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).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.6.1 |
rolling linux/jammy R-4.5 | ldhmm_0.6.1.tar.gz |
1.4 MiB |
0.6.1 |
rolling linux/noble R-4.5 | ldhmm_0.6.1.tar.gz |
1.4 MiB |
0.6.1 |
rolling source/ R- | ldhmm_0.6.1.tar.gz |
1.2 MiB |
0.6.1 |
latest linux/jammy R-4.5 | ldhmm_0.6.1.tar.gz |
1.4 MiB |
0.6.1 |
latest linux/noble R-4.5 | ldhmm_0.6.1.tar.gz |
1.4 MiB |
0.6.1 |
latest source/ R- | ldhmm_0.6.1.tar.gz |
1.2 MiB |
0.6.1 |
2026-04-26 source/ R- | ldhmm_0.6.1.tar.gz |
1.2 MiB |
0.6.1 |
2026-04-23 source/ R- | ldhmm_0.6.1.tar.gz |
1.2 MiB |
0.6.1 |
2026-04-09 windows/windows R-4.5 | ldhmm_0.6.1.zip |
1.4 MiB |
0.6.1 |
2025-04-20 source/ R- | ldhmm_0.6.1.tar.gz |
1.2 MiB |