This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and...

This paper examines long memory volatility in the cross-section of stock returns. We show that long memory volatility is widespread in the U.S. and that the degree of memory can be related to firm characteristics such as market capitalization, book-to-market ratio, prior performance and price...

This paper revisits the topic of time-scale parameterizations of the Heston-Nandi GARCH (1,1) model to create a new, theoretically valid setting compatible with real financial data. We first estimate parameters using three US market indices and six frequencies to let data reveal the correct,...

We introduce the class of FIR-GARCH models in this paper. FIR-GARCH models provide a parsimonious joint model for low-frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of...

We develop two new estimators for a general class of stationary GARCH models with possibly heavy tailed asymmetrically distributed errors, covering processes with symmetric and asymmetric feedback like GARCH, Asymmetric GARCH, VGARCH and Quadratic GARCH. The first estimator arises from...