| project |
| ARLID |
cav_un_auth*0292677 |
| project_id |
GA13-32263S |
| agency |
GA ČR |
|
| project |
| ARLID |
cav_un_auth*0308905 |
| project_id |
612955 |
| agency |
EC |
|
| abstract
(eng) |
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 beta-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 Whit- tle approximation and establish its strong consistency and asymptotic normality for general MSMD specifications. We show that the Whittle estimation is a computa- tionally 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. |
| result_subspec |
WOS |
| RIV |
AH |
| FORD0 |
50000 |
| FORD1 |
50200 |
| FORD2 |
50202 |
| reportyear |
2018 |
| num_of_auth |
3 |
| mrcbC52 |
4 A hod 4ah 4a 20231122140548.2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0238358 |
| mrcbC64 |
1 Department of Econometrics UTIA-B 50202 ECONOMICS |
| confidential |
S |
| mrcbC86 |
3+4 Article Economics|Mathematics Interdisciplinary Applications|Social Sciences Mathematical Methods|Statistics Probability |
| mrcbC86 |
2 Article Economics|Mathematics Interdisciplinary Applications|Social Sciences Mathematical Methods|Statistics Probability |
| mrcbC86 |
2 Article Economics|Mathematics Interdisciplinary Applications|Social Sciences Mathematical Methods|Statistics Probability |
| mrcbT16-e |
ECONOMICS|MATHEMATICSINTERDISCIPLINARYAPPLICATIONS|SOCIALSCIENCESMATHEMATICALMETHODS|STATISTICSPROBABILITY |
| mrcbT16-j |
1.599 |
| mrcbT16-s |
1.797 |
| mrcbT16-B |
80.01 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1 |
| arlyear |
2017 |
| mrcbTft |
\nSoubory v repozitáři: barunik-0434201.pdf, barunik-0434201.pdf |
| mrcbU14 |
84947254896 SCOPUS |
| mrcbU34 |
000408772300001 WOS |
| mrcbU63 |
cav_un_epca*0293034 Econometric Reviews 0747-4938 1532-4168 Roč. 36 č. 10 2017 1081 1110 Taylor & Francis |