Research article

Asymmetric Effects on Risks of Virtual Financial Assets (VFAs) in different regimes: A Case of Bitcoin

  • Received: 12 November 2018 Accepted: 20 November 2018 Published: 22 November 2018
  • JEL Codes: C32, C44, C51

  • The rapid development of VFAs allows investors to diversify their choices of investment products. In this paper, we measure the return risk of VFAs based on GARCH-type model. By establishing a Markov regime-switching Regression (MSR) Model, we explore the asymmetric effects of speculation, investor attention, and market interoperability on return risks in different risk regimes of VFAs. The results show that the influences of speculation and investor attention on the risks of VFAs are significantly positive at all regimes, while market interoperability only admits a positive impact on risk under high risk regime. All of the three factors exert asymmetric effects on risks in different regimes. Further study presents that the risk regime-switching also shows asymmetric characteristic but the medium risk regime is more stable than any others. Therefore, transactions of investors and arbitrageurs are monitored by certain policies, such as limiting the number of transactions or restricting the trading amount at high risk regime. However, when return risk is low, it will return to a medium level if we encourage investors to access.

    Citation: Zhenghui Li, Hao Dong, Zhehao Huang, Pierre Failler. Asymmetric Effects on Risks of Virtual Financial Assets (VFAs) in different regimes: A Case of Bitcoin[J]. Quantitative Finance and Economics, 2018, 2(4): 860-883. doi: 10.3934/QFE.2018.4.860

    Related Papers:

  • The rapid development of VFAs allows investors to diversify their choices of investment products. In this paper, we measure the return risk of VFAs based on GARCH-type model. By establishing a Markov regime-switching Regression (MSR) Model, we explore the asymmetric effects of speculation, investor attention, and market interoperability on return risks in different risk regimes of VFAs. The results show that the influences of speculation and investor attention on the risks of VFAs are significantly positive at all regimes, while market interoperability only admits a positive impact on risk under high risk regime. All of the three factors exert asymmetric effects on risks in different regimes. Further study presents that the risk regime-switching also shows asymmetric characteristic but the medium risk regime is more stable than any others. Therefore, transactions of investors and arbitrageurs are monitored by certain policies, such as limiting the number of transactions or restricting the trading amount at high risk regime. However, when return risk is low, it will return to a medium level if we encourage investors to access.


