Hidden Markov Model for Return 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 power-exponential 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).
stats, utils, ecd, xts, zoo, moments, parallel, graphics,
methods
knitr, testthat, depmixS4, roxygen2, scales, shape
'ldhmm-calc_stats_from_obs.R' 'ldhmm-numericOrNull-class.R'
'ldhmm-package.R' 'ldhmm-class.R' 'ldhmm-conditional_prob.R'
'ldhmm-constructor.R' 'ldhmm-decoding.R'
'ldhmm-forecast_prob.R' 'ldhmm-forecast_state.R'
'ldhmm-ld_stats.R' 'ldhmm-log_forward.R' 'ldhmm-mle.R'
'ldhmm-mllk.R' 'ldhmm-n2w.R' 'ldhmm-pseudo_residuals.R'
'ldhmm-state_ld.R' 'ldhmm-state_pdf.R' 'ldhmm-ts_abs_acf.R'
'ldhmm-viterbi.R' 'ldhmm-w2n.R' 'ldhmm.ts_log_rtn.R'

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).
stats, utils, ecd, optimx, xts, zoo, moments, parallel,
graphics, scales, ggplot2, grid, methods
knitr, testthat, depmixS4, roxygen2, R.rsp, shape
'ldhmm-calc_stats_from_obs.R' 'ldhmm-numericOrNull-class.R'
'ldhmm-package.R' 'ldhmm-class.R' 'ldhmm-conditional_prob.R'
'ldhmm-constructor.R' 'ldhmm-decode_stats_history.R'
'ldhmm-decoding.R' 'ldhmm-forecast_prob.R'
'ldhmm-forecast_state.R' 'ldhmm-forecast_volatility.R'
'ldhmm-fred_data.R' 'ldhmm-gamma_init.R' 'ldhmm-ld_stats.R'
'ldhmm-log_forward.R' 'ldhmm-mle.R' 'ldhmm-mllk.R'
'ldhmm-n2w.R' 'ldhmm-oxford_man_index_list.R'
'ldhmm-oxford_man_plot_obs.R'
'ldhmm-oxford_man_realized_data.R' 'ldhmm-oxford_man_ts.R'
'ldhmm-plot_spx_vix_obs.R' 'ldhmm-pseudo_residuals.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'