Politická ekonomie 2024, 72(3):565-596 | DOI: 10.18267/j.polek.1416

Price Spillovers from Decentralized Finance to CEE Stock Markets

Ngo Thai Hung ORCID...
University of Finance-Marketing, Ho Chi Minh City, Vietnam

Decentralized finance (DeFi) is a brand-new disruptive procedure that encourages the use of blockchain technology for developing and distributing a variety of financial goods and services. This study investigates the time-varying and asymmetric interplay between DeFi and CEE stock returns, concentrated around the COVID-19 outbreak and the Russo-Ukrainian conflict. While the associations between other cryptocurrencies and conventional assets have been studied, DeFi assets have not. For this purpose, we employ the multivariate DECO-GARCH model and cross-quantilogram framework. The results reveal a positive equicorrelation between DeFi and CEE stock market returns. Notably, the influence of DeFi on CEE stock markets is greater during the COVID-19 outbreak and the Russo-Ukrainian conflict than in the other periods. Furthermore, the cross-quantilogram estimations uncover that CEE stock markets depend less on the DeFi market at longer lag lengths. This means that the diversification benefits of DeFi against CEE stock market returns are more important for long-run investment horizons. In general, our research offers a new understanding of dependence structures, which might help investors make better investment decisions and direct their trading strategies.

Keywords: DeFi, stock markets, DECO-GARCH, cross-quantilogram, CEE regions
JEL classification: C58, G11, G15

Received: May 30, 2023; Revised: September 17, 2023; Accepted: October 9, 2023; Published: June 24, 2024  Show citation

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Hung, N.T. (2024). Price Spillovers from Decentralized Finance to CEE Stock Markets. Politická ekonomie72(3), 565-596. doi: 10.18267/j.polek.1416
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References

