Y. Eser ARISOY est titulaire d’un Doctorat de Bilkent University (2007) et d’une Habilitation à Diriger les Recherches de l’Université Paris-Dauphine (2015). Ses recherches se situent dans le champ de l’évaluation d’actifs et s’articulent autour de quatre axes: i) l’impact du risque de volatilité sur l’évaluation d’actifs, ii) l’impact des frictions de marché sur l’évaluation d’actifs, iii) la prévisibilité des rendements d’actifs et iv) la relation entre incertitude et volatilité de la volatilité. Ses travaux ont été publiés dans plusieurs revues internationales telles que Journal of Financial Economics, Journal of Banking and Finance, Applied Economics, Journal of Derivatives et Journal of Futures Markets. Son travail sur volatilité de volatilité a été choisi demi-finaliste dans le catégorie «Investissements» à FMA 2015 Conférence et a reçu le prestigieux «2016 Crowell Third Prize» qui est attribué chaque année à la recherche de haute qualité qui connecte la théorie avec le pratique d’investissement quantitative.
Agarwal V., Arisoy E., Naik N. (2017), Volatility of Aggregate Volatility and Hedge Fund Returns, Journal of Financial Economics
This paper investigates empirically whether uncertainty about equity market volatility can explain hedge fund performance both in the cross section and over time. We measure uncertainty via volatility of aggregate volatility (VOV) and construct an investable version through returns on lookback straddles on the VIX index. We find that VOV exposure is a significant determinant of hedge fund returns. After controlling for fund characteristics, we document a robust and significant negative risk premium for VOV exposure in the cross section of hedge fund returns. We corroborate our results using statistical and parameterized proxies of VOV over a longer sample period.
Fu X., Arisoy E., Shackleton M., Umutlu M. (2016), Option-Implied Volatility Measures and Stock Return Predictability, Journal of Derivatives, 24, 1, p. 58-78
Using firm-level option and stock data, we examine the predictive ability of option-implied volatility measures proposed by previous studies and recommend the best measure using up-to-date data. Portfolio-level analysis implies significant non-zero risk-adjusted returns on arbitrage portfolios formed on the call -- put implied volatility spread, implied volatility skew, and realized -- implied volatility spread. Firm-level cross-sectional regressions show that the implied volatility skew has the most significant predictive power over various investment horizons. The predictive power persists before and after the 2008 Global Financial Crisis
Aboura S., Arisoy E. (2016), Does Aggregate Uncertainty Explain Size and Value Anomalies?, Applied Economics
This paper examines the impact of aggregate uncertainty on return dynamics of size and book-to-market ratio sorted portfolios. Using VVIX as a proxy for aggregate uncertainty, and controlling for market risk, volatility risk, correlation risk and the variance risk premium, we document significant portfolio return exposures to aggregate uncertainty. In particular, portfolios that contain small and value stocks have significant and negative uncertainty betas, whereas portfolios of large and growth stocks exhibit positive and significant uncertainty betas. Using a quasi-natural experimental setting around the financial crisis, we confirm the differential sensitivity of small versus big and value versus growth portfolios to aggregate uncertainty. We posit that due to their negative uncertainty betas, uncertainty-averse investors demand extra compensation to hold small and value stocks. Our results offer an uncertainty-based explanation to size and value anomalies.
Arisoy Y., Altay-Salih A., Pinar M. (2014), Short Sales Constraints, Options and CCAPM, Finance Research Letters, 11, 1, p. 16â24
This article examines agents' consumption-investment problem in a multi-period pure exchange economy where agents are constrained with the short-sale of state-dependent risky contingent claims. In equilibrum, agents hold options written on aggregate consumption in their optimal portfolios. Furthermore, under the specific case of quadratic utility, the optimal risk-sharing rule derived for the pricing agent leads to a multifactor conditional consumption-based capital asset pricing model (CCAPM), where excess option returns appear as factors.
