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Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches

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  • Shafique Ur Rehman
  • Touqeer Ahmad
  • Wu Dash Desheng
  • Amirhossein Karamoozian

Abstract

This study examines the interdependence between cryptocurrencies and international financial indices, such as MSCI World and MSCI Emerging Markets. We compute the value at risk, expected shortfall (ES), and range value at risk (RVaR) and investigate the dynamics of risk spillover. We employ a hybrid approach to derive these risk measures that integrate GARCH models, extreme value models, and copula functions. This framework uses a bivariate portfolio approach involving cryptocurrency data and traditional financial indices. To estimate the above risks of these portfolio structures, we employ symmetric and asymmetric GARCH and both tail flexible EVT models as marginal to model the marginal distribution of each return series and apply different copula functions to connect the pairs of marginal distributions into a multivariate distribution. The empirical findings indicate that the eGARCH EVT-based copula model adeptly captures intricate dependencies, surpassing conventional methodologies like Historical simulations and t-distributed parametric in VaR estimation. At the same time, the HS method proves superior for ES, and the t-distributed parametric method outperforms RVaR. Eventually, the Diebold-Yilmaz approach will be applied to compute risk spillovers between four sets of asset sequences. This phenomenon implies that cryptocurrencies reveal substantial spillover effects among themselves but minimal impact on other assets. From this, it can be concluded that cryptocurrencies propose diversification benefits and do not provide hedging advantages within an investor's portfolio. Our results underline RVaR superiority over ES regarding regulatory arbitrage and model misspecification. The conclusions of this study will benefit investors and financial market professionals who aspire to comprehend digital currencies as a novel asset class and attain perspicuity in regulatory arbitrage.

