Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to ARARCH type processes
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 ARARCH type models efficiently. Extensions to the model like periodic ARARCH, threshold ARARCH or ARMAGARCH are discussed. Finally, different simulation results and applications to electricity market data and returns of metal prices are shown.
lasso conditional heteroscedasticity ararch high-dimensional time series autoregressive process volatility