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Keywords = regime-switching log-normal model

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29 pages, 3907 KB  
Article
Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading
by Oscar V. De la Torre-Torres, Dora Aguilasocho-Montoya and José Álvarez-García
Symmetry 2021, 13(12), 2346; https://doi.org/10.3390/sym13122346 - 6 Dec 2021
Cited by 5 | Viewed by 4292
Abstract
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 [...] Read more.
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. Full article
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16 pages, 743 KB  
Article
Variational Bayes for Regime-Switching Log-Normal Models
by Hui Zhao and Paul Marriott
Entropy 2014, 16(7), 3832-3847; https://doi.org/10.3390/e16073832 - 14 Jul 2014
Cited by 3 | Viewed by 6129
Abstract
The power of projection using divergence functions is a major theme in information geometry. One version of this is the variational Bayes (VB) method. This paper looks at VB in the context of other projection-based methods in information geometry. It also describes how [...] Read more.
The power of projection using divergence functions is a major theme in information geometry. One version of this is the variational Bayes (VB) method. This paper looks at VB in the context of other projection-based methods in information geometry. It also describes how to apply VB to the regime-switching log-normal model and how it provides a computationally fast solution to quantify the uncertainty in the model specification. The results show that the method can recover exactly the model structure, gives the reasonable point estimates and is very computationally efficient. The potential problems of the method in quantifying the parameter uncertainty are discussed. Full article
(This article belongs to the Special Issue Information Geometry)
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