Using Markov-switching models with Markov chain Monte Carlo inference methods in agricultural commodities trading
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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.
KeywordsMarkov-switching GARCH Markovian chain processes Markov chain Monte Carlo Commodities Alpha creation Financial crisis Computational finance Financial market crisis prediction Commodities market trading
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest to disclose.
This article does not contain any studies with human participants or animals performed by any of the authors.
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