Hidden Markov Model for Financial Time-Series Based on Lambda Distribution

Hidden Markov Model (HMM) based on symmetric lambda distribution framework is implemented for the study of return time-series in the financial market. Major features in the S&P500 index, such as regime identification, volatility clustering, and anti-correlation between return and volatility, can be extracted from HMM cleanly. Univariate symmetric lambda distribution is essentially a location-scale family of exponential power distribution. Such distribution is suitable for describing highly leptokurtic time series obtained from the financial market. It provides a theoretically solid foundation to explore such data where the normal distribution is not adequate. The HMM implementation follows closely the book: "Hidden Markov Models for Time Series", by Zucchini, MacDonald, Langrock (2016).


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0.4.2 by Stephen H-T. Lihn, 2 months ago


Browse source code at https://github.com/cran/ldhmm

Authors: Stephen H-T. Lihn [aut, cre]

Documentation:   PDF Manual  

Artistic-2.0 license

Imports stats, utils, ecd, optimx, xts, zoo, moments, parallel, graphics, scales, ggplot2, grid, methods

Suggests knitr, testthat, depmixS4, roxygen2, R.rsp, shape

See at CRAN