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Multiplicative Error Models: 20 years on

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  • Cipollini, Fabrizio
  • Gallo, Giampiero M.

Abstract

The issue of combining low– and high–frequency components of volatility is addressed within the class of Multiplicative Error Models both in the univariate and multivariate cases. Inference based on the Generalized Method of Moments is suggested, which has the advantage of not requiring a parametric choice for the error distribution. The application relates to several volatility market indices (US, Europe and East Asia, with interdependencies in the short–run components of absolute returns, realized kernel volatility and option–based implied volatility indices): a set of diagnostic tools is used to evaluate the evidence of a relevant low–frequency component across markets, also from a forecasting comparison perspective. The results show that the slow–moving component in the dynamics achieves a better fit to the data and allows for an interpretation of what moves the local average level of volatility.

Suggested Citation

  • Cipollini, Fabrizio & Gallo, Giampiero M., 2025. "Multiplicative Error Models: 20 years on," Econometrics and Statistics, Elsevier, vol. 33(C), pages 209-229.
    • Handle: RePEc:eee:ecosta:v:33:y:2025:i:c:p:209-229
    DOI: 10.1016/j.ecosta.2022.05.005
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    1. 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.
    2. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2021. "Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series," MPRA Paper 110954, University Library of Munich, Germany, revised 06 Dec 2021.
    3. Abdelhakim Aknouche & Stefanos Dimitrakopoulos, 2023. "Autoregressive conditional proportion: A multiplicative‐error model for (0,1)‐valued time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(4), pages 393-417, July.

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