Forecasting the Volatility of Coffee Arabica and Crude Oil Prices with a High Frequency Data

Abstract

Modeling, analyzing, and forecasting volatility has been the subject of extensive research among academics, practitioners and portfolio managers. This paper estimates a variety of 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 volatility, and employed high-frequency intra-day data to assess model forecasting performance. The analysis points to the conclusion that GARCH (1,1) for Arabica coffee and GARCH (1,2) crude oil returns were best models, respectively with Student’s t distributed innovation terms is the most accurate volatility forecasting models in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of GARCH models to pay particular attention to the measurement of realized volatility, and employ high-frequency data whenever feasible.