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).

Maintainer: Stephen H-T. Lihn <stevelihn at gmail.com>

Author(s): Stephen H-T. Lihn*

Install package and any missing dependencies by running this line in your R console:

install.packages("ldhmm")

Depends R (>= 3.3.3)
Imports stats, utils, ecd, optimx, xts(>=0.10-0), zoo, moments, parallel, graphics, scales, ggplot2, grid, methods
Suggests knitr, testthat, depmixS4, roxygen2, R.rsp, shape
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Package ldhmm
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URL https://ssrn.com/abstract=2979516
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Version 0.4.2
Published 2017-08-05
License Artistic-2.0
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NeedsCompilation no
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Package source ldhmm_0.4.2.tar.gz