Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach

TL;DR

This paper introduces a large t-vector autoregressive model to analyze volatility spillovers among multiple assets, accounting for heavy tails in error distributions, and reveals bidirectional spillovers in commodity markets.

Contribution

It develops a penalized estimation method for large VAR models with t-distributed errors to study volatility spillovers among many assets.

Findings

Bidirectional volatility spillovers between energy and biofuel

Bidirectional spillovers between energy and agricultural commodities

Effective modeling of heavy-tailed errors in large VAR settings

Abstract

Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the possible fat-tailed distribution of the VAR model errors using a VAR model with errors following a multivariate Student t-distribution with unknown degrees of freedom. Moreover, we study volatility spillovers among a large number of assets. To this end, we use penalized estimation of the VAR model with t-distributed errors. We study volatility spillovers among energy, biofuel and agricultural commodities and reveal bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.