Yaya, OaOluwa S and Vo, Xuan Vinh and Ogbonna, Ahamuefula E and Adewuyi, Adeolu O (2020): Modelling Cryptocurrency High-Low Prices using Fractional Cointegrating VAR.
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Abstract
This paper empirically provides support for fractional cointegration of high and low cryptocurrency price series, using particularly, Bitcoin, Ethereum, Litecoin and Ripple; synchronized at different high time frequencies. The difference of high and low price gives the price range, and the range-based estimator of volatility is more efficient than the return-based estimator of realized volatility. A more general fractional cointegration technique applied is the Fractional Cointegrating Vector Autoregressive framework. The results show that high and low cryptocurrency prices are actually cointegrated in both stationary and non-stationary levels; that is, the range of high-low price. It is therefore quite interesting to note that the fractional cointegration approach presents a lower measure of the persistence for the range compared to the fractional integration approach, and the results are insensitive to different time frequencies. The main finding in this work serves as an alternative volatility estimation method in cryptocurrency and other assets’ price modelling and forecasting.
Item Type: | MPRA Paper |
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Original Title: | Modelling Cryptocurrency High-Low Prices using Fractional Cointegrating VAR |
Language: | English |
Keywords: | Fractional cointegration; Cryptocurrency; Fractional integration; FCVAR; Price range |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes |
Item ID: | 102190 |
Depositing User: | Dr OlaOluwa Yaya |
Date Deposited: | 03 Aug 2020 10:55 |
Last Modified: | 03 Aug 2020 10:55 |
References: | Afzal, A. and Sibbertsen, P. (2019). Modeling fractional cointegration between high and low stock prices in Asian countries. Empirical Economics. 10.1007/s00181-019-01784-4. Alizadeh, S., Brandt, M.W. and Diebold, F.X. (2002). Range-based estimation of stochastic volatility. The Journal of Finance, 57(3), 1047–1091. Brandt, M. W. and Diebold, F. X. (2006). A no-arbitrage approach to range-based estimation of returns covariances and correlations. The Journal of Business, 79(1), 61-74. Barunik, J. and Dvorakova, S. (2015). An empirical model of fractionally cointegrated daily high and low stock market prices. Economic Modelling, 45: 193-206. Caporale, G. M. and Gil-Alana, L. A. (2019). Long-term interest rates in Europe: A fractional cointegration analysis. International Review of Economics and Finance, 61: 170-178. Caporin, M., Ranaldo, A. and Magistris, P. C. (2013). On the predictability of stock prices: A case for high and low prices. Journal of Banking and Finance, 37, 5132-5146. Cheung, Y.W. (2007). An empirical model of daily highs and lows. International Journal of Finance and Economics, 12 (1), 1–20. Cheung, Y.W. and Lai, K.S. (1993). A fractional cointegration analysis of purchasing power parity, Journal of Business and Economic Statistics 11, 103-112. Cheung, Y.L., Cheung, Y.W. and Wan, A.T.K. (2009). A high-low model of daily stock price ranges. Journal of Forecasting, 28(2): 103 – 119. Cheung, Y.L., Cheung, Y.W. He, A.W.W., Wan, A.T.K. (2010). A trading strategy based on Callable Bull/Bear Contracts. Pac Basin Finance J 18(2):186 – 198. https://doi.org/10.1016/j.pacfi n.2009.11.002 Degiannakis, S. and Floros, C. (2013). Modelling CAC40 volatility using ultra-high frequency data. Research in International Business and Finance, 28, 68-81. Dickey, D.A and Fuller, W. A. (1979). Distributions of the estimators for autoregressive time series with a unit root, Journal of American Statistical Association, 74 (366), 427-481. Diebold, F.X. and Rudebusch, G.D. (1991). On the power of Dickey-Fuller test against fractional alternatives. Economics Letters, 35, 155–160. Dolatabadi, S., Nielsen, M. and Xu, K. (2016). A fractionally cointegrated VAR model with