- class parametricGarch.Garch(data, vol='Garch', p=1, q=1, dist='normal', update_freq=0, disp='off', horizon=1, start=None, reindex=False)[source]
Parametric bootstrapping with GARCH models.
- bootstrap(num_iterations=1000)[source]
Perform parametric bootstrapping to estimate the forecast distribution.
- Parameters:
- num_iterations: int, optional
Number of bootstrap iterations. Default is 1000.
- Returns:
bool: True if the bootstrap is successful.
- property bootstrap_samples
Get the forecasted mean and volatility list from the bootstrapped model.
- Returns:
list: List of tuples containing forecasted mean and volatility for each bootstrap iteration.
- property bootstrap_summary
Get the summary of the bootstrapped model.
- Returns:
arch.univariate.base.ARCHModelResultSummary: Summary of the bootstrapped model.
- property conditional_volatility
Get the conditional volatility of the fitted model.
- Returns:
pandas.Series: Conditional volatility series.
- estimate_risk(confidence_level=0.95, q='empirical')[source]
Estimate risk measures: volatility and Value-at-Risk (VaR) using the bootstrapped model.
- Parameters:
- confidence_level: float, optional
Confidence level for calculating VaR and volatility. Default is 0.95.
- q: str, optional
Quantile calculation method. ‘empirical’ for empirical quantile, ‘parametric’ for parametric quantile. Default is ‘empirical’.
- Returns:
- dict: Dictionary containing risk estimates including mean volatility, volatility confidence interval,
mean VaR, and VaR confidence interval.
- property forecast_mean
Get the forecasted conditional mean of the model.
- Returns:
pandas.DataFrame: Forecasted conditional mean series.
- property forecast_residual_variance
Get the forecasted conditional variance of the residuals of the model.
- Returns:
pandas.DataFrame: Forecasted conditional residual variance series.
- property forecast_variance
Get the forecasted conditional variance of the model.
- Returns:
pandas.DataFrame: Forecasted conditional variance series.
- property standardised_residuals
Get the standardized residuals of the fitted model.
- Returns:
pandas.Series: Standardized residuals series.
- property summary
Get the summary of the fitted model.
- Returns:
arch.univariate.base.ARCHModelResultSummary: Summary of the fitted model.
Python API
- class parametricGarch.parametric.Garch(data, vol='Garch', p=1, q=1, dist='normal', update_freq=0, disp='off', horizon=1, start=None, reindex=False)[source]
Bases:
objectParametric bootstrapping with GARCH models.
- bootstrap(num_iterations=1000)[source]
Perform parametric bootstrapping to estimate the forecast distribution.
- Parameters:
- num_iterations: int, optional
Number of bootstrap iterations. Default is 1000.
- Returns:
bool: True if the bootstrap is successful.
- property bootstrap_samples
Get the forecasted mean and volatility list from the bootstrapped model.
- Returns:
list: List of tuples containing forecasted mean and volatility for each bootstrap iteration.
- property bootstrap_summary
Get the summary of the bootstrapped model.
- Returns:
arch.univariate.base.ARCHModelResultSummary: Summary of the bootstrapped model.
- property conditional_volatility
Get the conditional volatility of the fitted model.
- Returns:
pandas.Series: Conditional volatility series.
- estimate_risk(confidence_level=0.95, q='empirical')[source]
Estimate risk measures: volatility and Value-at-Risk (VaR) using the bootstrapped model.
- Parameters:
- confidence_level: float, optional
Confidence level for calculating VaR and volatility. Default is 0.95.
- q: str, optional
Quantile calculation method. ‘empirical’ for empirical quantile, ‘parametric’ for parametric quantile. Default is ‘empirical’.
- Returns:
- dict: Dictionary containing risk estimates including mean volatility, volatility confidence interval,
mean VaR, and VaR confidence interval.
- property forecast_mean
Get the forecasted conditional mean of the model.
- Returns:
pandas.DataFrame: Forecasted conditional mean series.
- property forecast_residual_variance
Get the forecasted conditional variance of the residuals of the model.
- Returns:
pandas.DataFrame: Forecasted conditional residual variance series.
- property forecast_variance
Get the forecasted conditional variance of the model.
- Returns:
pandas.DataFrame: Forecasted conditional variance series.
- property standardised_residuals
Get the standardized residuals of the fitted model.
- Returns:
pandas.Series: Standardized residuals series.
- property summary
Get the summary of the fitted model.
- Returns:
arch.univariate.base.ARCHModelResultSummary: Summary of the fitted model.