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

Torre-Torres, Oscar V. De la; Aguilasocho-Montoya, Dora; Río‐Rama, María de la Cruz del

doi:10.3390/math8061001

Cited by 8 publications

(10 citation statements)

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“…One of the advantages of the MS-GARCH typed model is that it can be used to characterize and forecast the probability of being in a high-or low-volatility regime [34]. Therefore, the regime probabilities obtained from the MS-Beta-t-EGARCH are illustrated in Fig 3.…”

Section: A Estimation Results For Different Volatility Modelsmentioning

confidence: 99%

“…Thus, they suggested using the Markov Switching GARCH (MS-GARCH) model to cope with the exchange rate's low-and high-volatility regime. The application of this model can be found in the work of la Torre-Torres et al [34]. Although this model has shown good performance in volatility forecasting, it seems not to take into account the asymmetric leverage effects for the conditional volatility.…”

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Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

2020

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The motivation of this study is built from the previous research to find a way to enhance the forecast of advanced and emerging market currency volatilities. Given the exchange rate's nonlinear and time-varying characteristics, we introduce the neural networks (NN) approach to enhance the Markov Switching Beta-Exponential Generalized Autoregressive Conditional Heteroscedasticity (MS-Betat-EGARCH) model. Our hybrid model synthesizes these two approaches' advantages to predict exchange rate volatility. We validate the performance of our proposed model by comparing it with various traditional volatility forecasting models. In-sample and out-of-sample volatility forecasts are considered to achieve our comparison. The empirical results suggest that our hybrid NN-MS Beta-t-EGARCH outperforms the other models for both emerging and advanced market currencies. INDEX TERMS Exchange rate volatility, neural networks, Markov-switching Beta-t-EGARCH. I. INTRODUCTION Volatility represents the degree to which variable changes over time, and it is an essential facet in risk evaluation of many essential economic tasks such as value at risk, financial asset pricing, and exchange rate [52]. Volatility has three significant characteristics in the financial area, namely, volatility clustering property [37], asymmetric property [10], and nonlinearity property [19]. These properties lead to uncertainty in financial time series. In the context of economic globalization, international transactions, and capital flow across the borders have increased. There is an indisputable fact that the foreign exchange market is a crucial factor affecting the transactions and capital flows. Thus, policymakers must understand the exchange rate volatility before making fiscal and monetary policy decisions, especially in those import-led and export-led countries. In this respect, our study aims to forecast the exchange rate volatility of the three emerging markets' currencies, namely the Brazilian Real, Chinese Yuan, and Indian Rupee, and three advanced markets' currencies, namely, Euro, The associate editor coordinating the review of this manuscript and approving it for publication was Francesco Benedetto .

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Section: A Estimation Results For Different Volatility Modelsmentioning

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Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

2020

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“…Other works, such as De la Torre-Torres et al's [36,37,39,71], developed similar tests in the US stock markets, energy and agricultural commodities and even in volatility futures markets. Similar to Brooks and Persand, these authors tested an MS-GARCH trading algorithm.…”

Section: Literature Reviewmentioning

confidence: 98%

“…Additionally, there are tests of MS models in trading decisions in commodities [36,37], European and US stock markets and volatility futures by using generalized autoregressive conditional heteroskedasticity (GARCH) variances in the estimated MS models (MS-GARCH) [38,39].…”

Section: Introductionmentioning

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Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading

2021

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In the present paper, we extend the current literature in algorithmic trading with Markov-switching models with generalized autoregressive conditional heteroskedastic (MS-GARCH) models. We performed this by using asymmetric log-likelihood functions (LLF) and variance models. From 2 January 2004 to 19 March 2021, we simulated 36 institutional investor’s portfolios. These used homogenous (either symmetric or asymmetric) Gaussian, Student’s t-distribution, or generalized error distribution (GED) and (symmetric or asymmetric) GARCH variance models. By including the impact of stock trading fees and taxes, we found that an institutional investor could outperform the S&P 500 stock index (SP500) if they used the suggested trading algorithm with symmetric homogeneous GED LLF and an asymmetric E-GARCH variance model. The trading algorithm had a simple rule, that is, to invest in the SP500 if the forecast probability of being in a calm or normal regime at t + 1 is higher than 50%. With this configuration in the MS-GARCH model, the simulated portfolios achieved a 324.43% accumulated return, of which the algorithm generated 168.48%. Our results contribute to the discussion on using MS-GARCH models in algorithmic trading with a combination of either symmetric or asymmetric pdfs and variance models.

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

Cited by 8 publications

References 64 publications

Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading

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

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