Volatility spillovers and heavy tails: a large t-Vector AutoRegressive approach

Luca Barbaglia, Christophe Croux and Ines Wilms

No 590528, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

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.

Keywords: Commodities; Forecasting; Multivariate t-distribution; Vector AutoRegressive model; Volatility spillover (search for similar items in EconPapers)
Date: 2017-08
New Economics Papers: this item is included in nep-ene, nep-ets and nep-rmg
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Citations: View citations in EconPapers (1)

Published in FEB Research Report KBI_1716

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Working Paper: Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ete:kbiper:590528

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