從 2022 年 11 月 FTX 交易所宣布破產，導致市值下降至 25兆台幣，到 2024 年 2 月市值回升至 62 兆台幣，加密貨幣市場經歷了波折。隨著各國監管政策趨嚴，如日本對穩定幣的管制，英國修改金融服務法以監管穩定幣，以及台灣金管會成為加密貨幣主要監管機構等，為市場帶來穩定，此外，SEC 通過 11 支比特幣 ETF 上市，標誌著加密貨幣向傳統金融商品靠攏。為了更有效地進行投資，投資者開始將傳統金融交易策略應用於加密貨幣市場，特別是配對交易策略。研究顯示，加密貨幣價格預測和配對交易是主要研究方向。文獻中使用的統計模型包括配對距離模型、共整合模型、均值迴歸模型和相關性分析。本研究以配對距離法和共整合法為例，探討加密貨幣市場的配對交易策略。通過對長時間跨度的資料進行分析，發現即使在高波動性的市場環境下，這些交易策略仍然能夠穩定獲利，並且共整合法的效果略優於價格距離法，這達 64%。此外，本研究的特點還包括選擇長時間週期且以日為單位的交易頻率，與常見的日內高頻交易不同。相關文獻雖然少數使用價格距離法進行交易策略，通常會先進行單根檢定測試。然而，鮮少文獻將通過單根檢定測試的配對商品實際應用於價格距離法，並和共整合法交易的績效做比較。本研究的發現顯示，即使兩個配對商品的時間序列並非定態關係，價格距離法仍能帶來正向交易報酬。

From November 2022 when FTX exchange declared bankruptcy, leading to a drop in market capitalization to 25 trillion New Taiwan Dollars, to February 2024 when the market capitalization rebounded to 62 trillion New Taiwan Dollars, the cryptocurrency market underwent turmoil. With stricter regulatory policies worldwide, such as Japan's regulations on stablecoins, the UK's amendments to financial services laws to regulate stablecoins, and Taiwan's Financial Supervisory Commission becoming the primary regulatory authority for cryptocurrencies, stability returned to the market. Additionally, the approval of 11 Bitcoin ETFs by the SEC signaled cryptocurrency's convergence with traditional financial assets. To enhance investment efficiency, investors began applying traditional financial trading strategies to the cryptocurrency market, particularly pair trading strategies. Research indicates that cryptocurrency price prediction and pair trading are the primary research directions. Statistical models used in the literature include pair distance models, cointegration models, mean reversion models, and correlation analysis. This study focuses on pair distance and cointegration methods, exploring pair trading strategies in the cryptocurrency market. Analysis of long-term data reveals that these trading strategies can still yield stable profits even in highly volatile market conditions, with cointegration outperforming pair distance methods. Moreover, the study's distinctiveness lies in its selection of longer timeframes, with daily trading frequencies, as opposed to the common intraday high-frequency trading. Although few studies employ pair distance methods, typically preceded by unit root tests, seldom do they apply the pairs identified through these tests to the pair distance method, and compare different pairs or statistical models. The findings of this study demonstrate that even when the time series of two paired assets are non-stationary, pair distance methods can still generate positive trading returns.

目錄
1.前言…………………………………………………………………1
2.文獻回顧………………………………………………………4
3.研究方法……………………………………………………10
4.實證結果……………………………………………………15
5.結論………………………………………………………………30
附錄……………………………………………………………………31
參考文獻…………………………………………………………44
 

英文參考文獻

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中文參考文獻
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