Using Markov-switching models with Markov chain Monte Carlo inference methods in agricultural commodities trading
| Autor: | Oscar V. De la Torre-Torres, José Álvarez-García, Biagio Simonetti, Dora Aguilasocho-Montoya |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Agricultural commodity Markov chain Autoregressive conditional heteroskedasticity Gaussian Inference Markov chain Monte Carlo 02 engineering and technology Passive management Theoretical Computer Science Treasury symbols.namesake 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Econometrics Economics symbols 020201 artificial intelligence & image processing Geometry and Topology Software |
| Zdroj: | Soft Computing. 24:13823-13836 |
| ISSN: | 1433-7479 1432-7643 |
| DOI: | 10.1007/s00500-019-04629-5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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