Introduction
The rapid development of the internet has transformed the financial landscape, with cryptocurrencies emerging as a disruptive force. These digital assets, built on cryptographic principles, operate independently of government oversight and traditional financial institutions. Despite their short history—spanning just over a decade—cryptocurrencies like Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Litecoin (LTC), and Ripple (XRP) have exhibited extreme volatility, attracting both investors and speculators.
During the COVID-19 pandemic, cryptocurrencies like Bitcoin experienced dramatic price swings, plummeting before rebounding to surpass previous all-time highs. This volatility has intensified interest in understanding the causal relationships between these assets.
Literature Review
Academic perspectives on cryptocurrencies remain divided. While some scholars highlight risks like price bubbles and speculative trading (Wu et al., 2020), others argue that cryptocurrencies could drive financial innovation if properly regulated (Zhang, 2021). Research on cryptocurrency causality is sparse, but key studies include:
- Lu et al. (2019): Applied Granger causality and Liang’s information flow analysis to identify short-term and long-term causal links between cryptocurrencies and emerging stock markets.
- Rehman & Apergis (2020): Used quantile causality methods to demonstrate significant causality from cryptocurrencies to commodity futures.
Methodology
Data Sources & Descriptive Statistics
Daily closing prices (2018–2020) for BTC, ETH, ADA, LTC, and XRP were sourced from Investing.com. Returns were calculated as:
[ r_t = \ln(P_t) - \ln(P_{t-1}) ]
| Statistic | BTC | ETH | ADA | LTC | XRP |
|----------------------|--------|--------|--------|--------|--------|
| Mean Return | 0.002 | 0.003 | 0.001 | 0.001 | 0.000 |
| Standard Dev. | 0.042 | 0.050 | 0.055 | 0.048 | 0.053 |
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Granger Causality Test
Granger causality assesses whether lagged values of one variable predict another. Key findings:
- BTC ↔ ADA: Bidirectional causality.
- XRP → BTC: Unidirectional causality.
However, Granger tests lack quantitative measures of causal strength.
Liang’s Information Flow Analysis
This physics-based method quantifies causality via information flow rates (T). Results:
| Pair | T (×10⁻³) | Direction |
|------------|-------------|-----------|
| BTC → ETH | 8.2 | Positive |
| ETH → XRP | -12.1 | Negative |
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Key Findings
- BTC Dominance: BTC significantly influences ETH, ADA, and LTC returns.
- ETH’s Role: ETH drives ADA and LTC returns but is less influential on XRP.
- Data Sensitivity: Causality weakened when analyzed over shorter periods (729 vs. 1096 days).
FAQ
Q: Can correlation imply causation in cryptocurrencies?
A: No—Pearson correlation only measures linear relationships. Causality requires methods like Granger or Liang’s tests.
Q: Why does BTC influence other cryptocurrencies?
A: As the largest crypto by market cap, BTC often sets market sentiment and liquidity trends.
Q: How reliable are causality tests with small datasets?
A: Larger datasets (e.g., 3+ years) improve reliability by capturing long-term dynamics.
Conclusion
While correlation among cryptocurrencies is well-documented, this study quantifies causal linkages using Liang’s method. Investors should monitor BTC and ETH trends to anticipate broader market movements. Regulatory clarity remains critical to mitigate risks in this volatile asset class.