ldhmm: Hidden Markov Model for Return 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 power-exponential 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: 0.1.0
Depends: R (≥ 3.3.1)
Imports: stats, utils, ecd, xts, zoo, moments, parallel, graphics, methods
Suggests: knitr, testthat, depmixS4, roxygen2, scales, shape
Published: 2017-04-13
Author: Stephen H-T. Lihn [aut, cre]
Maintainer: Stephen H-T. Lihn <stevelihn at gmail.com>
License: Artistic-2.0
NeedsCompilation: no
Materials: NEWS
CRAN checks: ldhmm results

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Reference manual: ldhmm.pdf
Package source: ldhmm_0.1.0.tar.gz
Windows binaries: r-devel: ldhmm_0.1.0.zip, r-release: ldhmm_0.1.0.zip, r-oldrel: ldhmm_0.1.0.zip
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: ldhmm_0.1.0.tgz

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