deterministic trends and application to commodity future markets. Journal of Empirical Finance, 38 (B): 623-639. Engle, R. and C.W.J. Granger (1987), Cointegration and error correction. Representation, estimation and testing, Econometrica, 55, 2, 251-276. Geweke, J. and Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4: 221-238. Gil-Alana, L.A. (2003). Testing of fractional cointegration in macroeconomic time series, Oxford Bulletin of Economics and Statistics, 65, 4, 517-529. Gil-Alana, L.A. and Hualde, J. (2009). Fractional integration and cointegration. An overview with an empirical application. The Palgrave Handbook of Applied Econometrics, Volume 2. Edited by Terence C. Mills and Kerry Patterson, MacMillan Publishers, pp. 434-472. Hassler, U. and Wolters, J. (1994). On the power of unit root tests against fractional alternatives. Economic Letters, 45: 1–5. Haniff, M. N. and Pok, W. C. (2010). Intraday volatility and periodicity in the Malaysian stock returns. Research in International Business and Finance, 24(3), 329–343. He, A. and Wan, A. (2009). Predicting daily highs and lows of exchange rates: a cointegration analysis. Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1191-1204. He, Y., Wang, SH., Lai, K.K. (2010). Global economic activity and crude oil prices: A cointegration analysis. Energy Economics, 32(4), 868-876. Hualde, J. and Robinson, P.M. (2007). Root n-consistent estimation of weak fractional cointegration, Journal of Econometrics 140, 450-484. Johansen, S. (1995). Identifying restrictions of linear equations with applications to simultaneous equations and cointegration. Journal of Econometrics, 69(1), 111–132. Johansen, S. and M. Nielsen (2012). Likelihood inference for a fractionally cointegrated vector autoregressive model. Econometrica, 80, 2667-2732. Johansen, S. and Nielsen, M. O. (2014). The role of initial values in conditional sum-of-squares estimation of non-stationary fractional time series models. QED working paper 1300, Queen’s University. Maciel, L. (2018). Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model. Empirical Economics. https://doi.org/10.1007/s00181-018-1603-8 Nielsen, M.A. and Popiel, M.K. (2018). A Matlab program and user’s guide for the fractionally cointegrated VAR model. Queen’s Economics Department Working Paper No. 1330. Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 53 (1): 61–65. Qu, Z. (2011). A Test Against Spurious Long Memory. Journal of Business and Economic Statistics, Vol. 29, No. 3, pp. 423 - 438. Robinson, P.M. (1995a). Gaussian Semiparametric Estimation of Long Range Dependence, Annals of Statistics, Vol. 23, pp. 1630-1661. Robinson, P.M. (1995b). Log-periodogram Regression of Time Series with Long Range Dependence. Annals of Statistics, Vol. 23, pp. 1048-1072. Robinson, P.M. and Hualde, J. (2003). Cointegration in fractional systems with an unknown integration order, Econometrica 71, 1727-1766. Robinson, P.M. and Marinucci, D. (2003). Semiparametric frequency domain analysis of fractional cointegration, in P.M. Robinson eds., Time Series with Long Memory, Oxford. Robinson, P.M. and Yajima, Y. (2002). Determination of cointegrating rank in fractional systems. Journal of Econometrics, 106 (2), 217–241. Shu, J. H. and Zhang, J. E. (2006). Testing range estimators of historical volatility. Journal of Future Markets, 26(3), 297–313. Xiong, T., Li, C. and Bao, Y. (2017). Interval-valued time series forecasting using a novel hybrid Holt and MSVR model. Economic Modelling, 60,11–23. Yaya, O. S., Gil-Alana, L. A. and Olubusoye, O. E. (2017). The Global Financial Crisis: Testing for Fractional Cointegration Between US and Nigerian Stock Markets. The Journal of Developing Areas, 51(4): 29-47. Yaya, O. S. and Gil-Alana, L. A. (2018). High and Low Intraday Commodity Prices: A Fractional Integration and Cointegration approach. MPRA Paper No. 90518, posted 15 December 2018. Online at https://mpra.ub.uni-muenchen.de/90518/ |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/102190 |