Author : Sandeep S. Saini 1
Date of Publication :26th August 2021
Abstract: In this analysis; we study the four major cryptocurrency returns that are Bitcoin, Ethereum, XRP, and Litecoin, where the dynamics of volatility spillover are observed for a span of 7 years – 2013 to 2020; wherein the total number of sample observations collected and analyzed were 10,953 (Data Points). This paper investigates the behaviour and responses of cryptocurrency assets with respect to each other by using VAR Granger Causality and Bayesian VAR Model, we find that Ethereum and Litecoin prove to be independent in the cryptocurrency market. Whereas Bitcoin, XRP, and Binance coins tend to be the recipient of the spillover effect. Our study indicates that there is a conditional variance in these cryptocurrency assets and Bitcoin & Binance coins are more adversely affected due to the bad news in the market, leading to rigorous fluctuations in volatility. While approaching for the analysis, we conducted a GARCH (1,1), T-ARCH, and E-GARCH analysis along with a univariate GARCH model which we used to estimate and quantify the nature of volatility spillovers. Given the overall cryptocurrency bull-run in the first quarter of 2017-18 we have analyzed the saturation of the cryptocurrency markets; where investors sought to invest in Bitcoin and Ethereum, vehemently and this resulted in extremely high volatility during the December 17’ period.
Reference :
-
- C. Conrad & A. Custovic, (2018) Long- and ShortTerm Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis, Journal of Risk and Financial Management 11(2):23; DOI: 10.3390/jrfm11020023
- D. Siourounis (2002), Modelling volatility and testing for efficiency in emerging capital markets: the case of the Athens stock exchange, Applied Financial Economics, 47–55.
- Balg, B. A., & Metcalf, H. (2010). Modelling exchange rate volatility. Review of International Economics.
- L.R Glosten, R. Jagannathan and D.E. Runkle, Relationship between the expected value and the volatility of the nominal excess return on stocks, The Journal of Finance, (1993), 1779-1801.
- Kee-Hong Bae, G Andrew Karolyi, Reneé M Stulz, A new approach to measuring financial contagion The Review of Financial Studies, Volume 16, p. 717 – 63, Posted: 2003.
- Neely, C. J., & Weller, P. A. (2009). Predicting exchange rate volatility: genetic programming vs. GARCH and Risk Metrics. Working Paper: 2001- 009B.
- Nelson, D. (1991). Conditional heteroscedasticity in asset returns: A new approach. Econ., 347-370. [8] Ngouana, C. L. (2012). Exchange rate volatility under peg: do trade patterns matter? IMF Working Paper. African Department. WP/12/73.
- Sengupta, K. J. (2002). Modelling exchange rate volatility. Department of Economicus‟s. Departmental Working Papers,12-96.
- So, M. K. P., Lam, K., & Li, W. K. (1999). Forecasting exchange rate volatility using autoregressive random variance model. Applied Financial Economics, 9,583-591.
- Taylor, S. (1986). Modelling financial time series. New York: John Wiley and Sons.
- Hasan, V. (2005). Exchange rate volatility in Turkey and its effect on trade flows. Journal of Economic and Social Research.
- Zakoïan, J. M. (1994). Threshold heteroskedastic models. J. Econ. Dynam. Cont. Elsevier, 931-955.
- Bollerslev, T. P. (1986). Generalized autoregressive conditional heteroscedasticity. J. Econ., 31, 307-327.