Robust Estimation of High-Dimensional cDCC Model
Conference
64th ISI World Statistics Congress
Format: IPS Abstract
Session: IPS 433 - High-Dimensional Financial Time Series
Tuesday 18 July 10 a.m. - noon (Canada/Eastern)
Abstract
Volatility plays an important role in many economic and financial applications and the number of assets has increased considerably in recent years. The cDCC model has been one of the most commonly used models and composite likelihood has been used to estimate high-dimensional cases. The paper shows that this estimator can be strongly affected by additive outliers, one of the most frequent types of outliers. A robust method is proposed and it is shown that its performance is better than that of the traditional nonrobust estimator both by simulation study and by backtesting on real-life stock return data.