ldhmm (0.4.5)

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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).

Maintainer: Stephen Horng-Twu Lihn
Author(s): Stephen H-T. Lihn [aut, cre]

License: Artistic-2.0

Uses: ecd, ggplot2, moments, optimx, scales, xts, zoo, R.rsp, depmixS4, shape, testthat, roxygen2, knitr

Released 4 months ago.

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