Arisoy Y. (2014), Aggregate Volatility and Market Jump Risk : An Option-Based Explanation to Size and Value Premia, The Journal of Futures Markets, 34, 1, p. 34â55
It is well-documented that stock returns have different sensitivities to changes in aggregate volatility, however less is known about their sensitivity to market jump risk. By using S&P 500 crash-neutral at-the-money straddle and out-of-money put returns as proxies for aggregate volatility and market jump risk, I document significant differences between volatility and jump loadings of value vs. growth, and small vs. big portfolios. In particular, small (big) and value (growth) portfolios exhibit negative (positive) and significant volatility and jump betas. I also provide further evidence that both volatility and jump risk factors are priced and negative.
Arisoy Y. (2010), Volatility Risk and the Value Premium : Evidence from the French Stock Market, Journal of Banking and Finance, 34, 5, p. 975-983
This paper documents that systematic volatility risk is an important factor that drives the value premium observed in the French stock market. Using returns on at-the-money straddles written on the CAC 40 index as a proxy for systematic volatility risk, I document significant differences between volatility factor loadings of value and growth stocks. Furthermore, when markets are classified into expected booms and recessions, volatility factor loadings are also time-varying. When expected market risk premium is above its average, i.e. during expected recessions, value stocks are seen riskier than their growth counterparts. This implies in bad times, investors shift their preferences away from value firms. Instead they use growth stocks as hedges against deteriorations in their wealth during those times. The findings are in line with the predictions of rational asset pricing theory and support a "flight-to-quality" explanation.
Arisoy Y., Altay-Salih A., Akdeniz L. (2007), Is Volatility Risk Priced in the Securities Market ? Evidence from S&P 500 Index Options, The Journal of Futures Markets, 27, 7, p. 617-642
The authors examine whether volatility risk is a priced risk factor in securities returns. Zero-beta at-the-money straddle returns of the S&P 500 index are used to measure volatility risk. It is demonstrated that volatility risk captures time variation in the stochastic discount factor. The results suggest that straddle returns are important conditioning variables in asset pricing, and investors use straddle returns when forming their expectations about securities returns. One interesting finding is that different classes of firms react differently to volatility risk. For example, small firms and value firms have negative and significant volatility coefficients, whereas big firms and growth firms have positive and significant volatility coefficients during high volatility periods, indicating that investors see these latter firms as hedges against volatile states of the economy. Overall, these findings have important implications for portfolio formation, risk management, and hedging strategies.
Agarwal V., Arisoy E., Naik N. (2015), Volatility of Aggregate Volatility and Hedge Fund Returns, 5th International Conference of the Financial Engineering and Banking Society (FEBS 2015), Nantes, France
This paper investigates empirically whether uncertainty about volatility of the market portfolio can explain the performance of hedge funds both in the cross-section and over time. We measure uncertainty about volatility of the market portfolio via volatility of aggregate volatility (VOV) and construct an investable version of this measure by computing monthly returns on lookback straddles on the VIX index. We find that VOV exposure is a significant determinant of hedge fund returns at the overall index level, at different strategy levels, and at an individual fund level. After controlling for a large set of fund characteristics, we document a robust and significant negative risk premium for VOV exposure in the cross-section of hedge fund returns. We further show that strategies with less negative VOV betas outperform their counterparts during the financial crisis period when uncertainty was at its highest. On the contrary, strategies with more negative VOV betas generate superior returns when uncertainty in the market is less. Finally, we demonstrate that VOV exposure-return relationship of hedge funds is distinct from that of mutual funds and is consistent with the dynamic trading of hedge funds and risk-taking incentives arising from performance-based compensation of hedge funds.
