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Adaptive Lasso for vector Multiplicative Error Models

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  • Luca Cattivelli
  • Giampiero M. Gallo

Abstract

In this paper we adopt Adaptive Lasso techniques in vector Multiplicative Error Models (vMEM), and we show that they provide asymptotic consistency in variable selection and the same efficiency as if the set of true predictors were known in advance (oracle property). A Monte Carlo exercise demonstrates the good performance of this approach and an empirical application shows its effectiveness in studying the network of volatility spillovers among European financial indices, during and after the sovereign debt crisis. We conclude demonstrating the superior volatility forecast ability of Adaptive Lasso techniques also when a common trend is removed prior to multivariate volatility spillover analysis.

Suggested Citation

  • Luca Cattivelli & Giampiero M. Gallo, 2020. "Adaptive Lasso for vector Multiplicative Error Models," Quantitative Finance, Taylor & Francis Journals, vol. 20(2), pages 255-274, February.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:2:p:255-274
    DOI: 10.1080/14697688.2019.1651451
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    Cited by:

    1. Cipollini, Fabrizio & Gallo, Giampiero M. & Otranto, Edoardo, 2021. "Realized volatility forecasting: Robustness to measurement errors," International Journal of Forecasting, Elsevier, vol. 37(1), pages 44-57.
    2. Amendola, A. & Candila, V. & Cipollini, F. & Gallo, G.M., 2024. "Doubly multiplicative error models with long- and short-run components," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    3. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.

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