Forecasting the Co-Volatility of Coffee Arabica and Crude Oil Prices: A Multivariate Garch Approach with High Frequency Data

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Addis Abeba university


Forecasting the volatility dynamics of asset returns has been the subject of extensive research among academics, practitioners and portfolio managers. This thesis estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of coffee Arabica, and compares the forecasting performance of these models based on a high frequency intra-day data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model co-volatility, and employed high-frequency intra-day data to assess model forecasting performance. The analysis points to the conclusion that varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility, and employ high-frequency data whenever feasible. Keywords: commodity price co-volatility, conditional correlation, forecasting, multivariate GARCH



Commodity Price Co-Volatility, Conditional Correlation, Forecasting, Multivariate GARCH