Estimation and Application of Multi-Threshold Volatility Models Based on LAD-LASSO
We study the estimation and application of a class of Threshold-Conditional Heteroscedas-ticity Autoregressive Models(T-CHARM)with Multiple Thresholds.Considering the unknown number of thresholds and the heavy-tailed property of financial data,we use the Least Absolute Deviation Lasso(LAD-LASSO)algorithm to estimate the unknown parameters and the number of thresholds simultane-ously.This method remains effective even when the fourth moment of the model error term does not exist,which relaxes the applicability of the classical LASSO method in threshold models.Monte Carlo simulations show that the LAD-LASSO method has excellent finite sample performance in terms of the correct identification rate of threshold numbers,estimation of threshold values,and volatility parameter values.The proposed LAD-LASSO method with the T-CHARM is applied to model and forecast the daily returns of the CSI 300 Index.Empirical results show that compared to the classical GARCH model estimated by the LAD approach,the proposed method has superior performance in both in-sample fitting and out-of-sample forecasting.
NETL
NSTL
万方数据
