ldhmm
0.6.1Hidden Markov Model for Financial Time-Series Based on Lambda Distribution
Overview
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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- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-06-0812 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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48 44 exported
Complexity
4.6 avg / 20 max
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48 nodes / 56 edges
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People & History
6 releases. Pick two to compare their code metrics; R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.6.1Latest2023-12-11 · current release
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.5.12019-12-05 · diff ↗
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 0.4.52018-02-28 · diff ↗
- 0.4.22017-08-05 · diff ↗
- 0.4.12017-06-03 · diff ↗
- RR 3.4.0 released · 2017-04-21
- 0.1.02017-04-13
Package metadata
- First published
- 2017-04-13
- Total releases
- 6 / 9 yrs
- License
- Artistic-2.0 OSI
- Download size
- not tracked yet
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- With dependencies
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