The two-component Beta-t-QVAR-M-lev: a new forecasting model

Abstract

AbstractWe introduce a new joint model of expected return and volatility forecasting, namely the two-component Beta-t-QVAR-M-lev (quasi-vector autoregression in-mean with leverage). The maximum likelihood estimator for the two-component Beta-t-QVAR-M-lev is an extension of theoretical results of the one-component Beta-t-QVAR-M. We compare the volatility forecasting performance of the two-component Beta-t-QVAR-M-lev and two-component GARCH-M (generalized autoregressive conditional heteroscedasticity), also considering their one-component frameworks. The results for G20 stock market indices indicate that the forecasting performance of the two-component Beta-t-QVAR-M-lev is superior compared with the two-component GARCH-M and their one-component versions.