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Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data
Joint Authors
Yeshiwas, Dawit
Berelie, Yebelay
Source
Journal of Probability and Statistics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-04
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers.
This paper 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 high-frequency intraday data which allows for a more precise realized volatility measurement.
The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance.
The analysis points to the conclusion that the 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.
American Psychological Association (APA)
Yeshiwas, Dawit& Berelie, Yebelay. 2020. Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data. Journal of Probability and Statistics،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1190163
Modern Language Association (MLA)
Yeshiwas, Dawit& Berelie, Yebelay. Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data. Journal of Probability and Statistics No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1190163
American Medical Association (AMA)
Yeshiwas, Dawit& Berelie, Yebelay. Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data. Journal of Probability and Statistics. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1190163
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190163