تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,504 |
تعداد مشاهده مقاله | 124,122,489 |
تعداد دریافت فایل اصل مقاله | 97,230,434 |
Regime changes between Bitcoin and six other assets using Copula model with Markov switching | ||
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
دوره 17، شماره 3، مهر 2024، صفحه 839-854 اصل مقاله (1.57 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijms.2023.344007.675144 | ||
نویسندگان | ||
Sajad Jamalian؛ Mohammad Ali Rastegar* | ||
Department of Financial Engineering, Faculty of Industrial and System Engineering, University of Tarbiat Modares, Tehran, Iran | ||
چکیده | ||
Examining the structure of dependence between financial assets and the effects of their Co-movement is one of the important issues in financial markets. The corresponding copula is one of the most computationally convenient ways to describe the dependency structure. This paper examines regime change probability and the best copula model between Bitcoin and six other assets from 2018 to 2021. First, using the ARMA-GARCH model, the marginal distribution functions for all assets and residuals are calculated. Then, by using the obtained residuals, 11 models of copula and six models of combined Copula with Markov switching were implemented. The model that has the best function for constructing combined distribution functions is selected. Finally, the regime probabilities each time are calculated from the best-fitted model. The results show that in the study period, for Bitcoin-Ethereum, Bitcoin-Cardano, and Bitcoin-Gold pairs MS-CT, for Bitcoin-Binance coin and Bitcoin-Ripple pairs MS-CRG and MS-CN for Bitcoin-Oil pair have the best performance. Furthermore, the probabilities of regime change between each asset at each time were calculated and described. | ||
کلیدواژهها | ||
Copula؛ Markov switching؛ Bitcoin؛ Crypto؛ ARMA-GARCH | ||
مراجع | ||
Aloui, R., Hammoudeh, S., & Nguyen, D. K. (2013). A time-varying copula approach to Oil and stock market dependence: The case of transition economies. Energy Economics, 39, 208-221. Awartani, B., & Maghyereh, A. I. (2013). Dynamic spillovers between Oil and stock markets in the Gulf Cooperation Council Countries. Energy Economics, 36, 28-42. Bai, L., Wei, Y., Wei, G., Li, X., & Zhang, S. (2021). Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective. Finance research letters, 40, 101709. Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money, 54, 177-189. Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets?. Journal of International Financial Markets, Institutions and Money, 54, 177-189. Bouri, E., Das, M., Gupta, R., & Roubaud, D. (2018). Spillovers between Bitcoin and other assets during bear and bull markets. Applied Economics, 50(55), 5935-5949. Bouri, E., Shahzad, S. J. H., Roubaud, D., Kristoufek, L., & Lucey, B. (2020). Bitcoin, Gold, and commodities as safe havens for stocks: New insight through wavelet analysis. The Quarterly Review of Economics and Finance, 77, 156-164. Brechmann, Eike Christian, and Ulf Schepsmeier. "Modeling dependence with C-and D-vine copulas: the R package CDVine." Journal of statistical software 52 (2013): 1-27. Brooks, C. (2002). Introductory Econometrics for Finance, Cambridge, UK: Cambridge U. Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula methods in finance. John Wiley & Sons. Das, D., Le Roux, C. L., Jana, R. K., & Dutta, A. (2020). Does Bitcoin hedge crude Oil implied volatility and structural shocks? A comparison with Gold, commodity, and the US Dollar. Finance Research Letters, 36, 101335. Dyhrberg, A. H. (2016). Bitcoin, Gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92. Gong, X., & Lin, B. (2021). Effects of structural changes on the prediction of downside volatility in futures markets. Journal of Futures Markets, 41(7), 1124-1153. Gozgor, G., Tiwari, A. K., Khraief, N., & Shahbaz, M. (2019). Dependence structure between business cycles and CO2 emissions in the US: Evidence from the time-varying Markov-Switching Copula models. Energy, 188, 115995. Hansen, B. E. (1994). Autoregressive conditional density estimation. International Economic Review, 705-730. Jareño, F., de la O González, M., Tolentino, M., & Sierra, K. (2020). Bitcoin and Gold price returns: A quantile regression and NARDL analysis. Resources Policy, 67, 101666. Jareño, F., de la O González, M., Tolentino, M., & Sierra, K. (2020). Bitcoin and Gold price returns: A quantile regression and NARDL analysis. Resources Policy, 67, 101666. Ji, Q., Liu, B. Y., Cunado, J., & Gupta, R. (2020). Risk spillover between the US and the remaining G7 stock markets using time-varying copulas with Markov switching: Evidence from over a century of data. The North American Journal of Economics and Finance, 51, 100846. Klein, T., Thu, H. P., & Walther, T. (2018). Bitcoin is not the New Gold–A comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59, 105-116. Kliber, A., Marszałek, P., Musiałkowska, I., & Świerczyńska, K. (2019). Bitcoin: Safe haven, hedge or diversifier? Perception of bitcoin in the context of a country’s economic situation—A stochastic volatility approach. Physica A: Statistical Mechanics and Its Applications, 524, 246-257. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review, 21260. Nguyen, C. C., & Bhatti, M. I. (2012). Copula model dependency between Oil prices and stock markets: Evidence from China and Vietnam. Journal of International Financial Markets, Institutions and Money, 22(4), 758-773. Niu, H., Xu, K., & Xiong, M. (2023). The Risk Contagion between Chinese and Mature Stock Markets: Evidence from a Markov-Switching Mixed-Clayton Copula Model. Entropy, 25(4), 619. Patton, A. J. (2006). Modeling asymmetric exchange rate dependence. International economic review, 47(2), 527-556. Shahzad, S. J. H., Bouri, E., Roubaud, D., Kristoufek, L., & Lucey, B. (2019). Is Bitcoin a better safe-haven investment than Gold and commodities?. International Review of Financial Analysis, 63, 322-330. Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publ. inst. statist. univ. Paris, 8, 229-231. Tiwari, A. K., Abakah, E. J. A., Le, T. L., & Leyva-de la Hiz, D. I. (2021). Markov-switching dependence between artificial intelligence and carbon price: The role of policy uncertainty in the era of the 4th industrial revolution and the effect of COVID-19 pandemic. Technological Forecasting and Social Change, 163, 120434 Wang, G., Tang, Y., Xie, C., & Chen, S. (2019). Is bitcoin a safe haven or a hedging asset? Evidence from China. Journal of Management Science and Engineering, 4(3), 173-188. Wang, Y. C., Wu, J. L., & Lai, Y. H. (2013). A revisit to the dependence structure between the stock and foreign exchange markets: A dependence-switching copula approach. Journal of Banking & Finance, 37(5), 1706-1719. Zhu, K., Yamaka, W., & Sriboonchitta, S. (2016). Multi-asset portfolio returns: a Markov switching copula-based approach. Thai Journal of Mathematics, 183-200. | ||
آمار تعداد مشاهده مقاله: 208 تعداد دریافت فایل اصل مقاله: 184 |