Akdeniz L., Altay-Salih A., Arisoy Y. (2012), Aggregate Volatility and Threshold CAPM, 2012 FMA ANNUAL MEETING, Atlanta, Georgia, États-Unis
We propose a volatility-based threshold capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to innovations in aggregate volatility. Using option-implied measures (i.e. returns on at-the-money straddles written on the S&P 500 index and range of the VIX index) as proxies for changes in aggregate volatility, we find that asset sensitivity to market risk changes significantly when aggregate market volatility is beyond a certain threshold. More specifically, portfolios of small (big) and value (growth) stocks have significantly higher (lower) betas at times of high volatility. Due to changes in their market betas, small and value stocks are perceived riskier than their big and growth counterparts in bad times, when aggregate volatility is high. The proposed model also does a better job with pricing, especially for value and small portfolios and when aggregate market volatility is high.
Aretz K., Arisoy E. (2017), Do Stock Markets Price Skewness? New Evidence from Quantile Regression Skewness Forecasts, Cahier de recherche DRM, Paris, Université Paris-Dauphine
We use density forecasts derived from recursively estimated quantile regressions to calculate a forecast of the physical skewness of an asset's future return distribution. The forecast is unbiased and efficient, and it can easily be adapted to forecast the skewness of returns calculated over any conceivable return interval. Using Neuberger's (2012) realized physical skewness, we show that our quantile regression skewness forecast outperforms other variables proposed in the literature. Despite this, it does not condition the cross-section of future stock returns, neither independently nor when combined with other forecasts. Overall, we cast doubt on whether stock markets price expected stock skewness.
Aboura S., Arisoy E. (2017), Can Exposure to Tail Risk Explain Size, Book-to-Market, and Idiosyncratic Volatility Anomalies?, Cahier de recherche DRM, Paris, Université Paris-Dauphine
We examine the impact of aggregate tail risk on return dynamics of size, book-to-market ratio, and idiosyncratic volatility sorted portfolios. Using changes in VIX Tail Hedge Index (?VXTH) as a proxy for aggregate tail risk, and controlling for market, size, book-to-market, and aggregate volatility risk, we document significant portfolio return exposures to tail risk. In particular, portfolios that contain small, value and volatile stocks exhibit consistently positive and statistically significant tail risk betas, whereas portfolios of big, growth and non-volatile stocks exhibit negative tail risk betas. We posit that due to their positive tail risk exposures, tail risk-averse investors demand extra compensation to hold small, value, and high idiosyncratic volatility stocks. Our results offer a tail risk-based explanation to size, value, and idiosyncratic volatility anomalies.
Arisoy Y., Altay-Salih A., Akdeniz L. (2014), Aggregate Volatility Expectations and Threshold CAPM,, 45
We propose a volatility-based capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to changes in investors' expectations regarding near-term aggregate volatility. Using a novel measure to proxy for expected changes in aggregate volatility, i.e. monthly range of the VIX index (RVIX), we find that portfolio betas change significantly when aggregate volatility expectations is beyond a certain threshold level. Due to changes in their market betas, small and value stocks are perceived as riskier than their big and growth counterparts in bad times, when aggregate volatility is expected to be high. The model yields a positive and significant market risk premium during periods when investors do not expect significant changes in near-term aggregate volatility. The findings support a volatility-based time-varying risk explanation.
Fu X., Arisoy Y., Shackleton M., Umutlu M. (2013), Option-Implied Volatility Measures and Stock Return Predictability,, 43
Using firm-level option and stock data, we examine the predictive ability of option-implied volatility measures proposed by other studies. More particularly, we focus on call-put implied volatility spread, implied volatility skew, 'above-minus-below', 'out-minus-at' of calls, 'out-minus-at' of puts, and realized-implied volatility spread. We document significant non-zero risk-adjusted returns on arbitrage portfolios formed on call-put implied volatility spread, implied volatility skew, 'above-minus-below', and realized-implied volatility spread. Cross-sectional regressions show that call-put implied volatility spread is the most important factor in predicting stock returns for one-month holding period. For two-month and three-month holding periods, 'out-minus-at' of calls has stronger predictive ability.