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  • Shafique Ur Rehman & Touqeer Ahmad & Wu Dash Desheng & Amirhossein Karamoozian, 2024. "Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches," Papers 2407.15766, arXiv.org.
    • Handle: RePEc:arx:papers:2407.15766
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    1. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    2. Liu, Jianjian & Wang, Shuhan & Xiang, Lijin & Ma, Shiqun & Xiao, Zumian, 2024. "Unveiling hidden connections: Spillover among BRICS' cryptocurrency-implied exchange rate discounts and US financial markets," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    3. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.
    4. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. Müller, Fernanda Maria & Santos, Samuel Solgon & Gössling, Thalles Weber & Righi, Marcelo Brutti, 2022. "Comparison of risk forecasts for cryptocurrencies: A focus on Range Value at Risk," Finance Research Letters, Elsevier, vol. 48(C).
    7. Yaya, OlaOluwa S. & Lukman, Adewale F. & Vo, Xuan Vinh, 2022. "Persistence and volatility spillovers of bitcoin price to gold and silver prices," Resources Policy, Elsevier, vol. 79(C).
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Hanif, Waqas & Teplova, Tamara & Rodina, Victoria & Alomari, Mohammed & Mensi, Walid, 2023. "Volatility spillovers and frequency dependence between oil price shocks and green stock markets," Resources Policy, Elsevier, vol. 85(PB).
    10. K. D. Patterson & S. M. Heravi, 2003. "The impact of fat-tailed distributions on some leading unit roots tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(6), pages 635-667.
    11. Rama Cont & Romain Deguest & Giacomo Scandolo, 2010. "Robustness and sensitivity analysis of risk measurement procedures," Quantitative Finance, Taylor & Francis Journals, vol. 10(6), pages 593-606.
    12. Jiang, Yonghong & Lie, Jiayi & Wang, Jieru & Mu, Jinqi, 2021. "Revisiting the roles of cryptocurrencies in stock markets: A quantile coherency perspective," Economic Modelling, Elsevier, vol. 95(C), pages 21-34.
    13. Bhanja, Niyati & Shah, Adil Ahmad & Dar, Arif Billah, 2023. "Aggregate, asymmetric and frequency-based spillover among equity, precious metals, and cryptocurrency," Resources Policy, Elsevier, vol. 80(C).
    14. Hanif, Waqas & Mensi, Walid & Vo, Xuan Vinh & BenSaïda, Ahmed & Hernandez, Jose Arreola & Kang, Sang Hoon, 2023. "Dependence and risk management of portfolios of metals and agricultural commodity futures," Resources Policy, Elsevier, vol. 82(C).
    15. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    16. Ruodu Wang & Yunran Wei, 2020. "Risk functionals with convex level sets," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1337-1367, October.
    17. Chebbi, Ali & Hedhli, Amel, 2022. "Revisiting the accuracy of standard VaR methods for risk assessment: Using the Copula–EVT multidimensional approach for stock markets in the MENA region," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 430-445.
    18. Amar Rao & Mansi Gupta & Gagan Deep Sharma & Mandeep Mahendru & Anirudh Agrawal, 2022. "Revisiting the financial market interdependence during COVID-19 times: a study of green bonds, cryptocurrency, commodities and other financial markets," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 18(4), pages 725-755, June.
    19. Karmakar, Madhusudan & Paul, Samit, 2019. "Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 699-709.
    20. Mizerka, Jacek & Stróżyńska-Szajek, Agnieszka & Mizerka, Piotr, 2020. "The role of Bitcoin on developed and emerging markets – on the basis of a Bitcoin users graph analysis," Finance Research Letters, Elsevier, vol. 35(C).
    21. Steven Kou & Xianhua Peng & Chris C. Heyde, 2013. "External Risk Measures and Basel Accords," Mathematics of Operations Research, INFORMS, vol. 38(3), pages 393-417, August.
    22. Michael L. Stein, 2021. "A parametric model for distributions with flexible behavior in both tails," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
    23. Gkillas, Konstantinos & Tsagkanos, Athanasios & Vortelinos, Dimitrios I., 2019. "Integration and risk contagion in financial crises: Evidence from international stock markets," Journal of Business Research, Elsevier, vol. 104(C), pages 350-365.
    24. Ahmed Jeribi & Mohamed Fakhfekh, 2021. "Portfolio management and dependence structure between cryptocurrencies and traditional assets: evidence from FIEGARCH-EVT-Copula," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 224-239, May.
    25. Michael L. Stein, 2021. "A parametric model for distributions with flexible behaviour in both tails," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    26. Amar Rao & Mansi Gupta & Gagan Deep Sharma & Mandeep Mahendru & Anirudh Agrawal, 2022. "Revisiting the financial market interdependence during COVID-19 times: a study of green bonds, cryptocurrency, commodities and other financial markets," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 18(4), pages 725-755, June.
    27. Katherine Baer & Ruud De Mooij & Shafik Hebous & Michael Keen, 2023. "Taxing cryptocurrencies," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 39(3), pages 478-497.
    28. Fabio Bellini & Valeria Bignozzi, 2015. "On elicitable risk measures," Quantitative Finance, Taylor & Francis Journals, vol. 15(5), pages 725-733, May.
    29. Kyriazis, Nikolaos & Papadamou, Stephanos & Tzeremes, Panayiotis & Corbet, Shaen, 2024. "Quantifying spillovers and connectedness among commodities and cryptocurrencies: Evidence from a Quantile-VAR analysis," Journal of Commodity Markets, Elsevier, vol. 33(C).
    30. Kellner, Ralf & Rösch, Daniel, 2016. "Quantifying market risk with Value-at-Risk or Expected Shortfall? – Consequences for capital requirements and model risk," Journal of Economic Dynamics and Control, Elsevier, vol. 68(C), pages 45-63.
    31. Karimi, Parinaz & Mirzaee Ghazani, Majid & Ebrahimi, Seyed Babak, 2023. "Analyzing spillover effects of selected cryptocurrencies on gold and brent crude oil under COVID-19 pandemic: Evidence from GJR-GARCH and EVT copula methods," Resources Policy, Elsevier, vol. 85(PB).
    32. Huang, Linxian, 2024. "The relationship between cryptocurrencies and convention financial market: Dynamic causality test and time-varying influence," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 811-826.
    33. MacDonald, A. & Scarrott, C.J. & Lee, D. & Darlow, B. & Reale, M. & Russell, G., 2011. "A flexible extreme value mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2137-2157, June.
    34. Ferko, Alex & Moin, Amani & Onur, Esen & Penick, Michael, 2023. "Who trades bitcoin futures and why?," Global Finance Journal, Elsevier, vol. 55(C).
    35. Lazar, Emese & Pan, Jingqi & Wang, Shixuan, 2024. "On the estimation of Value-at-Risk and Expected Shortfall at extreme levels," Journal of Commodity Markets, Elsevier, vol. 34(C).
    36. Ugolini, Andrea & Reboredo, Juan C. & Mensi, Walid, 2023. "Connectedness between DeFi, cryptocurrency, stock, and safe-haven assets," Finance Research Letters, Elsevier, vol. 53(C).
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