Abstract

http://ssrn.com/abstract=1747945
 
 

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


Christian Dorion


HEC Montreal

Nicolas Chapados


University of Montreal

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.

Number of Pages in PDF File: 47

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

JEL Classification: C11, C22, C53

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Date posted: January 26, 2011 ; Last revised: May 23, 2013

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: http://ssrn.com/abstract=1747945 or http://dx.doi.org/10.2139/ssrn.1747945

Contact Information

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
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