Copula-Based Trading of Cointegrated Cryptocurrency Pairs
ArXiv ID: 2305.06961 “View on arXiv”
Authors: Unknown
Abstract
This research introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs, the study employs linear and non-linear cointegration tests along with a correlation coefficient measure and fits different copula families to generate trading signals formulated from a reference asset for analyzing the mispricing index. The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms buy-and-hold trading strategies in terms of both profitability and risk-adjusted returns.
Keywords: Pairs Trading, Copulas, Cointegration, Cryptocurrencies, Statistical Arbitrage, Cryptocurrencies
Complexity vs Empirical Score
- Math Complexity: 7.5/10
- Empirical Rigor: 8.0/10
- Quadrant: Holy Grail
- Why: The paper employs advanced mathematical concepts like copula theory, non-linear cointegration tests, and conditional probability derivatives, indicating high mathematical complexity. It also demonstrates high empirical rigor by conducting back-tests on historical cryptocurrency data, assessing returns and risks, and comparing performance against buy-and-hold strategies.
flowchart TD
A["Research Goal<br>Develop a copula-based pairs trading strategy<br>for cointegrated cryptocurrency pairs"] --> B["Data Collection & Preparation"]
B --> C["Pair Selection"]
C --> D["Model Fitting<br>Copula Family Selection"]
D --> E["Signal Generation<br>Mispricing Index & Trading Triggers"]
E --> F["Back-testing & Evaluation<br>Performance & Risk Metrics"]
F --> G["Key Outcomes<br>Strategy outperforms Buy-and-Hold<br>in profitability & risk-adjusted returns"]
B["Data Preparation"] --> B1["Historical Crypto Price Data"]
C["Pair Selection"] --> C1["Linear/Non-linear<br>Cointegration Tests"]
C --> C2["Correlation Coefficient"]