ldhmm: 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).

Version: 0.4.5
Depends: R (≥ 3.3.0)
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
Published: 2018-02-28
Author: Stephen H-T. Lihn [aut, cre]
Maintainer: Stephen H-T. Lihn <stevelihn at gmail.com>
License: Artistic-2.0
URL: https://ssrn.com/abstract=2979516
NeedsCompilation: no
Materials: NEWS
CRAN checks: ldhmm results


Reference manual: ldhmm.pdf
Vignettes: ldhmm: Hidden Markov Model for Financial Time Series and In-Depth Analysis on S&P 500 Index
Package source: ldhmm_0.4.5.tar.gz
Windows binaries: r-devel: ldhmm_0.4.5.zip, r-release: ldhmm_0.4.5.zip, r-oldrel: ldhmm_0.4.5.zip
OS X binaries: r-release: ldhmm_0.4.5.tgz, r-oldrel: ldhmm_0.4.5.tgz
Old sources: ldhmm archive


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