10.6084/m9.figshare.1232119.v3
Filip Žikeš
Filip
Žikeš
Jozef Baruník
Jozef
Baruník
Nikhil Shenai
Nikhil
Shenai
Modeling and forecasting persistent financial durations
Taylor & Francis Group
2015
Long memory
Multifractal models
Price durations
Realized volatility
Whittle estimation
C13
C58
G17
2015-01-01 00:00:00
Dataset
https://tandf.figshare.com/articles/dataset/Modeling_and_Forecasting_Persistent_Financial_Durations/1232119
<p>This article introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility model of Calvet and Fisher (2004) to the duration setting. Although the MSMD process is exponential β-mixing as we show in the article, it is capable of generating highly persistent autocorrelation. We study, analytically and by simulation, how this feature of durations generated by the MSMD process propagates to counts and realized volatility. We employ a quasi-maximum likelihood estimator of the MSMD parameters based on the Whittle approximation and establish its strong consistency and asymptotic normality for general MSMD specifications. We show that the Whittle estimation is a computationally simple and fast alternative to maximum likelihood. Finally, we compare the performance of the MSMD model with competing short- and long-memory duration models in an out-of-sample forecasting exercise based on price durations of three major foreign exchange futures contracts. The results of the comparison show that the MSMD and the Long Memory Stochastic Duration model perform similarly and are superior to the short-memory Autoregressive Conditional Duration models.</p>