ldhmm
Hidden Markov Model for Financial Time-Series Based on Lambda Distribution
Last update: 11 December 2023
Copyright license: Artistic License 2.0
Software version identifier: 0.5.1, 0.1.0, 0.4.1, 0.4.2, 0.4.5, 0.6.1
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).
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