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True and Apparent Scaling: The Proximity of the Markov-Switching Multifractal Model to Long-Range Dependence

Citations

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Cited by:

  1. Nasr, Adnen Ben & Lux, Thomas & Ajmi, Ahdi Noomen & Gupta, Rangan, 2016. "Forecasting the volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. regime switching," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 559-571.
  2. Buonocore, R.J. & Aste, T. & Di Matteo, T., 2016. "Measuring multiscaling in financial time-series," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 38-47.
  3. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021. "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 62(2), pages 283-317, February.
  4. Lahmiri, Salim & Bekiros, Stelios, 2019. "Decomposing the persistence structure of Islamic and green crypto-currencies with nonlinear stepwise filtering," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 334-341.
  5. Morales, Raffaello & Di Matteo, T. & Aste, Tomaso, 2013. "Non-stationary multifractality in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6470-6483.
  6. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
  7. Pagnottoni, Paolo & Spelta, Alessandro & Pecora, Nicolò & Flori, Andrea & Pammolli, Fabio, 2021. "Financial earthquakes: SARS-CoV-2 news shock propagation in stock and sovereign bond markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
  8. Barunik, Jozef & Aste, Tomaso & Di Matteo, T. & Liu, Ruipeng, 2012. "Understanding the source of multifractality in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4234-4251.
  9. Yufang Liu & Weiguo Zhang & Junhui Fu & Xiang Wu, 2020. "Multifractal Analysis of Realized Volatilities in Chinese Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 319-336, August.
  10. Mawuli Segnon & Thomas Lux & Rangan Gupta, 2015. "Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-Type Volatility Models," Working Papers 201550, University of Pretoria, Department of Economics.
  11. Raffaello Morales & T. Di Matteo & Ruggero Gramatica & Tomaso Aste, 2011. "Dynamical Hurst exponent as a tool to monitor unstable periods in financial time series," Papers 1109.0465, arXiv.org.
  12. Lee, Hojin & Chang, Woojin, 2015. "Multifractal regime detecting method for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 70(C), pages 117-129.
  13. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
  14. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2676-2692, November.
  15. Liu, Yufang & Zhang, Weiguo & Fu, Junhui, 2016. "Binomial Markov-Switching Multifractal model with Skewed t innovations and applications to Chinese SSEC Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 56-66.
  16. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy (IfW Kiel).
  17. Riccardo Junior Buonocore & Tomaso Aste & Tiziana Di Matteo, 2015. "Measuring multiscaling in financial time-series," Papers 1509.05471, arXiv.org, revised Sep 2015.
  18. Malo, Pekka, 2009. "Modeling electricity spot and futures price dependence: A multifrequency approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(22), pages 4763-4779.
  19. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2015. "Modeling and forecasting crude oil price volatility: Evidence from historical and recent data," FinMaP-Working Papers 31, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
  20. 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.
  21. Lux, Thomas & Morales-Arias, Leonardo, 2009. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Kiel Working Papers 1532, Kiel Institute for the World Economy (IfW Kiel).
  22. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
  23. Ioannis P. Antoniades & Giuseppe Brandi & L. G. Magafas & T. Di Matteo, 2020. "The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool," Papers 2010.08890, arXiv.org, revised Dec 2020.
  24. Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
  25. Mawuli Segnon & Stelios Bekiros & Bernd Wilfling, 2018. "Forecasting Inflation Uncertainty in the G7 Countries," Econometrics, MDPI, vol. 6(2), pages 1-25, April.
  26. Erdős, Péter & Li, Youwei & Liu, Ruipeng & Mende, Alexander, 2021. "Same same but different – Stylized facts of CTA sub strategies," International Review of Financial Analysis, Elsevier, vol. 74(C).
  27. Pagnottoni, Paolo & Spelta, Alessandro & Flori, Andrea & Pammolli, Fabio, 2022. "Climate change and financial stability: Natural disaster impacts on global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
  28. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
  29. Mulligan, Robert F., 2014. "Multifractality of sectoral price indices: Hurst signature analysis of Cantillon effects in disequilibrium factor markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 252-264.
  30. 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 Kiel).
  31. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy (IfW Kiel).
  32. Mulligan, Robert F., 2017. "The multifractal character of capacity utilization over the business cycle: An application of Hurst signature analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 147-152.
  33. Pakrashi, Vikram & Kelly, Joe & Harkin, Julie & Farrell, Aidan, 2013. "Hurst exponent footprints from activities on a large structural system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1803-1817.
  34. Wang, Yi & Sun, Qi & Zhang, Zilu & Chen, Liqing, 2022. "A risk measure of the stock market that is based on multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
  35. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2011. "A copula–multifractal volatility hedging model for CSI 300 index futures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4260-4272.
  36. Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.
  37. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
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