Dynamic Cointegration Pairs Trading in Cryptocurrency Markets
Summary
This study evaluates a dynamic cryptocurrency pairs-trading strategy that selects assets and adapts its look-back window using cointegration tests and estimated mean-reversion speed. It applies Engle-Granger, Kapetanios-Snell-Shin, and Johansen tests in different scenarios. An Ornstein-Uhlenbeck process is used to estimate the spread’s half-life, informing both asset selection and window choice.
The authors report that the strategy outperformed naive buy-and-hold on BitMEX and achieved a reasonably low maximum drawdown. They also find that combining multiple coins in the modeled spread improved risk-adjusted profitability, with some coin groups appearing to offer better arbitrage opportunities. The backtest uses minute-binned formation data and simulates trading with best bid and ask quotes and market trades. It accounts for execution only when sufficient quoted size is available, with a period gap after signals. These design choices address market microstructure, but the reported findings remain specific to the tested exchange, assets, scenarios, and historical data.
Key ideas
- The strategy uses multiple cointegration tests to identify cryptocurrency relationships across scenarios.
- An Ornstein-Uhlenbeck half-life estimate guides asset selection and look-back window sizing.
- The study reports better returns than buy-and-hold on BitMEX and a reasonably low maximum drawdown.
- Using multiple coins in the spread is reported to improve risk-adjusted profitability.
- The backtest models quote-based execution and available order size after signal generation.
Tags
Cited by
- Strategies PEP/KO Heteroskedastic Filtered-Spread Re-entry
- Hypotheses PEP/KO Heteroskedastic Filtered-Spread Re-entry
Full text
# Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market # Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market This research aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market, and evaluate its return and risk by applying three different scenarios. We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test, and the Johansen test as cointegration tests in different scenarios. We calibrate the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. By considering the main limitations in the market microstructure, our strategy exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that we implement a numerous collection of cryptocurrency coins to formulate the model's spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy's maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, we simulate the trading signals using best bid/ask quotes and market trades. We exclusively take the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.