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High frequency multiscale relationships among major cryptocurrencies: portfolio management implications
Walid Mensi1, Mobeen Ur Rehman2, Muhammad Shafiullah3
1Department of Economics and Finance, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman.
Financial Innovation
|January 13, 2022
Summary
This study reveals significant multiscale relationships and dual causality among cryptocurrencies like Bitcoin and Ethereum using nonlinear Granger causality and wavelet analysis. Findings aid in cryptocurrency portfolio risk assessment and hedging strategies.
Area of Science:
- Quantitative Finance
- Computational Economics
- Cryptocurrency Market Analysis
Background:
- Understanding interdependencies in cryptocurrency markets is crucial for investors.
- High-frequency trading data offers insights into short-term market dynamics.
- Existing research often overlooks nonlinear and multiscale relationships.
Purpose of the Study:
- To investigate high-frequency multiscale relationships and causality among major cryptocurrencies.
- To assess the applicability of nonlinear Granger causality and wavelet correlation methods.
- To provide evidence for improved portfolio risk assessment and hedging strategies.
Main Methods:
- Application of nonlinear Granger causality tests.
- Utilizing rolling window wavelet correlation (RWCC) on 15-minute cryptocurrency data.
- Analysis of Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin.
Main Results:
- Empirical RWCC results show predominantly positive co-movements and long-term memory.
- Strong correlations observed particularly between Bitcoin, Ethereum, and Monero.
- Nonlinear Granger causality tests indicate dual causation between most cryptocurrency pairs.
Conclusions:
- Significant nonlinear and multiscale interdependencies exist among cryptocurrencies.
- Findings support the use of advanced methods for analyzing cryptocurrency markets.
- Evidence presented can enhance cryptocurrency portfolio management and hedging effectiveness.
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