本研究以配對交易為核心，比較共整合法、價差法、二元 Copula與納入隱含波動率資訊的階層式 Copula 交易訊號於投資績效上的差異，並著重在階層式模型是否有助於提升二元模型之績效表現。其中配對組合透過共整合檢定篩選，並以總再投資報酬率作為長期績效衡量指標。實證結果顯示，共整合法、價差法長期持有、低頻交易中產生正報酬。而Copula策略能頻繁捕捉短期資產價格偏離，且階層式模型能改善二元模型績效表現，並在敏感度測試中階層式模型之績效表現皆有改善。;This study adopts pairs trading as its core framework and systematically compares the investment performance of cointegration-based strategies, spread-based strategies, bivariate Copula models, and hierarchical Copula trading signals that incorporate implied volatility information. The analysis focuses in particular on whether the hierarchical Copula structure can effectively enhance the performance of conventional bivariate Copula models. Trading pairs are selected through cointegration tests, and total reinvested return is employed as the primary measure of long-term performance. The empirical results indicate that cointegration and spread-based strategies generate positive returns under long-horizon, low-frequency trading structures. In contrast, Copula-based strategies are more effective in capturing short-term price deviations between assets. Moreover, the hierarchical Copula model consistently improves upon the performance of the bivariate Copula model, with robustness and sensitivity analyses further confirming the superior and more stable performance of the hierarchical framework.
