Hidden Markov Model for Financial Time-Series Based on Lambda
Distribution
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Documentation for package ‘ldhmm’ version 0.6.1
Help Pages
| ldhmm-package | ldhmm: A package for HMM using lambda distribution. |
| ecld | Constructor of ecld class |
| ecld-class | An S4 class to represent the lambda distribution |
| ecld.ccdf | CDF and CCDF of ecld |
| ecld.cdf | CDF and CCDF of ecld |
| ecld.kurt | Compute statistics analytically for an ecld object |
| ecld.kurtosis | Compute statistics analytically for an ecld object |
| ecld.mean | Compute statistics analytically for an ecld object |
| ecld.pdf | Calculate the PDF of an ecld object |
| ecld.sd | Compute statistics analytically for an ecld object |
| ecld.skewness | Compute statistics analytically for an ecld object |
| ecld.var | Compute statistics analytically for an ecld object |
| ldhmm | Constructor of ldhmm class |
| ldhmm-class | The ldhmm class |
| ldhmm.calc_stats_from_obs | Computing the statistics for each state |
| ldhmm.conditional_prob | Computing the conditional probabilities |
| ldhmm.decode_stats_history | Estimating historical statistics (mean, volatility and kurtosis) |
| ldhmm.decoding | Computing the minus log-likelihood (MLLK) |
| ldhmm.df2ts | Utility to standardize timeseries from data.frame to xts |
| ldhmm.drop_outliers | Computing the statistics for each state |
| ldhmm.forecast_prob | Computing the forecast probability distribution |
| ldhmm.forecast_state | Computing the state forecast |
| ldhmm.forecast_volatility | Computing the volatility forecast for next one period |
| ldhmm.fred_data | Utility to download time series from FRED |
| ldhmm.gamma_init | Initializing tansition probability paramter |
| ldhmm.get_data | Read sample data |
| ldhmm.get_data.arr | Read sample data |
| ldhmm.get_data.ts | Read sample data |
| ldhmm.ld_stats | Computes the theoretical statistics per state |
| ldhmm.log_backward | Computing the log forward and backward probabilities |
| ldhmm.log_forward | Computing the log forward and backward probabilities |
| ldhmm.mle | Computing the MLEs |
| ldhmm.mllk | Computing the minus log-likelihood (MLLK) |
| ldhmm.n2w | Transforming natural parameters to a linear working parameter array |
| ldhmm.plot_spx_vix_obs | Plotting HMM expected volatility for SPX overlaid with adjusted VIX |
| ldhmm.pseudo_residuals | Computing pseudo-residuals |
| ldhmm.read_csv_by_symbol | Read csv file of sample data |
| ldhmm.read_sample_object | Read sample ldhmm object |
| ldhmm.simulate_abs_acf | Simulating auto-correlation (ACF) |
| ldhmm.simulate_state_transition | Simulating state transition |
| ldhmm.sma | Simple moving average of a time series |
| ldhmm.state_ld | Constructing the ecld objects per state |
| ldhmm.state_pdf | Computing the PDF per state given the observations |
| ldhmm.ts_abs_acf | Computing ACF of the absolute value of a time series |
| ldhmm.ts_log_rtn | Get log-returns from historic prices of an index |
| ldhmm.viterbi | Computing the global decoding by the Viterbi algorithm |
| ldhmm.w2n | Transforming working parameter array to natural parameters |
| numericOrNull-class | The numericOrNull class |