ldhmm: Hidden Markov Model for Financial 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 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 |
Documentation:
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