Yaya, OlaOluwa A and Lukman, Adewale F. and Vo, Xuan Vinh (2022): Persistence and Volatility Spillovers of Bitcoin price to Gold and Silver prices. Forthcoming in: Resources Policy
Preview |
PDF
MPRA_paper_114521.pdf Download (453kB) | Preview |
Abstract
The paper investigated persistence, returns and volatility spill overs from the Bitcoin market to Gold and Silver markets using daily datasets from 2 January 2018 to 31 July 2020. We applied the fractional persistence framework to the price series, returns and volatility proxy series. The results showed that price persistence with Bitcoin posed the highest volatility, while Silver posed the lowest volatility. The results of multivariate GARCH modelling, using the CCC-VARMA-GARCH model and other lower variants indicated the impossibility of returns spill over between Bitcoin and Gold (or Silver) market, while there existed volatility spill overs and these were bi-directional in form of shocks and volatility transmissions. Appropriate portfolio management and hedging strategies rendered towards the end of the paper required more gold and silver investments in the portfolio of Bitcoin to fully have the diversification advantage and reduce risk to the minimum without reducing the portfolio return expectancy.
Item Type: | MPRA Paper |
---|---|
Original Title: | Persistence and Volatility Spillovers of Bitcoin price to Gold and Silver prices |
Language: | English |
Keywords: | Bitcoin; Commodity markets; CCC-VARMA-GARCH model; Volatility spill overs; Portfolio management |
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: | 114521 |
Depositing User: | Dr OlaOluwa Yaya |
Date Deposited: | 21 Sep 2022 16:39 |
Last Modified: | 21 Sep 2022 16:39 |
References: | Adekoya, O.B., Oliyide, J. A., Yaya, O. S. and Al-Faryan, M. A. S. (2022). Does oil connect differently with prominent assets during war? Evidence from intra-day data during the Russia-Ukraine saga. Resources Policy, 77, 102728. Arouri, M., Jouini, J. and Nguyen, D. (2011). Volatility spillovers between oil prices and stock sector returns: implications for portfolio management. J. Int. Money Financ., 30, 1387–1405. Aggarwal, R., Lucey, B. M. (2007). Psychological barriers in gold prices? Rev. Financ. Econ. 16, 217–230. Auer, B. R. (2016). On the performance of simple trading rules derived from the fractal dynamics of gold and silver price fluctuations'. Finance Res. Lett. 16, 255–267. Babatunde, O. T., Ojo, O. O. and Yaya, O. S. (2021). Modelling Volatility of Bitcoin Prices: Classical or Fractional Integrated GARCH Variants? International Journal of Mathematics and Statistics, 22(2): 21-30. Bariviera, A. F., Basgall, M. J., Hasperué, W. and Naiouf, M. (2017). Some stylized facts of the Bitcoin market. Physica A: Statistical Mechanics and its Applications, 484, 82–90. Batten, J., Ciner, C. and Lucey, B. M. (2010). The macroeconomic determinants of volatility in precious metals markets. Resour. Policy 35 (2), 65–71. Beran, J. (1994). Statistics for Long-Memory Processes, Chapman and Hall, Boca Raton. Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics, 72: 498–505. Bouoiyour, J. and Selmi, R. (2015), What does Bitcoin look like? Annals of Economics and Finance, 16(2), pp. 449-492. Bouri, E., Molnár, P., Azzi, G., Roubaud, D. and Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198. Chevallier, J. (2011). Time-varying correlations in oil, gas and CO2 prices: an application using BEKK, CCC, and DCC-MGARCH models. Applied Economics. 1. 10.1080/00036846.2011.589809 Chuen, D. L. K. (2015). Handbook of digital currency: Bitcoin, innovation, financial instruments, and big data. London: Academic Press. Ciner, C., Gurdgier, C. and Lucey, B. M. (2013). Hedges and safe havens: an examination of stocks, bonds, gold, oil and exchange rates. Int. Rev. Financ. Anal. 29, 202–211. Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. Dwyer, G. P. (2015). The economics of Bitcoin and similar private digital currencies. Journal of Financial Stability, 17, 81–91. Dyhrberg, A. H. (2015). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Res Lett. Dyhrberg, A. H. (2015b). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Res Lett 16, 139–144. Dyhrberg, A. H. (2016) Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Res Lett., 16, 85–92. Elendner, H., Trimborn, S., Ong, B. and Lee, T. M. (2018). The cross-section of cryptocurrencies as financial assets, Volume 1. Handbook of blockchain, digital finance, and inclusion. Vol.649. Handbook of blockchain, digital finance, and inclusion, 145–173. https://doi.org/10.1016/B978-0-12-810441-5.00007-5. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom. Econometrica, 50, 987–1008. Engle, R. F. and Ng, V. K. (1993). Measuring and testing the impact of news on volatility. J. Financ. 