Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes

TL;DR

This paper introduces an iteratively reweighted adaptive lasso method for high-dimensional autoregressive time series with conditional heteroscedasticity, improving estimation accuracy over traditional homoscedastic models.

Contribution

It develops a novel reweighted adaptive lasso algorithm tailored for heteroscedastic time series, with theoretical analysis and practical applications to complex financial data.

Findings

The proposed method outperforms homoscedastic lasso in simulations.

Efficient computation of multivariate AR-ARCH models is demonstrated.

Applications to electricity and metal price data show improved modeling.

Abstract

Shrinkage algorithms are of great importance in almost every area of statistics due to the increasing impact of big data. Especially time series analysis benefits from efficient and rapid estimation techniques such as the lasso. However, currently lasso type estimators for autoregressive time series models still focus on models with homoscedastic residuals. Therefore, an iteratively reweighted adaptive lasso algorithm for the estimation of time series models under conditional heteroscedasticity is presented in a high-dimensional setting. The asymptotic behaviour of the resulting estimator is analysed. It is found that the proposed estimation procedure performs substantially better than its homoscedastic counterpart. A special case of the algorithm is suitable to compute the estimated multivariate AR-ARCH type models efficiently. Extensions to the model like periodic AR-ARCH, threshold AR-ARCH or ARMA-GARCH are discussed. Finally, different simulation results and applications to electricity market data and returns of metal prices are shown.