Prague Economic Papers 2024, 33(4):478-503 | DOI: 10.18267/j.pep.865

Volatility Spillover Effect from Energy Markets to Foreign Exchange Markets: The Case of Central and Eastern European and Eurasian Countries

Dejan Živkov ORCID..., Boris Kuzman ORCID..., Nataša Papić-Blagojević ORCID...
Dejan Živkov, Nataša Papić-Blagojević: University of Novi Sad, Serbia, Novi Sad, Serbia
Boris Kuzman: Institute of Agricultural Economics, Belgrade, Serbia

This paper investigates the nonlinear risk transmission from the oil and natural gas markets to the foreign exchange markets of five energy importers and one major energy exporter. We separate conditional volatility into the transitory (short-term) and permanent (long-term) parts, and then these volatilities are embedded in an elaborate robust linear quantile regression model. We find that the risk spillover effect is relatively low in Central and Eastern European countries (CEECs) probably because they pursue a managed float exchange rate regime. On the other hand, this effect is higher for Turkey and Russia, which is especially true for the effect from oil to the rouble at the highest quantile. This happens because Russia receives the largest amount of foreign currency from oil exports. The results indicate that the short-term risk spillover effect is notably stronger than the long-term one, which means that the exchange rate volatility is mainly determined by market sentiment. The rolling regression results coincide very well with the estimated quantile parameters.

Keywords: risk spillover, energy markets, exchange rate, CGARCH, quantile regression
JEL classification: C21, C51, F31, O13

