货币政策是加密货币的驱动力吗?来自结构性中断 GARCH-MIDAS 方法的证据

IF 1.1 Q3 ECONOMICS Econometrics Pub Date : 2024-01-05 DOI:10.3390/econometrics12010002
Md Samsul Alam, Alessandra Amendola, Vincenzo Candila, Shahram Dehghan Jabarabadi
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引用次数: 0

摘要

比特币作为一种分布式点对点数字现金于 2008 年问世,并于 2010 年首次记录了真实交易,发挥了交换媒介的功能,通过提供一种去中心化、点对点的传统货币体系替代品,改变了金融格局。本研究调查了加密货币与货币政策之间错综复杂的关系,尤其关注其长期波动动态。我们通过采用 SB-GARCH-MIDAS(结构性断裂混合数据采样)来增强 GARCH-MIDAS(混合数据采样),分析三种著名加密货币(比特币、Binance Coin 和 XRP)的日收益率以及美国和南非的月度货币政策数据,研究货币政策中是否存在潜在的结构性断裂,从而为我们提供两个 GARCH-MIDAS 模型。截至 2022 年 6 月 30 日,所有样本的最新数据观测值均已注明,但必须承认的是,由于加密货币数据获取途径的不同,数据样本的时间范围也不尽相同。我们的研究结合了模型置信集(MCS)程序,并使用各种指标评估模型性能,包括 AIC、BIC、MSE 和 QLIKE,并辅以全面的残差诊断。值得注意的是,我们的分析表明,SB-GARCH-MIDAS 模型在预测加密货币波动性方面优于其他模型。此外,我们还发现,与较年轻的加密货币相比,较老加密货币的长期波动性对外生变量的结构性断裂很敏感。我们的研究揭示了加密货币领域内由技术特征和时间因素形成的多样化,并提供了实用的见解,强调了在评估加密货币波动性时纳入货币政策的重要性。我们研究的意义延伸到考虑动态因素的投资组合管理,为投资者和决策者提供了宝贵的见解,强调了同时考虑加密货币类型和东道国经济背景的重要性。
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Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach
The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.
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来源期刊
Econometrics
Econometrics Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
2.40
自引率
20.00%
发文量
30
审稿时长
11 weeks
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