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Forecasting realized volatility with changing average levels

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  • Gallo, Giampiero M.
  • Otranto, Edoardo

Abstract

We explore the abilities of regime switching with Markovian dynamics (MS) and of a smooth transition (ST) nonlinearity within the class of Multiplicative Error Models (MEMs) to capture the slow-moving long-run average in the realized volatility. We compare these models to some alternatives, including considering (quasi) long memory features (HAR class), the benefits of log transformations, and the presence of jumps. The analysis is applied to the realized kernel volatility series of the S&P500 index, adopting residual diagnostics as a guidance for model selection. The forecast performance is evaluated and tested via squared and absolute losses both in- and out-of-sample, as well as based on a robustness check on different sample choices. The results show very satisfactory performances of both MS and ST models, with the former also allowing for the dating and interpretation of regimes in terms of market events.

Suggested Citation

  • Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:3:p:620-634
    DOI: 10.1016/j.ijforecast.2014.09.005
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    3. Caporin, Massimiliano & Rossi, Eduardo & Santucci de Magistris, Paolo, 2017. "Chasing volatility," Journal of Econometrics, Elsevier, vol. 198(1), pages 122-145.
    4. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Papers 1604.01338, arXiv.org.
    5. Thomas Chuffart, 2015. "Selection Criteria in Regime Switching Conditional Volatility Models," Econometrics, MDPI, Open Access Journal, vol. 3(2), pages 1-28, May.
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    7. Giampiero M. Gallo & Edoardo Otranto, 2016. "Combining Markov Switching and Smooth Transition in Modeling Volatility: A Fuzzy Regime MEM," Econometrics Working Papers Archive 2016_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    8. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula–Based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics, MDPI, Open Access Journal, vol. 5(2), pages 1-24, April.
    9. E. Otranto, 2015. "Adding Flexibility to Markov Switching Models," Working Paper CRENoS 201509, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    10. Bartoš, Erik & Pinčák, Richard, 2017. "Identification of market trends with string and D2-brane maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 57-70.
    11. AUGUSTYNIAK, Maciej & BAUWENS, Luc & DUFAYS, Arnaud, 2016. "A New Approach to Volatility Modeling : The High-Dimensional Markov Model," CORE Discussion Papers 2016042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    13. Giampiero M. Gallo & Edoardo Otranto, 2018. "Combining sharp and smooth transitions in volatility dynamics: a fuzzy regime approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 549-573, April.
    14. L. Bauwens & E. Otrando, 2018. "Nonlinearities and Regimes in Conditional Correlations with Different Dynamics," Working Paper CRENoS 201803, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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