  1. Ahmed, S., Assaf, R., Rahman, M. R., et al. (2023). Is geopolitical risk interconnected? Evidence from Russian-Ukraine crisis. The Journal of Economic Asymmetries, 28(4), e00306. https://doi.org/10.1016/j.jeca.2023.e00306 Go to original source...
  2. Alshater, M. M., Alqaralleh, H., El Khoury, R. (2023). Dynamic asymmetric connectedness in technological sectors. The Journal of Economic Asymmetries, 27(7), e00287. https://doi.org/10.1016/j.jeca.2022.e00287 Go to original source...
  3. Ali, S., Ijaz, M. S., Yousaf, I. (2023). Dynamic spillovers and portfolio risk management between defi and metals: Empirical evidence from the Covid-19. Resources Policy, 83, 103672. https://doi.org/10.1016/j.resourpol.2023.103672 Go to original source...
  4. Bennett, D., Mekelburg, E., Williams, T. H. (2023). BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing. Research in International Business and Finance, 65, 101939. https://doi.org/10.1016/j.ribaf.2023.101939 Go to original source...
  5. Bhimani, A., Hausken, K., Arif, S. (2022). Do national development factors affect cryptocurrency adoption?. Technological Forecasting and Social Change, 181, 121739. https://doi.org/10.1016/j.techfore.2022.121739 Go to original source...
  6. Beck, K., Stanek, P. (2019). Globalization or regionalization of stock markets? The Case of central and eastern European countries. Eastern European Economics, 57(4), 317-330. https://doi.org/10.1080/00128775.2019.1610895 Go to original source...
  7. Bejaoui, A., Frikha, W., Jeribi, A., et al. (2023). Connectedness between emerging stock markets, gold, cryptocurrencies, DeFi and NFT: Some new evidence from wavelet analysis. Physica A: Statistical Mechanics and its Applications, 619, 128720. https://doi.org/10.1016/j.physa.2023.128720 Go to original source...
  8. Chu, J., Chan, S., Zhang, Y. (2023). An analysis of the return-volume relationship in decentralised finance (DeFi). International Review of Economics & Finance, 85, 236-254. https://doi.org/10.1016/j.iref.2023.01.006 Go to original source...
  9. Cevik, E. I., Gunay, S., Zafar, M. W., et al. (2022). The impact of digital finance on the natural resource market: Evidence from DeFi, oil, and gold. Resources Policy, 79(C), 103081. https://doi.org/10.1016/j.resourpol.2022.103081 Go to original source...
  10. Corbet, S., Goodell, J. W., Günay, S. (2022). What drives DeFi prices? Investigating the effects of investor attention. Finance Research Letters, 48, 102883. https://doi.org/10.1016/j.frl.2022.102883 Go to original source...
  11. Chu, J., Chan, S., Zhang, Y. (2023). An analysis of the return-volume relationship in decentralised finance (DeFi). International Review of Economics & Finance, 85, 236-254. https://doi.org/10.1016/j.iref.2023.01.006 Go to original source...
  12. Chowdhury, M. A. F., Abdullah, M., Alam, M., et al. (2023). NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis. International Review of Financial Analysis, 87, 102642. https://doi.org/10.1016/j.irfa.2023.102642 Go to original source...
  13. Corbet, S., Goodell, J. W., Gunay, S., et al. (2023). Are DeFi tokens a separate asset class from conventional cryptocurrencies? Annals of Operations Research, 322(2), 609-630. https://doi.org/10.1007/s10479-022-05150-z Go to original source...
  14. Corbet, S., Lucey, B., Urquhart, A., et al. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199. https://doi.org/10.1016/j.irfa.2018.09.003 Go to original source...
  15. Dai, Z., Zhu, J., Zhang, X. (2022). Time-frequency connectedness and cross-quantile dependence between crude oil, Chinese commodity market, stock market and investor sentiment. Energy Economics, 114, 106226. https://doi.org/10.1016/j.eneco.2022.106226 Go to original source...
  16. Demiralay, S., Golitsis, P. (2021). On the dynamic equicorrelations in cryptocurrency market. The Quarterly Review of Economics and Finance, 80, 524-533. https://doi.org/10.1016/j.qref.2021.04.002 Go to original source...
  17. Engle, R., Kelly, B. (2012). Dynamic equicorrelation. Journal of Business & Economic Statistics, 30(2), 212-228. Go to original source...
  18. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350. Go to original source...
  19. Fabozzi, F. J., Gupta, F., Markowitz, H. M. (2002). The legacy of modern portfolio theory. The journal of investing, 11(3), 7-22. https://doi.org/10.3905/joi.2002.319510 Go to original source...
  20. Gambarelli, L., Marchi, G., Muzzioli, S. (2023). Hedging effectiveness of cryptocurrencies in the European stock market. Journal of International Financial Markets, Institutions and Money, 84, 101757. https://doi.org/10.1016/j.intfin.2023.101757 Go to original source...
  21. Ghosh, I., Alfaro-Cortés, E., Gámez, M., et al. (2023b). Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI. International Review of Financial Analysis, 87, 102558. https://doi.org/10.1016/j.irfa.2023.102558 Go to original source...
  22. Ghosh, B., Kazouz, H., Umar, Z. (2023a). Do Automated Market Makers in DeFi Ecosystem Exhibit Time-Varying Connectedness during Stressed Events? Journal of Risk and Financial Management, 16(5), 259. https://doi.org/10.3390/jrfm16050259 Go to original source...
