Using Markov-switching models with Markov chain Monte Carlo inference methods in agricultural commodities trading

被引：0
|
作者：
Oscar V. De la Torre-Torres
Dora Aguilasocho-Montoya
José Álvarez-García
Biagio Simonetti
机构：
[1] Michoacán State University of Saint Nicholas and Hidalgo (UMSNH),Faculty of Accounting and Management
[2] University of Extremadura,Financial Economy and Accounting Department, Faculty of Business, Finance and Tourism
[3] University of Sannio,undefined
[4] WSB University in Gdansk,undefined
[5] National Institute of Geophysics and Volcanology (INGV),undefined
来源：
Soft Computing | 2020年 / 24卷
关键词：
Markov-switching GARCH; Markovian chain processes; Markov chain Monte Carlo; Commodities; Alpha creation; Financial crisis; Computational finance; Financial market crisis prediction; Commodities market trading;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：13823 / 13836
页数：13
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