Panel quantile regressions for estimating and predicting the Value--at--Risk of commodities

TL;DR

This paper develops a panel quantile regression model to analyze how realized and implied volatilities influence the future quantiles of commodity returns, providing insights for risk management.

Contribution

It introduces a flexible panel quantile regression framework that jointly considers ex-post and ex-ante volatility measures for predicting commodity return quantiles.

Findings

Future quantile returns depend on both realized and implied volatilities.

The conditional return distribution is platykurtic and time-invariant.

Common dependence patterns are identified across different commodity sectors.

Abstract

This paper investigates how realized and option implied volatilities are related to the future quantiles of commodity returns. Whereas realized volatility measures ex-post uncertainty, volatility implied by option prices reveals the market's expectation and is often used as an ex-ante measure of the investor sentiment. Using a flexible panel quantile regression framework, we show how the future conditional quantiles of commodities returns depend on both ex-post and ex-ante uncertainty measures. Empirical analysis of the most liquid commodities covering main sectors including energy, food, agricultural, precious and industrial metals reveal several important stylized facts about the data. We document common patterns of the dependence between future quantile returns and ex-post as well as ex-ante volatilities. We further show that conditional returns distribution is platykurtic and time-invariant. The approach can serve as a useful risk management tools for investors interested in commodity future contracts.