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Volatility Spillovers Between Bitcoin and Traditional Financial Indicators : A VAR-Based Analysis

원문정보

Yuhyeon Bak, Giseob Yu

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초록

영어

This study analyzed the interactions and volatility spillover mechanisms between Bitcoin prices and major economic indicators, including the Dow Jones Index, S&P 500 Index, and gold prices, using a VAR model, Granger causality tests, and Impulse Response Functions (IRFs). The results reveal that the Dow Jones and S&P 500 indices significantly influence Bitcoin prices, with the Dow Jones positively impacting Bitcoin in the short term, while the S&P 500 exerts a negative influence. In contrast, gold prices exhibit a relatively weak interaction with Bitcoin, suggesting limited direct volatility spillover between these two assets. Academically, this research contributes by providing empirical evidence of Bitcoin’s dynamic relationships with traditional financial markets, moving beyond its characterization as an independent digital asset. The use of IRFs to differentiate short- and long-term interactions enhances the understanding of volatility spillover mechanisms in financial markets. Additionally, the findings highlight Bitcoin’s growing integration with traditional markets while maintaining independence, particularly in its distinct divergence from gold as an asset class. Practically, the study identifies the Dow Jones and S&P 500 indices as key predictors of Bitcoin price volatility, offering investors actionable insights for portfolio diversification and trading strategies. Furthermore, the weak relationship between Bitcoin and gold suggests that Bitcoin can serve as an independent alternative investment, providing opportunities for risk management and market diversification. These insights emphasize Bitcoin’s evolving role in the global financial ecosystem and its growing relevance as a strategic asset.

목차

Abstract
1. Introduction
2. Theoretical Background
2.1 Bitcoin Market Research
3. Research Method
3.1 Data Description and Collection
3.2 Overview of the VAR Model
3.3 Log Returns and Data Processing
3.4 Granger Causality and Impulse Response Function
4. Research Results
4.1 Data Collection
4.2 VAR Analysis Results
4.3 Results of Granger Causality and Impulse Response Function Analysis
5. Conclusion
5.1 Academic Implications
5.2 Practical Implication
5.3 Limitations and Future Research
References

저자정보

  • Yuhyeon Bak Assistant Professor, Department of Global Economics, Sun Moon University
  • Giseob Yu Assistant Professor, Division of Interdisciplinary Studies, Sun Moon University

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자료제공 : 네이버학술정보

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