  23. Hung, N. T. (2020a). Time-Frequency nexus between bitcoin and developed stock markets in the Asia-Pacific. The Singapore Economic Review, 69(1), 399-424. https://doi.org/10.1142/S0217590820500691 Go to original source...
  24. Hung, N. T. (2020b). Does volatility transmission between stock market returns of Central and Eastern European countries vary from normal to turbulent periods? Acta Oeconomica, 70(3), 449-468. https://doi.org/10.1556/032.2020.00022 Go to original source...
  25. Han, H., Linton, O., Oka, T., et al. (2016). The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270. https://doi.org/10.1016/j.jeconom.2016.03.001 Go to original source...
  26. Hung, N. T., Vo, X. V. (2023). Asymmetric impact of the COVID-19 pandemic on foreign exchange markets: evidence from an extreme quantile approach. Economics and Business Letters, 12(1), 20-32. https://doi.org/10.17811/ebl.12.1.2023.20-32 Go to original source...
  27. Joseph, N. L., Vo, T. T. A., Mobarek, A., et al. (2020). Volatility and asymmetric dependence in Central and East European stock markets. Review of quantitative finance and accounting, 55, 1241-1303. https://doi.org/10.1007/s11156-020-00874-0 Go to original source...
  28. Jeris, S. S., Chowdhury, A. N. U. R., Akter, M. T., et al. (2022). Cryptocurrency and stock market: bibliometric and content analysis. Heliyon, 8(9), e10514. https://doi.org/10.1016/j.heliyon.2022.e10514 Go to original source...
  29. Naeem, M. A., Pham, L., Senthilkumar, A., et al. (2022). Oil shocks and BRIC markets: Evidence from extreme quantile approach. Energy Economics, 108, 105932. https://doi.org/10.1016/j.eneco.2022.105932 Go to original source...
  30. Kang, S. H., Uddin, G. S., Troster, V., et al. (2019). Directional spillover effects between ASEAN and world stock markets. Journal of Multinational Financial Management, 52, 100592. https://doi.org/10.1016/j.mulfin.2019.100592 Go to original source...
  31. Karim, S., Lucey, B. M., Naeem, M. A., et al. (2022). Examining the interrelatedness of NFTs, DeFi tokens and cryptocurrencies. Finance Research Letters, 47, 102696. https://doi.org/10.1016/j.frl.2022.102696 Go to original source...
  32. Piñeiro-Chousa, J., López-Cabarcos, M. Á., Sevic, A., et al. (2022). A preliminary assessment of the performance of DeFi cryptocurrencies in relation to other financial assets, volatility, and user-generated content. Technological Forecasting and Social Change, 181, 121740. https://doi.org/10.1016/j.techfore.2022.121740 Go to original source...
  33. Razzaq, A., Sharif, A., An, H., et al. (2022). Testing the directional predictability between carbon trading and sectoral stocks in China: New insights using cross-quantilogram and rolling window causality approaches. Technological Forecasting and Social Change, 182, 121846. https://doi.org/10.1016/j.techfore.2022.121846 Go to original source...
  34. Sinha, A., Sharif, A., Adhikari, A., et al. (2022). Dependence structure between Indian financial market and energy commodities: a cross-quantilogram based evidence. Annals of Operations Research, 313(1), 257-287. https://doi.org/10.1007/s10479-021-04511-4 Go to original source...
  35. Şoiman, F., Dumas, J. G., Jimenez-Garces, S. (2023). What drives DeFi market returns? Journal of International Financial Markets, Institutions and Money, 101786. https://doi.org/10.1016/j.intfin.2023.101786 Go to original source...
  36. Tiwari, A. K., Raheem, I. D., Kang, S. H. (2019). Time-varying dynamic conditional correlation between stock and cryptocurrency markets using the copula-ADCC-EGARCH model. Physica A: Statistical Mechanics and its Applications, 535(C), 122295. https://doi.org/10.1016/j.physa.2019.122295 Go to original source...
  37. Umar, M., Hung, N. T., Chen, S., et al. (2020). Are stock markets and cryptocurrencies connected? The Singapore Economic Review. https://doi.org/10.1142/S0217590820470050 Go to original source...
  38. Wang, H., Wang, X., Yin, S., et al. (2022). The asymmetric contagion effect between stock market and cryptocurrency market. Finance Research Letters, 46(A), 102345. https://doi.org/10.1016/j.frl.2021.102345 Go to original source...
  39. Wang, Y., Horky, F., Baals, L. J., et al. (2022). Bubbles all the way down? Detecting and date-stamping bubble behaviours in NFT and DeFi markets. Journal of Chinese Economic and Business Studies, 20(4), 415-436. https://doi.org/10.1080/14765284.2022.2138161 Go to original source...
  40. Yousaf, I., Nekhili, R., Gubareva, M. (2022). Linkages between DeFi assets and conventional currencies: Evidence from the COVID-19 pandemic. International Review of Financial Analysis, 81, 102082. https://doi.org/10.1016/j.irfa.2022.102082 Go to original source...
  41. Yousaf, I., Yarovaya, L. (2022). Static and dynamic connectedness between NFTs, Defi and other assets: Portfolio implication. Global Finance Journal, 53(C), 100719. https://doi.org/10.1016/j.gfj.2022.100719 Go to original source...
  42. ®ivkov, D., Đuraąković, J., Ljubenović, S. (2023). Multiscale Interdependence Between Consumer and Producer Prices in Emerging Eastern European Countries. Politická ekonomie, 71(3), 319-341. https://doi.org/10.18267/j.polek.1390 Go to original source...

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