We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...

The intraday high-frequency datasets contain several zero returns, which state that no change occurs in two or more consecutive transactions, particularly in the transaction data of in- active securities. In addition, existing approaches to cleaning financial data, such as the previous-tick...

The price staleness is referred to as the extent of the presence of the zero returns in the price dynamics. The proportion of the zero returns could be high in the high-frequency data sets, as pointed by Bandi et al. (2020a). Considering the price staleness as a dynamic system too, in this...

This study provides an in-depth analysis of how to estimate risk-neutral moments robustly. A simulation and an empirical study show that estimating risk- neutral moments presents a trade-off between (1) the bias of estimates caused by a limited strike price domain and (2) the variance of...