Volatility estimation through stochastic processes: Evidence from cryptocurrencies

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance Pub Date : 2024-11-21 DOI:10.1016/j.najef.2024.102320

Murad Harasheh , Ahmed Bouteska

引用次数: 0

Abstract

We apply stochastic volatility modeling enriched with leverage and an asymmetrically heavy-tailed distribution to analyze the returns of Bitcoin and Ethereum. Our methodology leverages the generalized hyperbolic skew Student’s t-distribution (GH-ASV-skw-st) framework, as proposed by Nakajima and Omori (2012), employing a Bayesian Markov chain Monte Carlo (MCMC) sampling technique for effectiveness evaluation. The GH-ASV-skw-st model is demonstrated to adeptly capture the stochastic volatility patterns present in the returns of cryptocurrencies. After validation with several diagnostics and robustness checks, we illustrate the model’s suitability for high-volatility series by capturing asymmetry, leverage effects, and tail risk. Our findings indicate that the model fits the data more precisely than traditional models and provides a more reliable foundation for risk measures essential to portfolio management, such as Value at Risk (VaR) and Expected Shortfall (ES).

查看原文本刊更多论文

通过随机过程估算波动率：加密货币的证据

我们采用富含杠杆和非对称重尾分布的随机波动率模型来分析比特币和以太坊的收益。我们的方法利用了 Nakajima 和 Omori（2012 年）提出的广义双曲偏斜学生 t 分布（GH-ASV-skw-st）框架，采用贝叶斯马尔科夫链蒙特卡罗（MCMC）抽样技术进行有效性评估。结果表明，GH-ASV-skw-st 模型能很好地捕捉加密货币收益中存在的随机波动模式。经过若干诊断和稳健性检查验证后，我们通过捕捉非对称性、杠杆效应和尾部风险说明了该模型对高波动性系列的适用性。我们的研究结果表明，该模型比传统模型更精确地拟合数据，并为风险价值（VaR）和预期缺口（ES）等投资组合管理所必需的风险度量提供了更可靠的基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

North American Journal of Economics and Finance Multiple-

CiteScore

7.30

自引率

8.30%

发文量

168

期刊介绍： The focus of the North-American Journal of Economics and Finance is on the economics of integration of goods, services, financial markets, at both regional and global levels with the role of economic policy in that process playing an important role. Both theoretical and empirical papers are welcome. Empirical and policy-related papers that rely on data and the experiences of countries outside North America are also welcome. Papers should offer concrete lessons about the ongoing process of globalization, or policy implications about how governments, domestic or international institutions, can improve the coordination of their activities. Empirical analysis should be capable of replication. Authors of accepted papers will be encouraged to supply data and computer programs.