Can the GQARCH Latent Factor Model Improve the Prediction Performance of Multivariate Financial Time Series?

ABSTRACT

Factor model is a very useful and popular model in Finance. Such a model attempts to explain the correlation between a larger set of variables in terms of a small number of unobservable or latent random variables called factors. This paper considers a combination of the standard Gaussian factor model and the generalized quadratic autoregressive conditionally heteroskedastic model (GQARCH). We discuss some of its properties and we show that identification problems are alleviated when variation in factor variances is accounted for. The focus is on the iterative estimation procedure of the model parameters. Kalman filter and maximum likelihood methods based on the Expectation-Maximization (EM) algorithm lead to an elegant estimation procedure. A detailed comparison of the forecasts generated by the GQARCH-Factor Model is made with other alternative methods. On the basis of out-of-sample forecast encompassing tests as well as other measures for forecasting accuracy, results indicate that the use of this factorial approach yields overall better forecasts than those generated by competing models.

Key words and phrases:

• Conditional Heteroskedasticity
• EM Algorithm
• Forecasting
• Kalman Filter
• Latent Factor models