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ldhmm  

Hidden Markov Model for Financial Time-Series Based on Lambda Distribution
View on CRAN: Click here


Download and install ldhmm package within the R console
Install from CRAN:
install.packages("ldhmm")

Install from Github:
library("remotes")
install_github("cran/ldhmm")

Install by package version:
library("remotes")
install_version("ldhmm", "0.6.1")



Attach the package and use:
library("ldhmm")
Maintained by
Stephen H-T. Lihn
[Scholar Profile | Author Map]
First Published: 2017-04-13
Latest Update: 2023-12-11
Description:
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).
How to cite:
Stephen H-T. Lihn (2017). ldhmm: Hidden Markov Model for Financial Time-Series Based on Lambda Distribution. R package version 0.6.1, https://cran.r-project.org/web/packages/ldhmm. Accessed 11 Apr. 2025.
Previous versions and publish date:
0.1.0 (2017-04-13 22:54), 0.4.1 (2017-06-03 23:38), 0.4.2 (2017-08-05 16:45), 0.4.5 (2018-02-28 15:36), 0.5.1 (2019-12-05 21:20)
Other packages that cited ldhmm R package
View ldhmm citation profile
Other R packages that ldhmm depends, imports, suggests or enhances
Complete documentation for ldhmm
Functions, R codes and Examples using the ldhmm R package
Some associated functions: ecld-class . ecld.cdf . ecld . ecld.pdf . ecld.sd . ldhmm-class . ldhmm-package . ldhmm.calc_stats_from_obs . ldhmm.conditional_prob . ldhmm.decode_stats_history . ldhmm.decoding . ldhmm.df2ts . ldhmm.forecast_prob . ldhmm.forecast_state . ldhmm.forecast_volatility . ldhmm.fred_data . ldhmm.gamma_init . ldhmm.get_data . ldhmm.ld_stats . ldhmm.log_forward . ldhmm.mle . ldhmm.mllk . ldhmm.n2w . ldhmm . ldhmm.plot_spx_vix_obs . ldhmm.pseudo_residuals . ldhmm.read_csv_by_symbol . ldhmm.read_sample_object . ldhmm.simulate_abs_acf . ldhmm.simulate_state_transition . ldhmm.sma . ldhmm.state_ld . ldhmm.state_pdf . ldhmm.ts_abs_acf . ldhmm.ts_log_rtn . ldhmm.viterbi . ldhmm.w2n . numericOrNull-class . 
Some associated R codes: ecld-cdf-method.R . ecld-class.R . ecld-constructor.R . ecld-pdf-method.R . ecld-sd-method.R . ldhmm-calc_stats_from_obs.R . ldhmm-class.R . ldhmm-conditional_prob.R . ldhmm-constructor.R . ldhmm-data-config-internal.R . ldhmm-decode_stats_history.R . ldhmm-decoding.R . ldhmm-df2ts-method.R . ldhmm-forecast_prob.R . ldhmm-forecast_state.R . ldhmm-forecast_volatility.R . ldhmm-fred_data.R . ldhmm-gamma_init.R . ldhmm-get-data-method.R . ldhmm-ld_stats.R . ldhmm-log_forward.R . ldhmm-mle.R . ldhmm-mllk.R . ldhmm-n2w.R . ldhmm-numericOrNull-class.R . ldhmm-package.R . ldhmm-plot_spx_vix_obs.R . ldhmm-pseudo_residuals.R . ldhmm-read-csv-by-symbol-method.R . ldhmm-read_sample_object.R . ldhmm-simulate_abs_acf.R . ldhmm-simulate_state_transition.R . ldhmm-sma.R . ldhmm-state_ld.R . ldhmm-state_pdf.R . ldhmm-ts_abs_acf.R . ldhmm-ts_log_rtn.R . ldhmm-viterbi.R . ldhmm-w2n.R .  Full ldhmm package functions and examples
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