Modeling Bitcoin Price Dynamics: Overcoming Kurtosis and Skewness Challenges for Enhanced Predictive Accuracy

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-06-17 DOI:10.1007/s10614-024-10652-y
Mostafa Tamandi
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Abstract

In recent years, the surge of unofficial digital currencies, often referred to as cryptocurrencies, has disrupted traditional financial landscapes. Bitcoin, being the most prominent among them in terms of market adoption and capitalization, presents unique modeling challenges. This study delves into the application of an autoregressive model of order one, incorporating a skew-normal mean-variance mixture of Birnbaum–Saunders innovations, to better capture the dynamic behavior of Bitcoin prices. The model’s robustness to atypical observations and its effectiveness in handling the inherent price volatility associated with Bitcoin make it a promising tool for financial analysis and prediction in this novel asset class.

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比特币价格动态建模:克服峰度和偏度难题,提高预测准确性
近年来,非官方数字货币(通常被称为加密货币)的激增打破了传统的金融格局。比特币作为其中在市场应用和资本化方面最为突出的一种,带来了独特的建模挑战。本研究深入探讨了一阶自回归模型的应用,该模型结合了 Birnbaum-Saunders 创新的倾斜正态均方差混合物,以更好地捕捉比特币价格的动态行为。该模型对非典型观察结果的稳健性及其处理与比特币相关的固有价格波动的有效性,使其成为对这一新型资产类别进行金融分析和预测的有前途的工具。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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