ldhmm: Hidden Markov Model for Financial Time-Series Based on LambdaDistribution
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=2979516https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3435667 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | ldhmm results |
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