加密货币市场资产价格泡沫的检测及其在风险管理和模型风险度量中的应用

Q2 Economics, Econometrics and Finance International Journal of Economics and Finance Studies Pub Date : 2023-06-15 DOI:10.5539/ijef.v15n7p46
Michael Jacobs, Jr.
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引用次数: 0

摘要

本研究分析了资产价格泡沫对加密货币市场的影响,并考虑了标准的风险管理指标——风险价值(Value-at-Risk)。我们应用局部鞅理论,提出了一个连续时间内资产价格泡沫的模型,并通过恒定方差弹性(“CEV”)过程对资产价值进行了一维和二维随机微分方程(“SDE”)系统的模拟实验,该过程可以检测泡沫行为。在对几种广泛交易的加密货币的实证分析中,我们发现一维SDE系统的估计参数没有显示出泡沫行为的证据。然而,如果我们与股票市场指数一起估计一个二维系统,我们确实发现了泡沫,并将泡沫与非泡沫经济体进行比较,结果表明,资产价格泡沫导致VaR指标大幅膨胀。这一发现对投资组合和风险管理的含义是,加密货币不是作为一种多样化的资产类别,而是可能不仅与其他资产高度相关,而且具有反多样化的特性,从而大大增加了组合这些资产类型的投资组合的下行风险。我们还通过应用相对熵原理,通过忽略与另一个具有代表性的风险资产的关系,来衡量由于错误指定驱动加密货币的过程而产生的模型风险,我们发现,在所有研究的加密货币中,模拟VaR分布之间距离度量的分布几乎都高度向右倾斜并且非常重尾。我们发现,在大多数情况下,该模型存在乘数风险。加密货币的范围大约在2到5之间,估计可以用于建立模型风险储备,作为加密货币风险管理经济资本计算的一部分。
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The Detection of Asset Price Bubbles in the Cryptocurrency Markets with an Application to Risk Management and the Measurement of Model Risk
This study presents an analysis of the impact of asset price bubbles on the markets for cryptocurrencies and con-siders the standard risk management measure Value-at-Risk (“VaR”). We apply the theory of local martingales, present a styled model of asset price bubbles in continuous time and perform a simulation experiment featuring one- and two-dimensional Stochastic Differential Equation (“SDE”) systems for asset value through a Constant Elasticity of Variance (“CEV”) process that can detect bubble behavior. In an empirical analysis across several widely traded cryptocurrencies, we find that the estimated parameters of one-dimensional SDE systems do not show evidence of bubble behavior. However, if we estimate a two-dimensional system jointly with an equity market index, we do detect a bubble, and comparing bubble to non-bubble economies it is shown that asset price bubbles result in materially inflated VaR measures. The implication of this finding for portfolio and risk management is that rather than acting as a diversifying asset class, cryptocurrencies may not only be highly correlated with other assets but have anti-diversification properties that materially inflate the downside risks in portfolios combining these asset types. We also measure the model risk arising from mispecifying the process driving cryptocurrencies by ignoring the relationship to another representative risk asset through applying the principle of relative entropy, where we find that across all cryptocurrencies studied that the distributions of a distance measure between the simulated distributions of VaR are almost all highly skewed to the right and very heavy-tailed. We find that in the majority of cases that the model risk “multipliers” range in about two to five across cryptocurrencies, estimates which could be applied to establish a model risk reserve as part of an economic capital calculation for risk management of cryptocurrencies.
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来源期刊
International Journal of Economics and Finance Studies
International Journal of Economics and Finance Studies Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
3.40
自引率
0.00%
发文量
0
审稿时长
12 weeks
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