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Multiscale non-linear tale risk spillover effect from oil to stocks – The case of East European emerging markets

Author

Listed:
  • Dejan Zivkov

    (University of Novi Sad)

  • Boris Kuzman

    (Institute of Agricultural Economics)

  • Natasa Papic-Blagojevic

    (University of Novi Sad)

Abstract

This paper investigates the multiscale non-linear risk transmission effect from Brent oil to eleven European emerging stock markets. Dynamic extreme risk time series are created using the FIAPARCH-CVaR approach. The MODWT transformation is applied to make three wavelet details that represent different time horizons. In the final step, the MODWT time series are fitted into the Markov switching model to examine the spillover phenomenon. The results indicate that the Czech and Hungarian stock markets endure the spillover effect in crisis regime in the short term, probably because these markets are among the most efficient emerging European markets. On the other hand, a relatively high spillover effect is found in a peaceful rather than a crisis regime in the case of Poland. This is probably because the Polish index lists almost 300 stocks, which means that oil shocks disperse to a large number of different industry sectors. In small and less developed markets, such as Estonia, Slovenia, Bulgaria, and Croatia, a high spillover effect exists in a tranquil regime because these countries have high oil consumption per capita. Lithuania and Latvia do not report the spillover effect in the short run, while this is true for all time horizons in the case of Slovakia.

Suggested Citation

  • Dejan Zivkov & Boris Kuzman & Natasa Papic-Blagojevic, 2024. "Multiscale non-linear tale risk spillover effect from oil to stocks – The case of East European emerging markets," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, vol. 27(3), pages 186-200, September.
  • Handle: RePEc:bbl:journl:v:27:y:2024:i:3:p:186-200
    DOI: 10.15240/tul/001/2024-5-015
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    Keywords

    Extreme risk spillover effect; conditional value-at-risk (CVaR); wavelet methodology; Brent oil; stock markets;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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