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Capturing the Spillover Effect With Multiplicative Error Models

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  • Edoardo Otranto

Abstract

Recent statistical models for the analysis of volatility in financial markets serve the purpose of incorporating the effect of other markets in their structure, in order to study the spillover or the contagion phenomena. Extending the Multiplicative Error Model we are able to capture these characteristics, under the assumption that the conditional mean of the volatility can be decomposed into the sum of one component representing the proper volatility of the time series analyzed, and other components, each representing the volatility transmitted from one other market. Each component follows a proper dynamics with elements that can be usefully interpreted. This particular decomposition allows to establish, each time, the contribution brought by each individual market to the global volatility of the market object of the analysis. We experiment this model with four stock indices.

Suggested Citation

  • Edoardo Otranto, 2015. "Capturing the Spillover Effect With Multiplicative Error Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(15), pages 3173-3191, August.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:15:p:3173-3191
    DOI: 10.1080/03610926.2013.819919
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    Citations

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    Cited by:

    1. BenSaïda, Ahmed & Litimi, Houda & Abdallah, Oussama, 2018. "Volatility spillover shifts in global financial markets," Economic Modelling, Elsevier, vol. 73(C), pages 343-353.
    2. Khalifa, Ahmed A. & Alsarhan, Abdulwahab A. & Bertuccelli, Pietro, 2017. "Causes and consequences of energy price shocks on petroleum-based stock market using the spillover asymmetric multiplicative error model," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 307-314.
    3. 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.
    4. Darko B. Vuković & Senanu Dekpo-Adza & Vladislav Khmelnitskiy & Mustafa Özer, 2023. "Spillovers across the Asian OPEC+ Financial Market," Mathematics, MDPI, vol. 11(18), pages 1-23, September.
    5. Demetrio Lacava & Giampiero M. Gallo & Edoardo Otranto, 2022. "Unconventional policies effects on stock market volatility: The MAP approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1245-1265, November.
    6. Demetrio Lacava & Luca Scaffidi Domianello, 2021. "The Incidence of Spillover Effects during the Unconventional Monetary Policies Era," JRFM, MDPI, vol. 14(6), pages 1-18, May.
    7. Charfeddine, Lanouar & Benlagha, Noureddine & Khediri, Karim Ben, 2022. "An intra-cryptocurrency analysis of volatility connectedness and its determinants: Evidence from mining coins, non-mining coins and tokens," Research in International Business and Finance, Elsevier, vol. 62(C).
    8. Corbet, Shaen & Goodell, John W. & Günay, Samet, 2020. "Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19," Energy Economics, Elsevier, vol. 92(C).
    9. Costa, Antonio & Matos, Paulo & da Silva, Cristiano, 2022. "Sectoral connectedness: New evidence from US stock market during COVID-19 pandemics," Finance Research Letters, Elsevier, vol. 45(C).

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