48, 1749–1778. Engle, R. F. and Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. Mimeo UCSD. Eryiğit, M. (2017). Short-term and long-term relationships between gold prices and precious metal (palladium, silver and platinum) and energy (crude oil and gasoline) prices. Economic Research-Ekonomska Istraživanja. DOI: 10.1080/1331677X.2017.1305778 Fox, R. and Taqqu, M. S. (1986). Large sample properties of parameter estimates for strongly dependent gaussian time-series. Annals of Statistics, 14:517-532. Gourieroux, C. S. and Monfort, A. (1997). Time Series and Dynamic Models. Trans. ed. G. M. Gallo. Cambridge: Cambridge University Press. Granger, C. W. J. and Joyeux, R. (1980). An introduction to long memory time series and fractionally differencing. J. Time Ser. Anal., 1, 15–29. Geweke, J. and Porter-Hudak, S. (1983). The estimation and application of long memory time series models. J. Time Ser. Anal. 4, 221–238. Gil-Alana, L. A., Yaya, O. S. and Awe, O. O. (2017). Time Series Analysis of Co-movements in the Prices of Gold and Oil: Fractional Cointegration Approach. Resources Policy, 53: 117-224. Gokmenoglu, K. K. and Fazlollahi, N. (2015). 'The Interactions among gold, oil, and stock market: Evidence from S&P500′. Proc. Econ. Finance 25, 478–488. Härdle, W. K., Harvey, C. and Reule, R. (2018). Understanding Cryptocurrencies. Journal of Financial Economics (invited paper). Hosking, J. R. M. (1981). Fractional differencing. Biometrika, 68, 165–176. Klein, T., Thuc, H. P. and Walthera, T. (2018). Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59, 105-116. Kroner, K. F. and Ng, V. K.(1998). Modelling asymmetric movements of asset prices. Rev. Financ. Stud., 11, 844–871. Kroner, K.F. and Sultan, J., 1993. Time-varying distributions and dynamic hedging with foreign currency futures. J. Financ. Quant. Anal., 28, 535–551. Kunsch, H. R. (1987). Statistical aspects of self-similar processes. In: Prokhorov, Y., Sazanov, V.V., (Eds.), Proceedings of the First World Congress of the Bernoulli Society. VNU Science Press, Utrecht, 67–74. Liu, G. and Su, C. (2019). The dynamic causality between gold and silver prices in China market: A rolling window bootstrap approach. Finance Research Letters, 28(C), 101-106. Lucey, B., Larkin, C. and O’Connor, F. (2013). London or New York: where and when does the gold price originate? Appl. Econ. Lett., 20, 813–817. McAleer, M., Hoti S. and Chan, F., (2009), Structure and asymptotic theory for multivariate asymmetric conditional volatility, Econometric Reviews, 28 (5), 422-440. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf. Ooms, M. and Hassler, U. (1997). A note on the effect of seasonal dummies on the periodogram regression. Economic letters, 56, 135-141. Pierdzioch, C., Risse, M. and Rohloff, S., (2015). Cointegration of the prices of gold and silver: RALS-based evidence. Finance Res. Lett. 15, 133–137. Pierdzioch, C., Risse, M. and Rohloff, S. (2016). A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation. Appl. Econ. Lett., 23 (5), 347–352. Robinson, P. (1995a). Log-periodogram regression of time series with long range dependence. Annals of Statistics, 23, 1048-1072. Robinson, P. (1995b). Gaussian semiparametric estimation of long range dependence. Annals of Statistics, 23:1630-1661. Schweikert, K., (2018). Are gold and silver cointegrated? New evidence from quantile cointegrating regressions. J. Bank. Finance, 88, 44–51. Shahzada, S. J. H, Bouri, E., Roubaud, D., Kristoufek, L. and Lucey, B. (2019). Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322-330. Shimotsu, K. and P.C.B. Phillips, 2005, Exact local Whittle estimation of fractional integration, The Annals of Statistics 33, 4. Shimotsu, K., 2010, Exact local Whittle estimation of fractional integration with unknown mean and time trend. Econometric Theory 26, 2. Tiwari, A. K, Abakah, E. J. A., Yaya O. S. and Appiah, K. O. (2022). Tail risk dependence, comovement and predictability between green bond and green stocks. Applied Economics. https://doi.org/10.1080/00036846.2022.2085869 Wahab, M., Cohn, R. and Lashgari, M. (1994). The gold-silver spread: Integration, cointegration, predictability, and ex-ante arbitrage. J. Fut. Markets, 14 (6), 709–756. Wu C. and Pandey, V. (2014). The value of Bitcoin in enhancing the efficiency of an investor’s portfolio. J Financ Plann, 27, 44–52. Yaya, S. O., Tumala M. T. and Udomboso C. G. (2016). Volatility persistence and returns spillovers between oil and gold prices: Analysis before and after the global financial crisis. Resources Policy, 49, 273–281. Yaya, O. S., Ogbonna, A. E. and Olubusoye, O. E. (2019). How Persistent and Dynamic Inter-Dependent are pricing of Bitcoin to other Cryptocurrencies Before and After 2017/18 Crash? Physica A, Statistical Mechanics and its Applications, Volume 531, 1 October 2019, 121732. Yaya, O. S., Vo, X. V. and Olayinka, H. A. (2021a). Gold and Silver prices, their stocks and market fear gauges: Testing fractional cointegration using a robust approach. Resources Policy, 72, August 2021, 102045. Yaya, O. S., Ogbonna, A. E., Mudida, R. and Abu, N. (2021b). Market Efficiency and Volatility Persistence of Cryptocurrency during Pre- and Post-Crash Periods of Bitcoin: Evidence based on Fractional Integration. International Journal of Finance and Economics, 26: 1318–1335. Yaya, O. S., Vo, X. V., Ogbonna, A. E. and Adewuyi, A. O. (2022). Modelling Cryptocurrency High-Low Prices using Fractional Cointegrating VAR. International Journal of Finance and Economics, 27: 489–505. Zhu, H., Peng, C., You, W. (2016). Quantile behaviour of cointegration between silver and gold prices. Finance Res. Lett. 19, 119–125. Zhu, Y., Dickinson, D. and Li, J. (2017). Analysis on the influence factors of Bitcoin’s price based on VEC model. Financial Innovation, 3:3. DOI 10.1186/s40854-017-0054-0 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/114521 |