Multivariate generalized autoregressive score model (case study: vegetable oils and crude oil price data)

Abstract

A multivariate econometric model can be used to forecast volatilities and dynamic correlations between various assets. Volatilities and dynamic correlations forecasting are important applied in risk management, hedging, asset allocation, and asset pricing options. This study uses a multivariate Generalized Autoregressive Score (GAS) model to analyze and forecast volatilities and dynamic correlations of the weekly prices of Crude Palm Oil, coconut oil, soybean oil, and crude oil. The GAS model is a new framework based on a score-driven time series model for updating time-varying parameters. Another multivariate econometric model is the Dynamic Conditional Correlation (DCC) GARCH model as a comparison. The empirical experiments regarding the distribution of return data show that the data can be approximated by the multivariate t-student distribution. The likelihood ratio test in the multivariate GAS model shows that the time-varying parameters in the GAS model are volatility, correlation, and location parameters. The evaluation performance model based on RMSE and MAE show that the multivariate GAS model has better performance in estimating and forecasting volatilities than the DCC GARCH model. Multivariate GAS models have better performance in estimating and forecasting the dynamic correlation on returns of CPO and soybean oil.