本研究探討加密貨幣的配對交易，研究加密貨幣市值前三大的幣種，比特幣、以太幣以及幣安幣，並且分別依據 Sklar (1959)提出的關聯結構函數以及 Engle - Granger (1987)提出的二階段共整合檢定來構建配對交易，並且比較兩種模型捕捉出來訊號的準確程度，由於資產報酬率分配的不對稱性，透過雙變量關聯結構函數構建的交易策略考慮了左尾或右尾依賴關係較顯著的關聯結構函數，確保交易訊號能夠更準確的被捕捉，並且不用對資產進行常態假設 ; 而二階段共整合檢定先確定兩幣種長期下存在共整合關係，並且在價差擴大時進行交易。實證結果表明，三幣種在長期下皆存在共整合關係，而二階段共整合檢定構建的交易策略進場機會較多，但平均報酬率較低，且 Sharpe ratio 也較低。
而透過雙變量關聯結構函數構建的交易策略交易機會較少，但整體報酬率較 高，並且 Sharpe ratio 也較高，顯示雙變量關聯結構函數構建的交易策略能更 準確的辨別交易機會，所捕捉的交易訊號更精確，有更好的獲利能力。

The purpose of this paper is to investigate pair trading in cryptocurrencies, focusing on the top three cryptocurrencies by market capitalization: Bitcoin, Ethereum, and Binance Coin. The study constructs pair trading strategies using two different models: the bivariate copula proposed by Sklar (1959) and the two-step cointegration analysis proposed by Engle and Granger (1987). To evaluate the accuracy of trading signals the model captured, we compare the profitability of two approaches.
In pair trading strategies, the core conception is the correlation between two assets. The traditional approaches: distance approach, cointegration approach, and the stochastic spread approach are based on the linear hypothesis, which is the Achilles’ heel of traditional pair trading strategies. To overcome this restriction, this paper employs the copula, which can totally describe the dependence between variables and model joint distributions. Due to the asymmetries of financial returns, the trading strategy that considers the copula can seize the signal more effective.
The empirical results suggest that copula approach is more profitable, although the number of trading opportunities is lower, the mean return and total return is higher, and the standard deviation is lower. The copula approach is completely shown to have more probability as a pair trading model to construct a strategy.

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