A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

Torre-Torres, Oscar V. De la; Galeana-Figueroa, Evaristo; Álvarez‐García, José

doi:10.3390/en13010129

Cited by 16 publications

(15 citation statements)

References 71 publications

(125 reference statements)

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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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“…For the specific case of trading decisions in energy futures, we found the work of De la Torre-Torres, Galeana-Figueroa and Álvarez-García [83] who tested the use of MS and MS-GARCH models in the Oil and Natural gas markets. By using the 1-month contract weekly historic data of each commodity future, the authors simulated a portfolio that used the aforementioned investment strategy from June 1998 to February 2019 and found that only the use of a Gaussian MS model leads to an overperformance in the oil market.…”

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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Carbon Taxation and Electricity Price Dynamics: Empirical Evidence from the Australian Market

Comincioli,

Guerini,

Vergalli

2024

Environ Resource Econ

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In this paper, we study the change of Australian electricity price dynamics that was observed before, during and after the two-year period in which a Carbon Pricing Mechanism was in force. We fit a two-states Markov Switching Model, representing a high- and a low-volatility state of the world. To avoid the interference due to periodic patterns, a deseasonalization process accounting for short- and long-term seasonality is carried out prior to the study of volatility. Estimation results highlight that, during the period when the carbon tax applies, the volatility level is lower for both the states of the world. Furthermore, the persistence in the low-volatility state is increased in the presence of the carbon tax. This conclusion is particularly relevant for macroeconomic and investment considerations because the increased uncertainty in electricity prices can significantly influence firms’ investment decisions and shape inflation expectations.

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A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

Cited by 16 publications

References 71 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

Carbon Taxation and Electricity Price Dynamics: Empirical Evidence from the Australian Market

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