Received: February 14, 2024; Revised: May 3, 2024; Accepted: June 2, 2024; Published: August 30, 2024  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Živkov, D., Kuzman, B., & Papić-Blagojević, N. (2024). Volatility Spillover Effect from Energy Markets to Foreign Exchange Markets: The Case of Central and Eastern European and Eurasian Countries. Prague Economic Papers, 33(4), 478-503. doi: 10.18267/j.pep.865
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Azimli, A. (2020). The Impact of COVID-19 on the Degree of Dependence and Structure of Risk-return Relationship: A Quantile Regression Approach. Finance Research Letters, 36, 101648. https://doi.org/10.1016/j.frl.2020.101648 Go to original source...
    2. Barndorff-Nielsen, O. E. (1997). Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling. Scandinavian Journal of Statistics, 24(1), 1-13. https://doi.org/10.1111/1467-9469.00045 Go to original source...
    3. Bartosik, K., Mycielski, J. (2020). The Output Employment Elasticity and the Increased Use of Temporary Contracts: Evidence from Poland. Acta Oeconomica, 70(1), 83-104. https://doi.org/10.1556/032.2020.00005 Go to original source...
    4. Brahmasrene, T., Huang, J.-C., Sissoko, Y. (2014). Crude Oil Prices and Exchange Rates: Causality, Variance Decomposition and Impulse Response. Energy Economics, 44, 407-412. https://doi.org/10.1016/j.eneco.2014.05.011 Go to original source...
    5. Cheng, H.-W., Chang, L.-H., Lo, C.-L., Tsai, J.-T. (2023). Empirical Performance of Component GARCH Models in Pricing VIX Term Structure and VIX futures. Journal of Empirical Finance, 72, 122-142. https://doi.org/10.1016/j.jempfin.2023.03.005 Go to original source...
    6. Durusu-Ciftci, D., Soytas, U., Nazlioglu, S. (2020). Financial Development and Energy Consumption in Emerging Markets: Smooth Structural Shifts and Causal Linkages. Energy Economics, 87, 104729. https://doi.org/10.1016/j.eneco.2020.104729 Go to original source...
    7. Galarza, C., Lachos, V. H., Cabral, C. R. B., Castro, C. L. (2017). Robust Quantile Regression Using a Generalized Class of Skewed Distributions. Stat, 6(1), 113-130. https://doi.org/10.1002/sta4.140 Go to original source...
    8. Gomez-Gonzalez, J. E., Hirs-Garzon, J., Uribe, J. M. (2020). Giving and Receiving: Exploring the Predictive Causality between Oil Prices and Exchange Rates. International Finance, 23(1), 175-194. https://doi.org/10.1111/infi.12354 Go to original source...
    9. Hameed, Z., Shafi, K., Nadeem, A. (2021). Volatility Spillover Effect between Oil Prices and Foreign Exchange Markets. Energy Strategy Reviews, 38, 100712. https://doi.org/10.1016/j.esr.2021.100712 Go to original source...
    10. Hashmi, S. M., Chang, B. H., Huang, L., Uche, E. (2022). Revisiting the Relationship between Oil Prices, Exchange Rate, and Stock Prices: An Application of Quantile ARDL Model. Resources Policy, 75, 102543. https://doi.org/10.1016/j.resourpol.2021.102543 Go to original source...
    11. Huang, B.-N., Lee, C.-C., Chang, Y.-F., Lee, C.-C. (2021). Dynamic Linkage Between Oil Prices and Exchange Rates: New Global Evidence. Empirical Economics, 61(2), 719-742. https://doi.org/10.1007/s00181-020-01874-8 Go to original source...
    12. Koenker, R., Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. https://doi.org/10.2307/1913643 Go to original source...
    13. Liu, B.-Y., Ji, Q., Nguyen, D. K., Fan, Y. (2020). Dynamic Dependence and Extreme Risk Comovement: The Case of Oil Prices and Exchange Rates. International Journal of Finance and Economics, 26(2), 2612-2636. https://doi.org/10.1002/ijfe.1924 Go to original source...
    14. MacKinnon, J. G., White, H. (1985). Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties. Journal of Econometrics, 29(3), 305-325. https://doi.org/10.1016/0304-4076(85)90158-7 Go to original source...
    15. Mo, B., Zeng, H., Meng, J., Ding, S. (2024). The Connectedness between Uncertainty and Exchange Rates of Oil Import Countries: New Evidence from Time and Frequency Perspective. Resources Policy, 88, 104398. https://doi.org/10.1016/j.resourpol.2023.104398 Go to original source...
    16. Monjazeb, M. R., Amiri, H., Movahedi, A. (2024). Wholesale Electricity Price Forecasting by Quantile Regression and Kalman Filter Method. Energy, 290, 129925. Go to original source...
    17. Morales-Zumaquero, A., Sosvilla-Rivero, S. (2018). Volatility Spillovers between Foreign Exchange and Stock Markets in Industrialized Countries. The Quarterly Review of Economics and Finance, 70, 121-136. https://doi.org/10.1016/j.energy.2023.129925 Go to original source...
    18. Pershin, V., Molero, J. C., de Gracia, F. P. (2016). Exploring the Oil Prices and Exchange Rates Nexus in Some African Economies. Journal of Policy Modeling, 38(1), 166-180. https://doi.org/10.1016/j.jpolmod.2015.11.001 Go to original source...
    19. Poghosyan, K., Poghosyan, R. (2021). On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia. Finance a Úvěr - Czech Journal of Economics and Finance, 71(1), 52-79. Go to original source...
    20. Ross, S. A. (1989). Information and Volatility. The No Arbitrage and Martingale Approach to Timing and Resolution Irrelevancy. Journal of Finance, 44(1), 1-17. https://doi.org/10.1111/j.1540-6261.1989.tb02401.x Go to original source...
    21. Saidu, M. T., Naseem, N. A. M., Law, S. H., Yasmin, B. (2021). Exploring the Asymmetric Effect of Oil Price on Exchange Rate: Evidence from the Top Six African Net Oil Importers. Energy Reports, 7, 8238-8257. https://doi.org/10.1016/j.egyr.2021.07.037 Go to original source...
    22. Sahbaz, A., Adiguzel, U., Bayat, T., Kayhan, S. (2014). Relationship between Oil Prices and Exchange Rates: The Case of Romania. Economic Computation and Economic Cybernetics Studies and Research, 48(2), 245-256.
    23. Sekmen, T., Topuz, S. G. (2021). Asymmetric Oil Price and Exchange Rate Pass-through in the Turkish Oil-gasoline Markets. Romanian Journal of Economic Forecasting, 24(2), 74-93.
    24. Sun, C., Peng, Y., Zhan, Y. (2023). How Does China's Crude Oil Futures Affect the Crude Oil Prices at Home and Abroad? Evidence from the Cross-market Exchange Rate Spillovers. International Review of Economics and Finance, 88, 204-222. https://doi.org/10.1016/j.iref.2023.06.013 Go to original source...
    25. Verma, R. K., Bansal, R. (2021). Impact of Macroeconomic Variables on the Performance of Stock Exchange: A Systematic Review. International Journal of Emerging Markets, 16(7), 1291-1329. https://doi.org/10.1108/ijoem-11-2019-0993 Go to original source...
    26. Viola, A. P., Klotzle, M. C., Figueiredo Pinto, A. C., da Silveira Barbedo, C. H. (2019). Foreign Exchange Interventions in Brazil and Their Impact on Volatility: A Quantile Regression Approach. Research in International Business and Finance, 47, 251-263. https://doi.org/10.1016/j.ribaf.2018.08.002 Go to original source...
    27. Wen, D., Liu, L., Ma, C., Wang. Y. (2020). Extreme Risk Spillovers between Crude Oil Prices and the U.S. Exchange Rate: Evidence from Oil-exporting and Oil-importing countries. Energy, 212, 118740. https://doi.org/10.1016/j.energy.2020.118740 Go to original source...
    28. Wong, H. T. (2019). Volatility Spillovers between Real Exchange Rate Returns and Real Stock Price Returns in Malaysia. International Journal of Finance and Economics, 24(1), 131-149. https://doi.org/10.1002/ijfe.1653 Go to original source...
    29. Zhang, Y.-J., Fan, Y., Tsai, H. T., Wei, Y. M. (2008). Spillover Effect of US Dollar Exchange Rate on Oil Prices. Journal of Policy Modeling, 30(6), 973-991. https://doi.org/10.1016/j.jpolmod.2008.02.002 Go to original source...
    30. Zhu, H., Yu, D., Hau, L., Wu, H., Ye, F. (2022). Time-frequency Effect of Crude Oil and Exchange Rates on Stock Markets in BRICS Countries: Evidence from Wavelet Quantile Regression Analysis. The North American Journal of Economics and Finance, 61, 101708. https://doi.org/10.1016/j.najef.2022.101708 Go to original source...
    31. Živkov, D., Manić, S., Đurašković, J. (2020). Short and Long-term Volatility Transmission from Oil to Agricultural Commodities - The Robust Quantile Regression Approach. Borsa Istanbul Review, 20(S1), 11-25. https://doi.org/10.1016/j.bir.2020.10.008 Go to original source...
    32. Zolfaghari, M., Ghoddusi, H., Faghihian, F. (2020). Volatility Spillovers for Energy Prices: A Diagonal BEKK Approach. Energy Economics, 92, 104965. https://doi.org/10.1016/j.eneco.2020.104965 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY NC ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content