Abstract
Cryptocurrencies (CCs) have emerged as a strategic asset class for institutional investors, offering unique portfolio diversification opportunities. However, their high volatility and tail risk distinguish them from traditional assets. This study segments the CC market into core (assets with similar statistical properties) and satellite (dissimilar residual assets) using modern pattern recognition techniques. By analyzing returns, standard deviations, and tail risk parameters, we identify a methodology for creating tracking-error-optimized portfolios.
Key Findings:
- Segmentation of CCs into core/satellite components enables efficient portfolio construction.
- Bitcoin narrowly qualifies as part of the core but lacks centrality, challenging its perceived dominance.
- Dynamic Time Warping (DTW) algorithms effectively cluster CCs based on risk-return profiles.
Introduction
Cryptocurrencies have transitioned from niche investments to institutional portfolio staples. Their high volatility and non-normal return distributions necessitate tailored asset allocation strategies. Professional managers often adopt a core-satellite approach:
- Core: Broadly diversified assets with homogeneous statistical properties.
- Satellite: High-risk, high-reward assets for alpha generation.
This paper applies DTW algorithms—originally developed for speech recognition—to classify 27 CCs (2014–2019 data) based on three metrics:
- Average weekly returns (⟨r⟩).
- Standard deviation (s).
- Tail parameter (α) from Stable Distributions.
Methodology
Data Sources
- Price Data: CoinMarketCap (27 CCs, EUR-denominated weekly returns).
- Exclusions: CCs with >5 consecutive missing data points.
Dynamic Time Warping (DTW)
DTW measures dissimilarity between time-series sequences by "warping" time axes to align patterns. We compute DTW distances using:
- Manhattan Distance
- Euclidean Distance
- Squared Euclidean Distance
Key Steps:
- Calculate pairwise DTW distances for each CC.
- Normalize distances ([0, 1] scale).
- Model the distance matrix using Radial Basis Functions (RBFs) to smooth peaks.
- Define a threshold (d_bound) to separate core/satellite CCs.
Results
Core vs. Satellite Identification
| Metric | Core CCs | Satellite CCs |
|------------------|-------------|------------------|
| Squared Euclidean | 19 | 8 |
| Euclidean | 18 | 9 |
| Manhattan | 17 | 10 |
Intersection of all metrics:
- 19 CCs (e.g., Primecoin, Zetacoin) form the core.
- 8 CCs (e.g., Freicoin, Diamond) are satellites.
👉 Explore real-time CC market data
Bitcoin’s Role
- Bitcoin resides near the core boundary, indicating moderate similarity to other core CCs.
- Contrary to popular belief, Bitcoin does not dominate the core, suggesting diversification benefits from altcoins.
Robustness Checks
Correlation Analysis
- Core CCs exhibit higher intra-group correlations (mean = 0.24) vs. satellites (mean = 0.18).
- Welch’s t-test confirms statistical significance (p < 0.01).
Practical Implications
- Portfolio Construction: Pair 5–10 liquid core CCs with selective satellites for alpha.
- Tracking Error: Monitor deviations from core benchmarks.
FAQs
1. Why use DTW instead of correlation analysis?
DTW captures dynamic patterns in multi-parameter time series, whereas correlations only measure linear relationships.
2. How does Bitcoin’s core status affect investors?
Its peripheral position suggests that altcoins (e.g., Ethereum, Litecoin) may offer comparable risk-return profiles.
3. What are the limitations?
- Liquidity risks for small-cap satellites.
- Short historical data (pre-2020).
Conclusion
Our DTW-based framework successfully segments the CC market into statistically coherent clusters. Bitcoin’s non-central role in the core challenges conventional narratives, while the methodology offers scalable applications for other asset classes.
👉 Dive deeper into crypto portfolio strategies
Future Research:
- Extend analysis to DeFi tokens.
- Incorporate liquidity metrics (e.g., LIBRO approach).
Methodology inspired by Trimborn & Härdle (2018) and Majoros & Zempléni (2018). Data sourced from CoinMarketCap.