High Dimension Dynamic Correlations

Abstract

This paper develops time series methods for forecasting correlations in high dimensional problems. The Dynamic Conditional Correlation model is given a new convenient estimation approach called the MacGyver method. It is compared with the FACTOR ARCH model and a new model called the FACTOR DOUBLE ARCH model. Finally the latter model is blended with the DCC to give a FACTOR DCC model. This family of models is estimated with daily returns from 18 US large cap stocks. Economic loss functions designed to form optimal portfolios and optimal hedges are used to compare the performance of the methods. The best approach invariably is the FACTOR DCC and the next best is the FACTOR DOUBLE ARCH.