Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/108900
Authors: 
Zikes, Filip
Barunik, Jozef
Shenai, Nikhil
Year of Publication: 
2015
Series/Report no.: 
FinMaP-Working Paper 36
Abstract: 
This paper 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 ß-mixing as we show in the paper, 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.
Subjects: 
price durations
long memory
multifractal models
realized volatility
Whittle estimation
JEL: 
C13
C58
G17
Document Type: 
Working Paper
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