A Comparison of Realized Measures of Integrated Volatility: Price Duration- vs. Return-Based Approaches

Abstract

We study the accuracy of a variety of parametric price duration-based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return-based realized variance estimators. Our financial duration models consist of an autoregressive conditional duration (ACD), its logarithmic version (Log–ACD), and the fractionally integrated ACD (FIACD), as well as the Markov switching multifractal duration (MSMD) model and the factorial hidden Markov duration (FHMD) process. In an empirical study using high-frequency data on 10 stocks traded on the New York stock exchange (NYSE), our in-sample and out-of-sample results show that the parametric price duration-based realized variance (RV) estimators, especially the ACD-based RV estimator, seem to be robust to price jumps and microstructure noise and perform better than the non-parametric return-based RV estimators. Furthermore, we also find that the price duration- and return-based RV models perform relatively well and produce more accurate and valid value-at-risk forecasts than the GARCH(1,1).