Considering the inferior volatility tracking capability of the point-data-based models, we propose using the more … informative price interval data and building interval regression models for volatility forecasting. To characterize the … heterogeneity of the market and the nonlinearity of volatility, we incorporated the heterogeneous autoregressive structure and the …

This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose … a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel …

Robust estimation techniques based on symmetric probability distributions are often substituted for OLS to obtain … paper show that with skewed distributed data, symmetric robust estimation techniques produce biased regression intercepts … with symmetric robust estimation techniques produce biased alphas. The results support the recommendation that robust …

conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance …-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings …

, an estimation of volatility in the DAX is provided. Such estimation is then plugged into a quantile regression model …This paper addresses stock market volatility in Germany between 1991 and 2018. Through a GARCH model with leverage term … where potential economic determinants are analyzed. The results suggest that stock market volatility in Germany reached its …

In this paper, we develop a novel high-dimensional time-varying coefficient estimation method, based on high …

We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the … individual stocks. We provide an inference framework for all components of the model, including idiosyncratic volatility and …