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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=ldhmm to link to this page.
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