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Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading
2021
Symmetry
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)
doi:10.3390/sym13122346
fatcat:oaggvtra3nc6tej6ghitlatd64