Working Papers
"Estimating and Forecasting Skewness Using Affine Stochastic Volatility Models", with Kevin Aretz and Yifan Li
Abstract: We derive an estimator of the physical skewness of an asset's discrete return over any time horizon based on the assumption that the asset's price follows a stochastic process from the affine stochastic volatility (ASV) model class. Conceptually, our estimator improves upon others by (i) focusing on discrete returns; (ii) allowing us to capture compounding and leverage effects; yielding (iii) horizon-consistent (iv) conditional ("forward-looking") and unconditional ("historical") estimates; and (v) not requiring ad-hoc conditioning variables. We use a simulation exercise to show that our estimator is highly precise even when the true data-generating process partially deviates from that assumed by the estimator. The exercise further suggests that our estimator comfortably beats others advocated in recent studies. Using options data, we finally show that our estimator best captures time-series variations in the risk-neutral conditional skewness of the S&P 500 index.
Presented at (* = co-author): University of Konstanz*, 2020 CFE-CMStatistics, 6th KoLaMaFr Workshop on Financial Econometrics, 7th IYFS, University of Manchester, 2022 AFA (Poster), 2022 IAAE, 3rd FOFI
"The Skewness of Discrete Single-Stock Returns", with Kevin Aretz and Yifan Li
Abstract: We use the new efficient and flexible parametric estimator of the skewness of discrete returns of Aretz et al. (2024) to reassess important empirical properties of the skewness of single-stock returns. In a nutshell, our estimator assumes that single-stock returns obey Heston’s (1993) stochastic process and then estimates their skewness from the fitted moment-generating functions of that process. We start with showing that our estimates easily beat those of other estimators from the literature. We next identify those stocks more likely to exhibit a U-shaped skewness-return horizon relation or an explosive skewness. Moreover, we show that while historical and forward-looking skewness align over long return horizons, historical skewness is a weak predictor of forward-looking skewness over short horizons, with the spread conditioned by various firm fundamentals and the economic state. We next reveal how our estimates relate to firm fundamentals and firm-fundamental-based skewness estimators. We finally estimate the term structure of forward-looking skewness premiums orthogonal to firm characteristics.
Presented at: 5th QFFE, 2023 CFRI&CIRF