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

verfasst von: Oscar V. De la Torre-Torres, Dora Aguilasocho-Montoya, José Álvarez-García, Biagio Simonetti

Erschienen in: Soft Computing | Ausgabe 18/2020

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Abstract

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.

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A method that later Pelletier (2006) would improve without the presence of factors and Rotta and Valls Pereira (2016)would use for the Latin American case.

The most relevant research in the subject will be discussed in the next section.

The previous literature review gives some proofs of this statement.

With zero mean and a finite scale parameter for each regime.

With finite degrees of freedom \( (\nu_{s} ) \).

For the sake of simplicity in the calculations made in our simulations, an MS-GARCH model with only one lag in the ARCH and GARCH terms will be used. This was done by following the estimation method of the MSGARCH (2016) library that only estimates one lag in these terms.

With the method proposed by Kim (1994).

That is, the same pdf at \( t \) for both regimes.

As will be mentioned in more detail in the next two sections, weekly simulations of the investment system were made from 7 January 2000 to 29 March 2019. As a consequence, the simulation period has 1004 weeks or simulation dates.

With \( p_{D} = \left( {\text{TM}} \right)^{ - 1} \sum\nolimits_{m = 1}^{M} {\sum\nolimits_{t = 1}^{T} {{\text{LLF}}\left( {r,\theta_{i,j} } \right) - {\text{LLF}}\left( {r,{\acute{\theta}}} \right)} } \) and \( {\acute{\theta}} = \left( {NM} \right)^{ - 1} \sum\nolimits_{m = 1}^{M} {\sum\nolimits_{t = 1}^{T} {\theta_{i,j} } } \). Being \( M \) the number of simulations and \( T \) the time series length.

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Titel: Using Markov-switching models with Markov chain Monte Carlo inference methods in agricultural commodities trading
verfasst von: Oscar V. De la Torre-Torres
Dora Aguilasocho-Montoya
José Álvarez-García
Biagio Simonetti
Publikationsdatum: 14.01.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 18/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04629-5

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