加密货币市场风险价值和预期缺口的贝叶斯预测:一个非线性半参数框架

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Cathy W. S. Chen, Po-Hui Chen, Ying-Lin Hsu
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引用次数: 0

摘要

加密货币表现出高波动性,强调了准确衡量其市场尾部风险的重要性。这项研究将阈值转换机制整合到Taylor的ES-CAViaR模型中,揭示了不对称和跳跃现象等特征。这些增强功能有效地捕获了加密货币的各种尾部风险,同时能够同时预测风险价值(VaR)和预期缺口(ES)。提出的模型包含两种类型的函数来解决VaR和ES的关系,并选择使用滚动标准偏差作为短期波动率代理作为回归量。我们在贝叶斯框架内估计参数和预测尾部风险。以市值最大的两种加密货币比特币和以太坊为例,我们使用滚动窗口方法评估了四年样本外期的一步预测性能。八个竞争模型的回测和五个评分函数的比较结果支持这样的结论:具有阈值机制的模型比其他风险模型更准确地捕获了加密货币的尾部风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Forecasting of Value-at-Risk and Expected Shortfall in Cryptocurrency Markets: A Nonlinear Semi-Parametric Framework

Cryptocurrencies exhibit high volatility, emphasizing the importance of accurately measuring tail risk in their markets. This research incorporates a threshold-switching mechanism into Taylor's ES-CAViaR models that unveil features such as asymmetry and jump phenomena. These enhancements effectively capture the diverse tail risks of cryptocurrencies while enabling the simultaneous forecasting of both Value-at-Risk (VaR) and Expected Shortfall (ES). The proposed models incorporate two types of functions to address the VaR and ES nexus with the option to use the rolling standard deviation of returns as a short-term volatility proxy as a regressor. We estimate the parameters and forecast tail risk within a Bayesian framework. Taking the two largest cryptocurrencies by market capitalization, Bitcoin and Ethereum, we assess the one-step-ahead forecasting performance over a four-year out-of-sample period using a rolling window approach. The comparative results from backtests and five scoring functions among eight competing models support the conclusion that models with a threshold mechanism capture the tail risk of cryptocurrencies more accurately than other risk models.

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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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