Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility

By Filip Žikeš, Jozef Baruník. 2013

55 citations recorded by the source corpus.

This paper investigates how the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility. We work in the flexible quantile regression framework and rely on recently developed model-free measures of integrated variance, upside and downside semivariance, and jump variation. Our results for the S&P 500 and WTI Crude Oil futures contracts show that simple linear quantile regressions for returns and heterogenous quantile autoregressions for realized volatility perform very well in capturing the dynamics of the respective conditional distributions, both in absolute terms as well as relative to a couple of well-established benchmark models. The models can therefore serve as useful risk management tools for investors trading the futures contracts themselves or various derivative contracts written on realized volatility.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/29a131286ad0b67cb75a6f912379e931804b10f2

Citations in the corpus

• Quantile coherency: A general measure for dependence between cyclical economic variables (2015, unknown): “These notions have recently been documented by Engle and Manganelli (2004) and ˇZikeˇs and Barun´ık (2016) who propose models for the conditional quantiles of the return distribution based on the past volatility.”
• A joint quantile and expected shortfall regression framework (2017, unknown): “Forecasts of the VaR and ES for the HAR model are obtained from the volatility forecasts and by assuming a Gaussian return distribution.”