Hidden Markov Model (HMM) based on symmetric lambda distribution framework is implemented for the study of return timeseries in the financial market. Major features in the S&P500 index, such as regime identification, volatility clustering, and anticorrelation between return and volatility, can be extracted from HMM cleanly. Univariate symmetric lambda distribution is essentially a locationscale 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 


Author  Stephen HT. Lihn [aut, cre] 
Maintainer  Stephen HT. Lihn <stevelihn@gmail.com> 
License  Artistic2.0 
Version  0.5.1 
URL  https://ssrn.com/abstract=2979516 https://ssrn.com/abstract=3435667 
Package repository  View on CRAN 
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