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研究生:黃才育
研究生(外文):Huang, Tsai Yu
論文名稱:半母數容許區間應用於配對交易策略
論文名稱(外文):Semi-parametric version of tolerance interval strategy for pair trading
指導教授:陳婉淑
指導教授(外文):Cathy W. S. Chen
口試委員:顏盟峯林彩玉
口試委員(外文):Stephane Meng-Feng YenTsai-Yu Lin
口試日期:2018-06-11
學位類別:碩士
校院名稱:逢甲大學
系所名稱:統計學系統計與精算碩士班
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:45
中文關鍵詞:單邊容許區間波動率預測指數加權移動平均模型AI股價差
外文關鍵詞:One-sided tolerance intervalvolatility forecastEWMA modelartificial intelligence stockspread
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配對交易是一個著名的市場中性策略,也是一種利用歷史資料有相似趨勢的兩個資產來獲取利潤的統計套利方法。本篇論文提出將波動預測引入傳統的無母數容許區間,並稱為半母數的容許區間,其中的波動率預測是依據指數加權移動平均模型和變異數異質性模型。本文的目的是運用所提出的半母數的容許區間,在短期的歷史資料上尋找明日進場或出場的交易訊號。本研究挑選美國五檔AI在相同產業的股票,共十組配對投資組合。在樣本內期間,我們利用共整合檢定(Engle-Granger兩步驟法)檢驗標準化價格的共整合配對關係,只有一對的投資組合具有共整合性。在配對交易的策略上,考慮三組回溯樣本(60天, 80天, 100天)以及三個母體比率(70%, 80%, 90%),並在2016下半年至2017下半年,三期六個月的樣本外預測區間,檢驗本文所提出的配對交易策略之獲利能力。在考慮交易成本下,我們觀察到選擇80%的母體比率時,平均報酬率高,標準差低;在三個樣本外期間下,選擇90%的母體比率及100天的回溯樣本,使用配對交易策略的平均報酬率分別為21.872%、41.373%、29.065%。此外,當我們將AI股票與一檔其他行業的股票進行配對時,提出的配對交易策略能降低了購買此股票的風險,並且有優異的平均收益率(0.939%至30.898%)。本研究的實證分析證實,不論是風險厭惡還是風險厭惡較低的投資者,都能利用引入波動預測的配對交易策略中賺取獲利。
Pair trading is a widely known market-neutral strategy that is also a form of statistical arbitrage, which targets profit gains through assets exhibiting a similar trend. In this study we incorporate volatility forecasting based on the EWMA (exponentially weighted moving average) model and the IGARCH (integrated generalized autoregressive conditional heteroscedastic) model into nonparametric one-sided tolerance and call it the semi-parametric version of the tolerance interval. The purpose of this study is to propose a semi-parametric version of a tolerance interval to find trading signals based on a short history of data. We select five AI (artificial intelligence) stocks in U.S. and obtain ten pairs. We utilize a cointegration test, the Engle-Granger two-step method, to test for the cointegrating pairwise relationship of standardized prices during the in-sample period. All pairs are not cointegrated except for one pair. We focus on three “look-back” sample sizes (60, 80, and 100 days) and three p-contents (70, 80, and 90%) for pair trading. We evaluate their performances from the second half of 2016 until the second half of 2017, which are three out-of-sample periods within a six-month time frame. We observe high average profit returns with low standard deviation when we choose 80% p-content with the transaction cost; the average pair-trading returns based on 90% p-content and 100-day “look-back” sample size are 21.872%, 41.373%, and 29.065% for the out-of-sample period. Moreover, when we match AI stocks with a stock of another industrial sector, we confirm the proposed pair-trading strategy thus reduces the risk on just purchasing this stock alone, and have excellent average returns (0.939% to 30.898%). Finally, both risk-averse investors and less risk-averse investors can gain the profits with pair-trading strategy that incorporate volatility forecasting into a nonparametric one-sided tolerance limit.
1 Introduction 5
2 Semi-parametric version of the tolerance interval. 9
2.1 Nonparametric tolerance intervals . . . . . . . . . . . . . . 9
2.2 Volatility forecasting . . . . . . . . . . . . . . . . . . . . 12
2.3 Pair-trading procedure . . . . . . . . . . . . . . . . . . . . 14
3 Data 18
4 Analytic results 22
5 Conclusions and future research 42
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