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

Torre-Torres, Oscar V. De la; Venegas-Martı́nez, Francisco; Martínez-Torre-Enciso, Ma Isabel

doi:10.3390/math9020185

Cited by 8 publications

(16 citation statements)

References 91 publications

(172 reference statements)

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“…The ARMA process is the combination of the autoregressive model and moving average [ 2 ] designed for a stationary time series. Autoregression (AR) describes a stochastic process, and AR(p) can be denoted as shown below:

where:

denotes the weights given to past observations at each lag, p is a positive integer providing the number of lags to be included, and

is white noise.…”

Section: Methodology and Datamentioning

confidence: 99%

“…As demonstrated by many researchers and studies, the SGARCH (1,1) process is able to represent the majority of the time series [17]. The dataset which requires a model of higher orders like SGARCH (1,2) or SGARCH(2,1) is very rare [11]. However, financial time series inherits many characteristics that SGARCH is not able to incorporate well.…”

Section: Generalized Autoregressive Conditional Heteroscedasticity-garch(pq)mentioning

confidence: 99%

“…The ARMA process is the combination of the autoregressive model and moving average [2] designed for a stationary time series. Autoregression (AR) describes a stochastic process, and AR(p) can be denoted as shown below:…”

Section: Fundamental Concepts and Definitions Autoregressive Moving Average Models-arma(pq)mentioning

confidence: 99%

“…Over the past few decades, time series forecasting in finance has been an interesting and important research area. It has attracted the attention of not only the researcher community but also investors, speculators, and governments who are interested in verification of various models and approaches in predicting of future prices of various types of assets (Sakowski and Turovtseva, 2020 [1], Torre-Torres et al (2021) [2]) with the use of more standard tools (Goubuzaite and Teresiene, 2021 [3], Ivanyuk, 2021 [4]) or the newest ML techniques (Chlebus et al, 2021) [5] The main aim of time series modeling is to carefully measure and analyze the historical observations of the time series in order to develop the most appropriate models. The most important function of these models is to forecast future values of the time series, i.e., to predict the movements, behaviors, and changes, usually by reflecting the characteristics of the historical observations.…”

Section: Introductionmentioning

confidence: 99%

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Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index

Ślepaczuk

2022

Entropy

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This research aims to compare the performance of ARIMA as a linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index. We used the data collected from Yahoo Finance with daily frequency for the period from 1 January 2000 to 31 December 2019. By using a rolling window approach, we compared ARIMA with the hybrid models to examine whether hybrid ARIMA-SGARCH and ARIMA-EGARCH can really reflect the specific time-series characteristics and have better predictive power than the simple ARIMA model. In order to assess the precision and quality of these models in forecasting, we compared their equity lines, their forecasting error metrics (MAE, MAPE, RMSE, MAPE), and their performance metrics (annualized return compounded, annualized standard deviation, maximum drawdown, information ratio, and adjusted information ratio). The main contribution of this research is to show that the hybrid models outperform ARIMA and the benchmark (Buy&Hold strategy on S&P500 index) over the long term. These results are not sensitive to varying window sizes, the type of distribution, and the type of the GARCH model.

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where:

denotes the weights given to past observations at each lag, p is a positive integer providing the number of lags to be included, and

is white noise.…”

Section: Methodology and Datamentioning

confidence: 99%

Section: Generalized Autoregressive Conditional Heteroscedasticity-garch(pq)mentioning

confidence: 99%

“…The ARMA process is the combination of the autoregressive model and moving average [2] designed for a stationary time series. Autoregression (AR) describes a stochastic process, and AR(p) can be denoted as shown below:…”

Section: Fundamental Concepts and Definitions Autoregressive Moving Average Models-arma(pq)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index

Ślepaczuk

2022

Entropy

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“…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

confidence: 99%

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

2021

Self Cite

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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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On the analysis of time-varying causality between VIX exchange-traded products and VIX futures contracts in high and low volatility regimes

O'Neill,

Rajaguru

2024

JAL

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PurposeThe authors analyse the nature of nonlinear long-run causal dynamics between VIX futures and exchange-traded products (ETPs).Design/methodology/approachNonlinear long-run causal relations between daily price movements in ETPs and futures are established through a Markov switching vector error correction model (MS-VECM).FindingsThe authors observe time variation in causality with the volatility of volatility. In particular, demand pressures for VIX ETNs and futures can change in different regimes. The authors observe two regimes where regime 1 is classified as low-mean low-volatility, while regime 2 is classified as high-mean high-volatility. The convergence to the long-run equilibrium in the low-mean low-volatility regime is faster than the high-mean high-volatility regime. The nature of the time varying lead lag relations demonstrates the opportunities for arbitrage.Originality/valueThe linear causal relations between VXX and VIX futures are well established, with leads and lags generally found to be short-lived with arbitrage relations holding. The authors go further to capture the time-varying causal relationships through a Markovian process. The authors establish the nonlinear causal relations between inverse and leveraged products where causal relations are not yet documented.

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

Cited by 8 publications

References 91 publications

Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index

Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index

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

On the analysis of time-varying causality between VIX exchange-traded products and VIX futures contracts in high and low volatility regimes

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