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

Torre-Torres, Oscar V. De la; Aguilasocho-Montoya, Dora; Álvarez‐García, José; Simonetti, Biagio

doi:10.1007/s00500-019-04629-5

Cited by 13 publications

(12 citation statements)

References 72 publications

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“…These authors tested this use in U.K. stocks. This use was later developed by several authors [18][19][20][21] in stock markets and other commodity futures such as oil, natural gas, corn, or soybean [22,23].…”

Section: Discussionmentioning

confidence: 99%

“…With these forecasted probabilities, the trader or portfolio manager could follow a trading algorithm similar to the one of Brooks and Persand [17] or the one tested by De la Torre-Torres, Galeana-Figueroa, and Álvarez-García [20,22] or De la Torre-Torres et al [23]:…”

Section: The Rationale Behind Ms and Ms-garch Models The Input Data Processing Method And The Trading Strategy Simulation's Parametersmentioning

confidence: 99%

“…For the specific case of active trading with MS or MS-GARCH models in commodity future markets, we can mention the works of De la Torre-Torres, Galeana-Figueroa, and Álvarez-García [22] and De la Torre-Torres, Aguilasocho-Montoya, and Álvarez-García [23]. These authors tested the use of Gaussian MS and MS-GARCH models in the west Texas intermediate oil, the natural gas, the corn, and soybean markets.…”

Section: Literature Review Of the Use Of Ms-garch Modelsmentioning

confidence: 99%

“…For the specific case of the use of MS models for trading in agricultural soft commodity markets, little has been written. Only their use in energy futures [22] and in the corn or soy bean markets [23] has been tested. Given this, our interest was to fill the gap in the literature about active trading in the most traded soft agricultural commodities [24]: Coffee, cocoa, and sugar.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

2020

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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.

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Section: Discussionmentioning

confidence: 99%

Section: The Rationale Behind Ms and Ms-garch Models The Input Data Processing Method And The Trading Strategy Simulation's Parametersmentioning

confidence: 99%

Section: Literature Review Of the Use Of Ms-garch Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

2020

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“…Finally in the Agricultural futures markets, we can mention the works of Valera and Lee [67], De la Torre-Torres, Aguilasocho-Montoya and Del Río-Rama [85] and De la Torre-Torres et al [86]. These works test the benefits of MS and MS-GARCH models in commodity spot prices and futures.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Torre-Torres

Venegas-Martı́nez

Martínez-Torre-Enciso

2021

Mathematics

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In the present paper, we test the use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance the performance of a U.S. dollar-based portfolio that invest in the S&P 500 (SP500) stock index, the 3-month U.S. Treasury-bill (T-BILL) or the 1-month volatility index (VIX) futures. For the investment algorithm, we propose the use of two and three-regime, Gaussian and t-Student, MS and MS-GARCH models. This is done to forecast the probability of high volatility episodes in the SP500 and to determine the investment level in each asset. To test the algorithm, we simulated 8 portfolios that invested in these three assets, in a weekly basis from 23 December 2005 to 14 August 2020. Our results suggest that the use of MS and MS-GARCH models and VIX futures leads the simulated portfolio to outperform a buy and hold strategy in the SP500. Also, we found that this result holds only in high and extreme volatility periods. As a recommendation for practitioners, we found that our investment algorithm must be used only by institutional investors, given the impact of stock trading fees.

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Deep learning based regime-switching models of energy commodity prices

Mari

2022

Energy Syst

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We discuss a deep learning based approach to model the complex dynamics of commodity prices observed in real markets. A regime-switching model is proposed to describe the time evolution of market prices. In this model, the base regime is described by a mean-reverting diffusion process and the second regime is driven by the predictions of a deep neural network trained on the market log-returns time series. A statistical technique, based on the method of simulated moments, is proposed to estimate the model on market data. We applied this methodology to energy commodity price time series with very different characteristics, namely the US wholesale electricity, natural gas and crude oil price daily time series. The obtained results show a good agreement with empirical data. In particular, the model seems to reproduce in a very interesting way the first four central moments of the empirical distributions of log-returns as well as the shape of the observed price time series.

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

Cited by 13 publications

References 72 publications

A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Deep learning based regime-switching models of energy commodity prices

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