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A Markov-Switching Multi-Fractal Inter-Trade Duration Model, with Application to U.S. Equities

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  • Fei Chen

    () (Huazhong University of Science and Technology)

  • Francis X. Diebold

    () (Department of Economics, University of Pennsylvania)

  • Frank Schorfheide

    () (Department of Economics, University of Pennsylvania)

Abstract

We propose and illustrate a Markov-switching multi-fractal duration (MSMD) model for analysis of inter-trade durations in financial markets. We establish several of its key properties with emphasis on high persistence (indeed long memory). Empirical exploration suggests MSMD's superiority relative to leading competitors.

Suggested Citation

  • Fei Chen & Francis X. Diebold & Frank Schorfheide, 2012. "A Markov-Switching Multi-Fractal Inter-Trade Duration Model, with Application to U.S. Equities," PIER Working Paper Archive 12-020, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:12-020
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    Cited by:

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    2. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW).
    3. Jorge Pérez-Rodríguez & Emilio Gómez-Déniza & Simón Sosvilla-Rivero, 2019. "“Testing for private information using trade duration models with unobserved market heterogeneity: The case of Banco Popular”," IREA Working Papers 201907, University of Barcelona, Research Institute of Applied Economics, revised Apr 2019.
    4. Xin-Lan Fu & Xing-Lu Gao & Zheng Shan & Zhi-Qiang Jiang & Wei-Xing Zhou, 2018. "Multifractal characteristics and return predictability in the Chinese stock markets," Papers 1806.07604, arXiv.org.
    5. Filip Žikeš & Jozef Baruník & Nikhil Shenai, 2017. "Modeling and forecasting persistent financial durations," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1081-1110, November.
    6. Eric M. Aldrich & Indra Heckenbach & Gregory Laughlin, 2014. "The Random Walk of High Frequency Trading," Papers 1408.3650, arXiv.org, revised Aug 2014.
    7. Yogo Purwono & Irwan Adi Ekaputra & Zaäfri Ananto Husodo, 2018. "Estimation of Dynamic Mixed Hitting Time Model Using Characteristic Function Based Moments," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 295-321, February.
    8. Aldrich, Eric M. & Heckenbach, Indra & Laughlin, Gregory, 2016. "A compound duration model for high-frequency asset returns," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 105-128.
    9. Herrera, Rodrigo & Rodriguez, Alejandro & Pino, Gabriel, 2017. "Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model," Energy Economics, Elsevier, vol. 63(C), pages 129-143.
    10. Kuosmanen, Petri & Nabulsi, Nasib & Vataja, Juuso, 2015. "Financial variables and economic activity in the Nordic countries," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 368-379.
    11. Zhicheng Li & Haipeng Xing & Xinyun Chen, 2019. "A multifactor regime-switching model for inter-trade durations in the limit order market," Papers 1912.00764, arXiv.org.
    12. Aknouche, Abdelhakim & Francq, Christian, 2020. "Stationarity and ergodicity of Markov switching positive conditional mean models," MPRA Paper 102503, University Library of Munich, Germany.
    13. Augustyniak, Maciej & Dufays, Arnaud, 2018. "Modeling macroeconomic series with regime-switching models characterized by a high-dimensional state space," Economics Letters, Elsevier, vol. 170(C), pages 122-126.
    14. de Bruijn, L.P. & Franses, Ph.H.B.F., 2015. "Stochastic levels and duration dependence in US unemployment," Econometric Institute Research Papers EI2015-20, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    15. Veronika Czellar & David T. Frazier & Eric Renault, 2020. "Approximate Maximum Likelihood for Complex Structural Models," Papers 2006.10245, arXiv.org.
    16. Lux, Thomas, 2013. "Exact solutions for the transient densities of continuous-time Markov switching models: With an application to the poisson multifractal model," Kiel Working Papers 1871, Kiel Institute for the World Economy (IfW).
    17. Suh, Jong Hwan, 2015. "Forecasting the daily outbreak of topic-level political risk from social media using hidden Markov model-based techniques," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 115-132.
    18. Farzad Alavi Fard, 2014. "Optimal Bid-Ask Spread in Limit-Order Books under Regime Switching Framework," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 33-48, November.
    19. Czellar, Veronika & Frazier, David T. & Renault, Eric, 2021. "Approximate Maximum Likelihood for Complex Structural Models," The Warwick Economics Research Paper Series (TWERPS) 1337, University of Warwick, Department of Economics.
    20. Renault, Eric & van der Heijden, Thijs & Werker, Bas J.M., 2014. "The dynamic mixed hitting-time model for multiple transaction prices and times," Journal of Econometrics, Elsevier, vol. 180(2), pages 233-250.
    21. Kang, Bo Soo & Ryu, Doojin & Ryu, Doowon, 2014. "Phase-shifting behaviour revisited: An alternative measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 167-173.

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    More about this item

    Keywords

    High-frequency trading data; point process; long memory; time deformation; scaling law; self-similarity; regime-switching model; market microstructure; liquidity;
    All these keywords.

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets

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