Volatility Forecasting and Explanatory Variables: A Tractable Bayesian Approach to Stochastic Volatility
Nicolas Chapados (ApStat Technologies)
We provide a formulation of stochastic volatility based on Gaussian processes, a flexible framework for Bayesian nonlinear regression. The advantage of using Gaussian processes in this context is to place volatility forecasting within a regression framework; this allows a large number of explanatory variables to be used for forecasting, a task difficult with standard volatility-forecasting formulations. Our approach builds upon the range-based estimator of Alizadeh, Brandt and Diebold (2002) to provide much greater accuracy than traditional close-to-close estimators using daily data.