Financial markets frequently exhibit transient price divergences between economically linked assets, yet traditional pair trading strategies struggle to adapt to structural breaks and complex dependencies, limiting their robustness in dynamic regimes. This paper addresses these challenges by developing a novel framework that integrates sparse synthetic control with copula-based dependence modeling to enhance adaptability and risk management. Economically, our approach responds to the need for strategies that systematically identify latent linkages while mitigating overfitting in high-dimensional asset pools. The sparse synthetic control methodology constructs a parsimonious synthetic asset via an ℓ1-regularized least squares optimization, automatically selecting a sparse subset of influential assets from a broad donor pool while maintaining interpretability and computational efficiency. Empirical application to S&P 500 constituents demonstrates that relatively few donor assets (27 in our case) suffice to create effective synthetic controls. By embedding this within a copula-based dependence framework, we capture non-linear and tail dependencies between target and synthetic assets. Our analysis reveals that elliptical copulas, particularly the Student's t specification, provide the best fit for modeling return dependencies, highlighting the importance of accommodating tail dependence in pair trading strategies. Trading signals, grounded in the relative mispricing between these assets, employ a cumulative index that resets after position closures to isolate episodic opportunities, with disciplined entry rules requiring concurrent misalignment signals to filter noise. The empirical results demonstrate the superior performance of our integrated approach across diverse market conditions. The best-performing copula specification, N14, achieves an annualized return of 17.26% and a Sharpe ratio of 3.97, with moderate volatility (4.35%). Notably, all tested copula specifications deliver positive risk-adjusted returns, underscoring the robustness of our framework. Future research directions include exploring time-varying copulas, extending the framework to multiple target assets, and incorporating transaction costs for practical implementation.