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    [1] Andersen TG, Bollerslev T (1997) Intraday periodicity and volatility persistence in financial markets. J Empir Financ 4: 115–158. doi: 10.1016/S0927-5398(97)00004-2
    [2] Balcilar M, Elie B, Rangan G, et al. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Econ Model 64: 74–81.
    [3] Blau BM (2017) Price dynamics and speculative trading in bitcoin. Res in Int Business and Financ 41: 493–499. doi: 10.1016/j.ribaf.2017.05.010
    [4] Baur DG, Hong K, Lee AD (2017) Medium of exchange or speculative Assets? Working paper, SSRN.
    [5] Bollerslev T (1986) Generalized autoregressive conditional heteroscedasticity. J Econom 3:307–327.
    [6] Bouri E, Jalkh N, Molnar P, et al. (2017a) Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven? Applied Economic 49: 5063–5073.
    [7] Bouri E, Rangan G, Aviral KT, et al. (2017b) Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Financ Res Lett 23: 87–95.
    [8] Carrick J (2016) Bitcoin as a Complement to Emerging Market Currencies. Emerg Mark Financ Trade 52: 2321–2334. doi: 10.1080/1540496X.2016.1193002
    [9] Cabedoa JD, Moyab I (2003) Estimating oil price 'Value at Risk' using the historical simulation approach. Energy Econ 25: 239–253.
    [10] Chen Y (2018) Blockchain tokens and the potential democratization of entrepreneurship and innovation. Bus Horiz 61: 567–575. doi: 10.1016/j.bushor.2018.03.006
    [11] Cocco L, Marchesi M (2016) Modeling and Simulation of the Economics of Mining in the Bitcoin Market. PloS ONE 11: e0164603. doi: 10.1371/journal.pone.0164603
    [12] Davidson S, Filippi PD, Potts J (2018) Blockchains and the economic institutions of capitalism. J Inst Econ 14: 639–658.
    [13] De Bondt WFM, Thaler RH (1995) Financial decision-making in markets and firms: A behavioral perspective. Handbooks in operations res and management sci 9: 385–410. doi: 10.1016/S0927-0507(05)80057-X
    [14] Dyhrberg AH, Foley S, Svec J (2018) How investible is Bitcoin? Analyzing the liquidity and transaction costs of Bitcoin markets. Econ Lett 171: 140–143.
    [15] Feng WJ, Wang YM, Zhang ZJ (2018) Informed trading in the Bitcoin market. Financ Res Lett 26: 63–70. doi: 10.1016/j.frl.2017.11.009
    [16] Garcia D, Tessone C, Mavrodiev P, et al. (2014) The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy. J of the Royal Society Interface 11: 1–8.
    [17] Gkillas K, Katsiampa P (2018). An application of extreme value theory to cryptocurrencies. Econ Lett, 164: 109–111. doi: 10.1016/j.econlet.2018.01.020
    [18] Glaser F, Zimmarmann K, Haferhorn M, et al. (2014) Bitcoin-Asset or currency? Revealing users' hidden intentions. ECIS 2014, Tel Aviv.
    [19] Grinberg R (2012) Bitcoin: An Innovative Alternative Digital Currency. Hastings Sci & Technology Law J 4: 159–207.
    [20] Groshoff D (2014) Kickstarter My Heart: Extraordinary Popular Delusions and the Madness of Crowdfunding Constraints and Bitcoin Bubbles. William & Mary Business Law Rev 5: 489–557.
    [21] Gunter FR (2017) Corruption, costs, and family: Chinese capital flight, 1984–2014. China Econ Rev 43: 105–117. doi: 10.1016/j.chieco.2017.01.010
    [22] Hamilton JD (2008) Regime switching models. In: Durlauf, N., & Blume, L.E. (Eds.), The New Palgrave Dictionary of Economics, 2nd edition. London: Palgrave Macmillan.
    [23] Hayes AS (2016) Cryptocurrency value formation: an empirical study leading to a cost of production model for valuing bitcoin. Telematics and Informatics 34: 1308–1321.
    [24] Jacobs E (2011) Bitcoin: A Bit Too Far? J of IntBanking and Commerc 16: 1–4.
    [25] Kristoufek L (2015) What are the main drivers of the bitcoin price? evidence from wavelet coherence analysis. PLOS ONE 10: e0123923.
    [26] Kristoufek L (2013) Bitcoin meets Google trends and wikipedia: quantifying the relationship between phenomena of the Internet era. Scientific Reports 3:3415. doi: 10.1038/srep03415
    [27] Lan J, Timothy L, Tu Z (2016) Capital Flight and Bitcoin Regulation. Int Rev Financ 16: 445–455. doi: 10.1111/irfi.12072
    [28] Llorente G, Michaely R, Saar G, et al. (2002) Dynamic volume-Return relation of individual stocks. Rev Financ Stud 15: 1005–1047. doi: 10.1093/rfs/15.4.1005
    [29] Li Z, Chen S, Chen S (2017) Statistical Measure of Validity of Financial Resources Allocation. EURASIA J of Mathematics, Science and Technology Education 13: 7731–7741.
    [30] Lukáš P, Taisei K (2017) Volatility Analysis of Bitcoin Price Time Series. Quantitative Financ and Econ 1: 474–485. doi: 10.3934/QFE.2017.4.474
    [31] Luther WJ (2016) Bitcoin and the Future of Digital Payments. The Independent Rev 20: 397–404.
    [32] Mertzanis C (2018) Complexity, big data and financial stability. Quantitative Financ and Econ 2: 637–660. doi: 10.3934/QFE.2018.3.637
    [33] Murad Z, Sefton M, Starmer C (2016) How do risk attitudes affect measured confidence? J Risk Uncertain 52: 21–46. doi: 10.1007/s11166-016-9231-1
    [34] Nelson DB (1991) Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica 59: 347–370. doi: 10.2307/2938260
    [35] Osterrieder J, Lorenz J (2017) A statistical risk assessment of Bitcoin and its extreme tail behavior. Annals of Financ Econ 12: UNSP 1750003.
    [36] Pilkington M (2013) Bitcoin and Complexity Theory: Some Methodological Implications. Working Paper, University of Burgundy, France.
    [37] Plassaras NA (2013) Regulating Digital Currencies: Bringing Bitcoin within the Reach of the IMF. Chicago J of Int Law 14: 377–407.
    [38] Rockafellar RT, Uryasev S (2002) Conditional value-at-risk for general loss distributions. J of Banking & Financ 26: 1443–1471.
    [39] Sapuric S, Kokkinaki A (2014) Bitcoin Is Volatile! Isn't that Right?. In: Abramowicz W., Kokkinaki A. (eds) Business Information Systems Workshops. BIS 2014. Lecture Notes in Business Information Processing, vol 183. Springer, Cham.
    [40] Sornette D, Cauwels P, Smilyanov G (2018) Can we use volatility to diagnose financial bubbles? lessons from 40 historical bubbles. Quantitative Financ and Econ 2: 1–105.
    [41] Yermack D (2013) Is Bitcoin A Real Currency? An Economic Appraisal. Working paper, National Bureau of Economic Research.
    [42] Yermack D (2015) Bitcoin, innovation, financial instruments, and big data. Handbook of Digital Currency 31–43.
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