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).


ldhmm 0.1.1

2017-04-13: The first public release. MLE optimizer is implemented according to Zucchini's book on HMM.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.5.1 by Stephen H-T. Lihn, a year ago

https://ssrn.com/abstract=2979516 https://ssrn.com/abstract=3435667

Browse source code at https://github.com/cran/ldhmm

Authors: Stephen H-T. Lihn [aut, cre]

Documentation:   PDF Manual  

Artistic-2.0 license

Imports stats, utils, ecd, optimx, xts, zoo, moments, parallel, graphics, scales, ggplot2, grid, methods

Suggests knitr, testthat, depmixS4, roxygen2, R.rsp, shape

See at CRAN