après déduction de la prime reçue pour compenser les fluctuations de la variance positive (appelée bonne PRV). La mauvaise PRV fournit une évaluation directe du degré d'exacerbation du risque à la baisse pesant sur les actifs, alors que la bonne PRV donne une indication du degré de réduction de la hausse potentielle des prix d'actifs. Nous constatons que la mauvaise PRV revêt une importance économique; dans l'échantillon représentatif d'entreprises, à une augmentation d'un écart-type correspond une augmentation de l'excédent de rendement attendu pouvant aller jusqu'à 13 % (en taux annualisé). Prendre une position longue sur des titres assortis d'une mauvaise PRV élevée, en concomitance avec une position courte sur des titres assortis d'une mauvaise PRV faible, procure un excédent de rendement attendu (en taux annualisé, ajusté en fonction des risques) de 18 %. Ces résultats demeurent significatifs dans les stratégies dichotomiques et les régressions transversales tenant compte d'une série de caractéristiques des entreprises et d'expositions à des risques habituels ainsi qu'à des risques à la baisse.

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The transformed Gram Charlier distribution: Parametric properties and financial risk applications

León

Ñíguez

2021

Journal of Empirical Finance

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Which Parametric Model for Conditional Skewness?

Cited by 9 publications

References 54 publications

Good Volatility, Bad Volatility, and Option Pricing

Good Volatility, Bad Volatility, and Option Pricing

Variance Premium, Downside Risk, and Expected Stock Returns

The transformed Gram Charlier distribution: Parametric properties and financial risk applications

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