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Forecasting volatility under fractality, regime-switching, long memory and student-t innovations

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  • Lux, Thomas
  • Morales-Arias, Leonardo

Abstract

The Markov-switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood (ML) and Generalized Method of Moments (GMM) and volatility forecasting can be performed via Bayesian updating (ML) or best linear forecasts (GMM). Monte Carlo simulations show that using GMM plus linear forecasts leads to minor losses in efficiency compared to optimal Bayesian forecasts based on ML estimates. The forecasting capability of the MSM-t model is evaluated empirically in a comprehensive panel forecasting analysis with three different cross-sections of assets at the country level (all-share equity indices, bond indices and real estate security indices). Empirical forecasts of the MSM-t model are compared to those obtained from its Gaussian counterparts and other volatility models of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family. In terms of mean absolute errors (mean squared errors), the MSM-t (Gaussian MSM) dominates all other models at most forecasting horizons for the various asset classes considered. Furthermore, forecast combinations obtained from the MSM and (Fractionally Integrated) GARCH models provide an improvement upon forecasts from single models.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Pages: 2676-2692

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Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2676-2692

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Web page: http://www.elsevier.com/locate/csda

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Keywords: Multiplicative volatility models Long memory Student-t innovations International volatility forecasting;

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Cited by:
  1. Diniz, Ana & Barreiros, João & Crato, Nuno, 2012. "A new model for explaining long-range correlations in human time interval production," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1908-1919.
  2. Janczura, Joanna & Weron, Rafal, 2010. "Goodness-of-fit testing for regime-switching models," MPRA Paper 22871, University Library of Munich, Germany.
  3. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
  4. Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013. "Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model," Working Papers 201357, University of Pretoria, Department of Economics.
  5. Ruipeng Liu & Thomas Lux, 2010. "Flexible and Robust Modelling of Volatility Comovements: A Comparison of Two Multifractal Models," Kiel Working Papers 1594, Kiel Institute for the World Economy.
  6. Kwan, Wilson & Li, Wai Keung & Li, Guodong, 2012. "On the estimation and diagnostic checking of the ARFIMA–HYGARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3632-3644.
  7. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer, vol. 97(3), pages 239-270, July.

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