Modeling and forecasting persistent financial durations

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: FinMaP-Working Paper ; No. 36

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Estimation: General ; Financial Econometrics ; Financial Forecasting and Simulation
• Thema: price durations ; long memory ; multifractal models ; realized volatility ; Whittle estimation

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Zikes, Filip ; Barunik, Jozef ; Shenai, Nikhil
• Ereignis: Veröffentlichung
• (wer): Kiel University, FinMaP - Financial Distortions and Macroeconomic Performance
• (wo): Kiel
• (wann): 2015

Weitere Informationen

• Handle: http://hdl.handle.net/10419/108900
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