scholarly journals Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Melike Bildirici ◽  
Özgür Ersin
Keyword(s):  
Neural Networks ◽  
Stock Returns ◽  
Emerging Market ◽  
Markov Switching ◽  
Forecast Accuracy ◽  
Garch Model ◽  
Stock Index ◽  
Garch Models ◽  
Approximation Properties ◽  
Asymmetric Power

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.

NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI

1999 ◽  
Vol 02 (02) ◽  
pp. 221-241 ◽  
Author(s):  
JINGTAO YAO ◽  
CHEW LIM TAN ◽  
HEAN-LEE POH
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Exchange ◽  
Emerging Market ◽  
Neural Nets ◽  
Stock Index ◽  
Range Analysis ◽  
The Neural Network ◽  
Rescaled Range Analysis ◽  
Stock Index Forecasting

This paper presents a study of artificial neural nets for use in stock index forecasting. The data from a major emerging market, Kuala Lumpur Stock Exchange, are applied as a case study. Based on the rescaled range analysis, a backpropagation neural network is used to capture the relationship between the technical indicators and the levels of the index in the market under study over time. Using different trading strategies, a significant paper profit can be achieved by purchasing the indexed stocks in the respective proportions. The results show that the neural network model can get better returns compared with conventional ARIMA models. The experiment also shows that useful predictions can be made without the use of extensive market data or knowledge. The paper, however, also discusses the problems associated with technical forecasting using neural networks, such as the choice of "time frames" and the "recency" problems.

Evaluating the Forecast Accuracy of Emerging Market Stock Returns

2008 ◽  
Vol 44 (1) ◽  
pp. 21-40 ◽  
Author(s):  
Andre Carvalhal ◽  
Beatriz Vaz de Melo Mendes
Keyword(s):  
Stock Returns ◽  
Emerging Market ◽  
Forecast Accuracy

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

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 185
Author(s):  
Oscar V. De la Torre-Torres ◽  
Francisco Venegas-Martínez ◽  
Mᵃ Isabel Martínez-Torre-Enciso
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Garch Models ◽  
Portfolio Performance ◽  
Futures Trading ◽  
Volatility Index ◽  
Investment Level ◽  
Vix Futures ◽  
High Volatility ◽  
The Impact

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.

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

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2346
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
José Álvarez-García
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Garch Models ◽  
Algorithmic Trading ◽  
Stock Trading ◽  
Markov Switching Models ◽  
Switching Models ◽  
Asymmetric Garch ◽  
The Impact ◽  
T Distribution

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.

scholarly journals Geopolitical Uncertainties and Malaysian Stock Market Returns: Do Market Conditions Matter?

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2393
Author(s):  
Mohammad Enamul Hoque ◽  
Mohd Azlan Shah Zaidi ◽  
M. Kabir Hassan
Keyword(s):  
Quantile Regression ◽  
Financial Markets ◽  
Stock Returns ◽  
Emerging Market ◽  
Markov Switching ◽  
Switching Regression ◽  
Market Returns ◽  
Market Conditions ◽  
Stock Market Returns ◽  
Country Specific

Geopolitical uncertainties have been a concern for global economies and financial markets’ participants. By employing Markov switching regression and quantile regression, we investigated the effect of global and country-specific geopolitical uncertainties on Malaysian Conventional and Islamic stock returns in different market conditions. The estimated results of the Markov switching regression show that Malaysian conventional and Islamic stocks react differently to global and country-specific geopolitical uncertainties under different market volatility conditions, implying volatility dependent exposures and reactions to global and country-specific geopolitical uncertainties. The quantile regression results also reveal that Malaysian conventional and Islamic stocks respond differently to global and country-specific geopolitical uncertainties at different market stages. The empirical findings, therefore, indicate a heterogeneous and non-linear stock reaction to geopolitical uncertainties, providing new insights into geopolitical uncertainties and stock return relationships. Hence, the results will be valuable for asset pricing and investments in an emerging market such as the Malaysian market.

scholarly journals Comparing the Leverage Effect of Different Frequencies of Stock Returns in an Emerging Market: A Case Study of Pakistan

2011 ◽  
Vol 3 (6) ◽  
pp. 283-288
Author(s):  
Amir Rafique
Keyword(s):  
Stock Returns ◽  
Emerging Market ◽  
Garch Model ◽  
Leverage Effect ◽  
Low Frequencies ◽  
High Frequencies ◽  
Asymmetric Shocks ◽  
Egarch Model ◽  
Variance Structure

This study compares the volatility behavior and variance structure of high (daily) and low (weekly, monthly) frequencies of data. The study used seventeen years data from 1991 to 2008 of KSE-100 index. By employing Exponential GARCH (EGARCH) model (asymmetric type GARCH model), the study finds evidence that there are significant asymmetric shocks (leverage effect) to volatility in the three series but the intensity of the shocks are not equal for all the series. The results show that the variance structure of high frequencies data is dissimilar from the low frequencies data.

Contagion of COVID-19 pandemic between oil and financial assets: the evidence of multivariate Markov switching GARCH models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Achraf Ghorbel ◽  
Ahmed Jeribi
Keyword(s):  
Crude Oil ◽  
Markov Switching ◽  
Stock Index ◽  
Garch Models ◽  
Financial Assets ◽  
Volatility Spillover ◽  
Safe Haven ◽  
Content Type ◽  
Time Frequency ◽  
Stock Indexes

Purpose In this paper, we investigate empirically the time-frequency co-movement between the recent COVID-19 pandemic, G7stock markets, gold, crude oil price (WTI) and cryptocurrency markets (bitcoin) using both the multivariate MSGARCH models. Design/methodology/approach This paper examines the relationship between the volatilities of oil, Chinese stock index and financial assets (cryptocurrency, gold, and G7 stock indexes), for the period January 17th 2020 to December 10th 2020. It tests the presence of regime changes in the GARCH volatility dynamics of bitcoin, gold, Chinese, and G7 stock indexes as well as oil prices by using Markov–Switching GARCH model. Also, the paper estimates the dynamic correlation and volatility spillover between oil, Chinese and financial assets by using the MSBEKK-GARCH and MSDCC-GARCH models. Findings Overall, we find that all variables display a strong volatility concentrated in the first four months of Covid-19 outbreak. The paper conducts different backtesting procedures of the 1% and 5% Value-at-Risk forecasts of risk. The results find that gold has the lowest VaR. However, the Canadian and American indices have the highest VaR, for respectively 1% and 5% confidence level. The estimation results of MSBEKK-GARCH prove the volatility spillover between Chinese index, oil and financial assets. Although, the past news about shocks in the Chinese index significantly affects the current conditional volatility of financial assets. Moreover, for the high regime, the correlation increased between Chinese and G7 stock indexes which proving the contagion effect of the COVID-19 pandemic. On the contrary, the correlation decreased between Chinese-gold and Chinese-bitcoin, which confirming that gold and bitcoin can be considered as an alternative hedge for some investors during a crisis. During the COVID-19 pandemic, the correlations for the couples oil-gold and oil-bitcoin peaked. Contrary to gold, bitcoin cannot be considered as a safe haven during the global pandemic when investing in crude oil. Originality/value In contrast, comparative analysis in terms of responses to US COVID-19 pandemic, the US Covid-19 confirmed cases have relative higher impact on the co-movement in WTI and bitcoin. This paper confirms that gold is a safe haven during the COVID19 pandemic period.

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