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    A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-06-18
    Description: In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.
    Electronic ISSN: 2227-7390
    Topics: Mathematics
    Published by Molecular Diversity Preservation International
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  • 2
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    Using Markov-switching models with Markov chain Monte Carlo inference methods in agricultural commodities trading (2021)
    Springer-Verlag
    Details
    Publication Date: 2021-05-12
    Description: In this work, the use of Markov-switching GARCH (MS-GARCH) models is tested in an active trading algorithm for corn and soybean future markets. By assuming that a given investor lives in a two-regime world (with low- and high-volatility time periods), a trading algorithm was simulated (from January 2000 to March 2019), which helped the investor to forecast the probability of being in the high-volatility regime at t ? 1. Once this probability was known, the investor could decide to invest either in commodities, during low-volatility periods or in the 3-month US Treasury bills, during high-volatility periods. Our results suggest that the Gaussian MS-GARCH model is the most appropriate to generate alpha or extra returns (from a passive investment strategy) in the corn market and the t-Student MS-GARCH is the best one for soybean trading.
    Description: Published
    Description: 13823–13836
    Description: 7SR AMBIENTE – Servizi e ricerca per la società
    Description: JCR Journal
    Keywords: Markov-switching GARCH ; Markovian chain processes
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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