Forecasting the Co-Volatility of Coffee Arabica and Crude Oil Prices: A Multivariate Garch Approach with High Frequency Data
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Date
2017-06
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Addis Abeba university
Abstract
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
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Keywords
Commodity Price Co-Volatility, Conditional Correlation, Forecasting, Multivariate GARCH