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).