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).
Package details |
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Author | Stephen H-T. Lihn [aut, cre] |
Maintainer | Stephen H-T. Lihn <stevelihn@gmail.com> |
License | Artistic-2.0 |
Version | 0.6.1 |
URL | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2979516 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3435667 |
Package repository | View on CRAN |
Installation |
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