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: 0.6.1
Depends: R (≥ 4.2.0)
Imports: stats, utils, gnorm, optimx, xts (≥ 0.10-0), zoo, moments, parallel, graphics, scales, ggplot2, grid, yaml, methods
Suggests: knitr, testthat, depmixS4, roxygen2, R.rsp, shape
Published: 2023-12-11
DOI: 10.32614/CRAN.package.ldhmm
Author: Stephen H-T. Lihn [aut, cre]
Maintainer: Stephen H-T. Lihn <stevelihn at gmail.com>
License: Artistic-2.0
URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2979516 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3435667
NeedsCompilation: no
Materials: NEWS
CRAN checks: ldhmm results


Reference manual: ldhmm.pdf


Package source: ldhmm_0.6.1.tar.gz
Windows binaries: r-devel: ldhmm_0.6.1.zip, r-release: ldhmm_0.6.1.zip, r-oldrel: ldhmm_0.6.1.zip
macOS binaries: r-release (arm64): ldhmm_0.6.1.tgz, r-oldrel (arm64): ldhmm_0.6.1.tgz, r-release (x86_64): ldhmm_0.6.1.tgz, r-oldrel (x86_64): ldhmm_0.6.1.tgz
Old sources: ldhmm archive


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