IDEAS home Printed from https://ideas.repec.org/p/fir/econom/wp2010_03.html
   My bibliography  Save this paper

A Time-varying Mixing Multiplicative Error Model for Realized Volatility

Author

Listed:

Abstract

In this paper we model the dynamics of realized volatility as a Multiplicative Error Model with a mixture of distributions for the innovation term with time-varying mixing weights forced by past behavior of volatility. The mixture considers innovations as a source of time-varying volatility of volatility and is able to capture the right tail behavior of the distribution of volatility. The empirical results show that there is no substantial difference in the one-step ahead conditional expectations obtained according to various mixing schemes but that fixity of mixing weights may be a binding constraint in deriving accurate quantiles of the predicted distribution.

Suggested Citation

  • Giovanni De Luca & Giampiero Gallo, 2010. "A Time-varying Mixing Multiplicative Error Model for Realized Volatility," Econometrics Working Papers Archive wp2010_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    • Handle: RePEc:fir:econom:wp2010_03
    as

    Download full text from publisher

    File URL: https://labdisia.disia.unifi.it/ricerca/pubblicazioni/working_papers/2010/wp2010_03.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 31-67.
    2. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    3. Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
    4. Giovanni Luca & Giampiero Gallo, 2009. "Time-Varying Mixing Weights in Mixture Autoregressive Conditional Duration Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 102-120.
    5. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    6. Markku Lanne, 2006. "A Mixture Multiplicative Error Model for Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 594-616.
    7. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Yongdeng, 2022. "The Exponential HEAVY Model: An Improved Approach to Volatility Modeling and Forecasting," Cardiff Economics Working Papers E2022/5, Cardiff University, Cardiff Business School, Economics Section.
    2. Driton Kuçi, 2015. "Contemporary Models of Organization of Power and the Macedonian Model of Organization of Power," European Journal of Interdisciplinary Studies Articles, Revistia Research and Publishing, vol. 1, September.
    3. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
    4. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    5. G.M. Gallo & D. Lacava & E. Otranto, 2023. "Volatility jumps and the classification of monetary policy announcements," Working Paper CRENoS 202306, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    6. Giampiero M. Gallo & Edoardo Otranto, 2014. "Forecasting Realized Volatility with Changes of Regimes," Econometrics Working Papers Archive 2014_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    7. Heejoon Han & Myung D. Park & Shen Zhang, 2015. "A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 209-219, April.
    8. Christian T. Brownlees & Giampiero M. Gallo, 2010. "Comparison of Volatility Measures: a Risk Management Perspective," Journal of Financial Econometrics, Oxford University Press, vol. 8(1), pages 29-56, Winter.
    9. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula–Based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics, MDPI, vol. 5(2), pages 1-24, April.
    10. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    11. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    12. Ng, F.C. & Li, W.K. & Yu, Philip L.H., 2016. "Diagnostic checking of the vector multiplicative error model," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 86-97.
    13. Ke, Rui & Lu, Wanbo & Jia, Jing, 2021. "Evaluating multiplicative error models: A residual-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    14. Donelli, Nicola & Peluso, Stefano & Mira, Antonietta, 2021. "A Bayesian semiparametric vector Multiplicative Error Model," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    15. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Papers 1604.01338, arXiv.org.
    16. Bubák, Vít & Kocenda, Evzen & Zikes, Filip, 2011. "Volatility transmission in emerging European foreign exchange markets," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2829-2841, November.
    17. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    18. Serdengeçti, Süleyman & Sensoy, Ahmet & Nguyen, Duc Khuong, 2021. "Dynamics of return and liquidity (co) jumps in emerging foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    19. Ahoniemi, Katja & Lanne, Markku, 2009. "Joint modeling of call and put implied volatility," International Journal of Forecasting, Elsevier, vol. 25(2), pages 239-258.
    20. Luca Scaffidi Domianello & Giampiero M. Gallo & Edoardo Otranto, 2024. "Smooth and Abrupt Dynamics in Financial Volatility: The MS‐MEM‐MIDAS," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 21-43, February.

    More about this item

    Keywords

    Multiplicative Error Models; Realized Volatility; Mixture Distributions;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fir:econom:wp2010_03. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Fabrizio Cipollini (email available below). General contact details of provider: https://edirc.repec.org/data/dsfirit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.