A Multifractal Cross-Correlation Framework for Cryptocurrency Pairs Trading with CAPM Filtering

The cryptocurrency market is characterized by high volatility and strong systematic co-movements among assets, posing significant challenges for conventional statistical arbitrage strategies. Effective pair trading in this environment requires the identification of return variation that is unexplained by common market factors. This study proposes a hybrid framework that combines CAPM-based market-model filtering with multifractal detrended cross-correlation analysis (MFDCCA) for cryptocurrency pair trading. A single-index market-model regression is first employed to remove the linear market component, and the resulting market-model-filtered series are then analyzed using MFDCCA to characterize multifractal cross-correlation structures, which are subsequently used for pair selection and signal generation. The results show that the proposed MFDCCA-based approach effectively captures nonlinear and scale-dependent relationships in the market-model-filtered series, leading to improved risk-adjusted trading performance under the evaluated experimental settings. Compared with conventional dependence measures such as Pearson correlation, cointegration, and standard DCCA, the proposed framework achieves favorable risk-adjusted returns under the evaluated experimental settings. These findings highlight the effectiveness of combining market-model filtering with multifractal analysis for statistical arbitrage in cryptocurrency markets and demonstrate its potential to support portfolio management and risk control.