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 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.
Highlights
Given the current computer and financial econometrics advances in the last 15 years, the modeling of the non-rational behavior among investors and the need for quantitative models to determine trading signs have evolved
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 generalized error distribution (GED) likelihood function (LLF) and an asymmetric E-generalized autoregressive conditional heteroskedasticity (GARCH) variance model
As mentioned in the previous section, we present the performance of the 36 simulated portfolios in 6 parts or groups given the homogeneous pdf used to estimate the MS-GARCH model
Summary
Given the current computer and financial econometrics advances in the last 15 years, the modeling of the non-rational behavior among investors and the need for quantitative models to determine trading signs have evolved. Classical financial economics theories and assumptions do not hold entirely in real life, these have been a good starting point to explain some financial phenomena These theories lack a proper explanation of asset price bubbles or financial market crashes. A potential explanation of these phenomena was proposed by Black [2], who suggested the existence of two types of financial market agents, i.e., (1) the informed traders, who act with a broad and complete information set, and (2) the noisy ones, who lack a proper set and decide according to their sentiment This explanation led to developing asset pricing models, such as the ones of Fama and French [3,4] or Carhart [5], that extend the original CAPM of Sharpe [6] and Lintner [7]. These two models explain some anomalies that the original capital asset pricing model (CAPM) and the efficient market hypothesis could not [8]
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