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Copula–Based vMEM Specifications versus Alternatives: The Case of Trading Activity

Author

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  • Fabrizio Cipollini

    (Dipartimento di Statistica, Informatica, Applicazioni “G. Parenti”, Università di Firenze, 50134 Firenze, Italy)

  • Robert F. Engle

    (Department of Finance, Stern School of Business, New York University, New York, 10012 NY, USA)

  • Giampiero M. Gallo

    (Dipartimento di Statistica, Informatica, Applicazioni “G. Parenti”, Università di Firenze, 50134 Firenze, Italy
    NYU Florence, Villa La Pietra, 50139 Firenze, Italy)

Abstract

We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past values and conditional expectations of the variables can be simultaneously estimated. Results with realized volatility, volumes and number of trades of the JNJ stock show that significantly superior realized volatility forecasts are delivered with a fully interdependent vMEM relative to a single equation. Alternatives involving log–Normal or semiparametric formulations produce substantially equivalent results.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jecnmx:v:5:y:2017:i:2:p:16-:d:95642
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    Cited by:

    1. E. Otranto, 2024. "A Vector Multiplicative Error Model with Spillover Effects and Co-movements," Working Paper CRENoS 202404, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    2. Nguyen, Giang & Engle, Robert & Fleming, Michael & Ghysels, Eric, 2020. "Liquidity and volatility in the U.S. Treasury market," Journal of Econometrics, Elsevier, vol. 217(2), pages 207-229.
    3. Carol Alexander & Daniel F. Heck & Andreas Kaeck, 2022. "The Role of Binance in Bitcoin Volatility Transmission," Applied Mathematical Finance, Taylor & Francis Journals, vol. 29(1), pages 1-32, January.
    4. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    5. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
    6. Cipollini, Fabrizio & Gallo, Giampiero M., 2019. "Modeling Euro STOXX 50 volatility with common and market-specific components," Econometrics and Statistics, Elsevier, vol. 11(C), pages 22-42.

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    More about this item

    Keywords

    GARCH; MEM; realized volatility; trading volume; trading activity; trades; copula; volatility forecasting;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

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