Paper: arXiv 2305.06961

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]