Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility

TL;DR

This paper develops flexible semiparametric quantile regression models to analyze how future returns and volatility of financial assets depend on various measures of ex-post variation and option-implied volatility, demonstrating their effectiveness for risk management.

Contribution

It introduces semiparametric conditional quantile models using model-free measures of variance and volatility, improving the understanding of distribution dynamics in financial markets.

Findings

Linear quantile regressions effectively capture return distribution dynamics.

Heterogeneous quantile autoregressions perform well for realized volatility.

Models outperform benchmark models in predictive accuracy.

Abstract

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.