Forecasting Cryptocurrency Volatility Using a MS-EGARCH Model

다국어 초록 (Multilingual Abstract)

This study analyzes the cryptocurrency index (CRIX) using the generalized autoregressive conditional heteroskedasticity (GARCH) and extended GARCH models. Using the daily cryptocurrency index for July 31, 2014, to March 22, 2019, from the CRIX (https://thecrix.de/), we examine the CRIX return volatility forecast performance of three GARCH models. This empirical research investigates the importance of asymmetry in cryptocurrency volatility, which is not accounted for by the standard GARCH model; thus, asymmetric model variations are applied. The results show that the regime-switching model resolves the single-regime model’s problem of elevated forecasts for high-volatility periods. Additionally, we show that the forecasting performance of the Markov-switching exponential GARCH (MS-EGARCH) model is superior to that of other models. This suggests that the MS-EGARCH model outperforms other models in accounting for cryptocurrency index volatility. Hence, the regime-switching model, which applies asymmetry, has greater explanatory power than the standard GARCH model.

참고문헌 (Reference)

1 최서연, "미국 주식과 가상화폐의 비대칭 변동성과 군집 현상" 한국재무관리학회 35 (35): 163-184, 2018

2 최서연, "가상 화폐의 비선형성에 관한 연구" 한국자료분석학회 20 (20): 791-799, 2018

3 P. Klein, "Using the generalized Schur form to solve a multivariate linear rational expectations model" 24 : 1405-1423, 2000

4 G. W. Schwert, "Stock Market volatility ten years after the crash" 65-114, 1990

5 K. Salhi, "Regime switching model for financial data: Empirical risk analysis" 461 : 148-157, 2016

6 X. Zhu, "Predicting stock returns: A regime-switching combination approach and economic links" 37 : 4120-4133, 2013

7 R. C. Merton, "On estimating the expected return on the market: An exploratory investigation" 8 : 323-361, 1980

8 S. F. Gray, "Modeling the conditional distribution of interest rates as a regime-switching process" 42 (42): 27-62, 1996

9 P. Chaim, "Is Bitcoin a bubble?" 517 : 222-232, 2019

10 F. Klaassen, "Improving GARH volatility forecasts with regime-switching GARCH" 27 : 363-394, 2002

11 T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity" 31 : 307-327, 1986

12 최서연, "GARCH 모형을 적용한 가상화폐 수익률의 변동성 분석" 한국재무관리학회 36 (36): 65-82, 2019

13 Cumhy, "Forecasting volatility and correlations with EGARCH models" 51-63, 1993

14 J. Marcucci, "Forecasting stock market volatility with regime-switching GARCH models" 9 : 1-53, 2005

15 K. R. French, "Expected stock returns and volatility" 19 (19): 3-29, 1987

16 J. B. Gray, "Empirical comparisons of distributional models for stock index returns" 17 (17): 451-459, 1990

17 D. Bianchi, "Cryptocurrencies as an asset class? An empirical assessment" WBS Finance Group 2017

18 F. Allen, "Credit risk transfer and contagion" 53 : 89-111, 2006

19 D. B. Nelson, "Conditional heteroskedasticity in asset returns: A new approach" 59 : 347-370, 1991

20 G. W. Schwert, "Business cycles, financial crises, and stock volatility" 31 : 83-125, 1989

21 T. Klein, "Bitcoin is not the new Gold-A comparison of volatility, correlation, and portfolio performance" 59 : 105-116, 2018

22 C. Eom, "Bitcoin and investor sentiment:Statistical characteristics and predictability" 514 : 511-521, 2019

23 R. F. Engle, "Autoregressive conditional heteroskedasticity with estimates of the variance of U.K inflation" 5 : 51-63, 1982

24 T. Anderson, "Answering the skeptics: YES, standard volatility models do provide accurate forecasts" 39 : 885-905, 1998

25 A. R. Pagan, "Alternative models for conditional stock volatility" 45 : 267-290, 1990

26 J. D. Hamilton, "A new approach to the economic analysis of nonstationary time series and the business cycle" 64 : 307-333, 1989