Title
Laplace approximation with Gaussian Processes for volatility forecasting
Abstract
Generalized Autoregressive Conditional Heteroscedascity (GARCH) models are ad hoc methods very used to predict volatility in financial time series. On the other hand, Gaussian Processes (GPs) offer very good performance for regression and prediction tasks, giving estimates of the average and dispersion of the predicted values, and showing resilience to overfitting. In this paper, a GP model is proposed to predict volatility using a reparametrized form of the Ornstein-Uhlenbeck covariance function, which reduces the underlying latent function to be an AR(1) process, suitable for the Brownian motion typical of financial time series. The tridiagonal character of the inverse of this covariance matrix and the Laplace method proposed to perform inference allow accurate predictions at a reduced cost compared to standard GP approaches. The experimental results confirm the usefulness of the proposed method to predict volatility, outperforming GARCH models with more accurate forecasts and a lower computational burden.
Year
DOI
Venue
2014
10.1109/CIP.2014.6844502
CIP
Keywords
Field
DocType
gaussian processes,financial management,forecasting theory,time series,brownian motion,garch models,gp,laplace approximation,ornstein-uhlenbeck covariance function,ad hoc methods,financial time series,generalized autoregressive conditional heteroscedascity,volatility forecasting,approximate inference,predictive models,noise,forecasting,computational modeling
Econometrics,Financial models with long-tailed distributions and volatility clustering,Autoregressive model,Covariance function,Laplace's method,Gaussian process,Overfitting,Autoregressive conditional heteroskedasticity,Volatility (finance),Mathematics
Conference
ISSN
Citations 
PageRank 
2327-1671
0
0.34
References 
Authors
5
6