Download this Paper Open PDF in Browser

Volatility Forecasting and Explanatory Variables: A Tractable Bayesian Approach to Stochastic Volatility

47 Pages Posted: 26 Jan 2011 Last revised: 23 May 2013

Christian Dorion

HEC Montreal

Nicolas Chapados

University of Montreal

Date Written: May 23, 2013

Abstract

We provide a formulation of stochastic volatility (SV) based on Gaussian process regression (GPR). Forecasting volatility out-of-sample, both simulation and empirical analyses show that our GPR-based stochastic volatility (GPSV) model clearly outperforms SV and GARCH benchmarks, especially at long horizons. Most importantly, our approach enables the straightforward incorporation of arbitrary covariates without requiring the specification of functional forms a priori. Augmenting the GPSV model with exogenous variables increases its performance even further. In particular, a simple set of covariates reduces the error rate on one-year out-of-sample forecasting during the 2007-09 recession by 26% relative to a benchmark range-based SV model.

Keywords: Bayesian Volatility Models, Stochastic Volatility, Generalized Autoregressive Conditional Heteroscedasticity Models, Long Memory in Volatility, Multifactor Volatility

JEL Classification: C11, C22, C53

Suggested Citation

Dorion, Christian and Chapados, Nicolas, Volatility Forecasting and Explanatory Variables: A Tractable Bayesian Approach to Stochastic Volatility (May 23, 2013). Available at SSRN: https://ssrn.com/abstract=1747945 or http://dx.doi.org/10.2139/ssrn.1747945

Christian Dorion (Contact Author)

HEC Montreal ( email )

3000, Chemin de la Côte-Sainte-Catherine
Montreal, Quebec H2X 2L3
Canada
5143401522 (Phone)
5143405632 (Fax)

HOME PAGE: http://neumann.hec.ca/pages/christian.dorion/

Nicolas Chapados

University of Montreal ( email )

C.P. 6128 succursale Centre-ville
Montreal, Quebec H3C 3J7
Canada

Paper statistics

Downloads
214
Rank
119,457
Abstract Views
1,063