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: | 0.5.1 |
| Depends: | R (≥ 3.5.0) |
| 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: | 2019-12-05 |
| 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 https://ssrn.com/abstract=3435667 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | ldhmm results |
| Reference manual: | ldhmm.pdf |
| Package source: | ldhmm_0.5.1.tar.gz |
| Windows binaries: | r-devel: ldhmm_0.5.1.zip, r-release: ldhmm_0.5.1.zip, r-oldrel: ldhmm_0.5.1.zip |
| macOS binaries: | r-release: ldhmm_0.5.1.tgz, r-oldrel: ldhmm_0.5.1.tgz |
| Old sources: | ldhmm archive |
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