全球ETF近10年的交易規模及投資報酬率大幅成長，中國更適時開放滬港通、深港通股票市場，之後，MSCI也積極加入中國成份股，2019年更宣布每季增加成份股比重，可見國際投資機構都看好中國股票市場。本論文將利用多變量GARCH、狹峰、厚尾及偏態T分配的模型，實證2015年以來在台灣上市的跨境滬深300正向2倍及反向1倍槓桿型ETF投資組合及上證50ETF、A50期貨投資組合的避險效益及報酬率。本文以Bauwens and Laurent (2005)所提出之多變量偏態T分配結合多變量GARCH模型，應用於股票收益的投資組合模型，尤其是預測投資組合的風險價值，期以最佳權重分配獲得投資報酬。實證結果顯示多變量GARCH模型融合多變量偏態T分配，確實可捕捉到波動度較大的金融商品，具有顯著的解釋能力，其中現貨ETF與期貨的避險績效最佳，而滬深300槓桿/反向ETF的投資組合報酬相對較高且為正，最後本文融合模擬實務投資方式，每日調整最適投資比重，驗證可獲取的報酬率則更高。

In recent years, the scale and transaction volume of global ETFs have grown substantially. After China opened Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect, MSCI also joined China's constituent stocks. In 2019, MSCI also announced the proportion of cost-increasing stocks per quarter. It is clear to see that the international market is optimistic about the Chinese market. 
This paper will use the multivariate GARCH skew-T distribution model to observe the portfolio of Yuanta Daily CSI 300 Bull 2X ETF , Yuanta Daily CSI 300 Bear -1X ETF and A50 china index ETF , A50 index futures hedging benefits and return rate. Using Bauwens and Laurent (2005) with the multivariate GARCH model, we can analyse stock portfolios to assess their level of risk and predict their renumeration.
The empirical results show that the multivariate GARCH model integrates multivariate skewed -T distribution, which can capture financial products with large volatility and has significant explanatory power. The spot ETF and futures have the best risk-avoiding performance while the CSI 300 leverage ETFs' portfolio return is relatively high. 
Finally, the study integrates the simulation of practical investment methods, adjusts the proportion of the most suitable capital daily and verifies that the renumeration could be higher.

第一章 緒論	1
第一節 研究背景與主題	1
第二節 研究動機與目的	4
第二章 文獻探討與回顧	8
第一節 槓桿/反向型ETF績效研究	8
第二節 最適避險比率	13
第三節 非常態分配的資產價格行為	18
第三章 研究方法	21
第一節 自我迴歸條件異質變異數模型 (ARCH MODEL)	21
第二節 單變量GARCH模型	22
第三節 多變量GARCH模型	23
(一) 第一階段估計：	25
(二) 第二階段估計：	25
第四節 多變量偏態T分配 (MULTIVARIATE SKEW-STUDENT DENSITY)	28
第四章 實證結果	29
第一節 樣本資料	29
第二節 樣本介紹與基本統計量	30
(一)國泰富時中國A50基金(00636)與新交所富時中國A50指數期貨(CN)	30
(二)元大滬深300正向2倍(00637L) 與元大滬深300反向1倍(00638R)	34
第三節 實證結果	40
第四節 實務應用	43
(一) 避險績效	44
(二) 投資組合組成及績效	46
第五章 結論	52

表目錄
【表4-1】國泰富時中國A50基金(ETF)商品規格	31
【表4-2】富時中國A50指數期貨商品簡介	32
【表4-3】富時中國A50 ETF與期貨報酬率基本統計量	34
【表4-4】滬深300單日正向兩倍ETF商品規格	35
【表4-5】滬深300單日反向1倍ETF商品規格	37
【表4-6】滬深300單日正向2倍與反向1倍ETF之基本統計量	39
【表4-7】全樣本期間DCC-GARCH模型在各種分配下之估計結果	41
【表4-8】中國A50 ETF與指數期貨在子樣本期間之估計結果	42
【表4-9】元大滬深300正向2倍與反向1倍ETF在子樣本期間之估計結果	43
【表4-10】 國泰中國A50 ETF與指數期貨避險績效	45
【表4-11】元大滬深300正向2倍與反向1倍ETF之避險績效	46
【表4-12】國泰中國A50 ETF與指數期貨加權平均投資組合績效	47
【表4-13】元大滬深300正向2倍與反向1倍ETF交易方法	48
【表4-14】元大滬深300正向2倍與反向1倍ETF加權平均投資組合績效	49

圖目錄
【圖1-1】全球近10年ETF/ETP 規模趨勢概況	2
【圖1-2】全球ETF成長預估	3
【圖1-3】基金市場成交比重概況	3
【圖1-4】台灣ETF規模變化	5
【圖1-5】台灣ETF總分類	5
【圖1-6】槓桿/反向型ETF概念	7
【圖4-1】中國A50 ETF與期貨走勢圖	33
【圖4-2】槓桿2倍ETF-架構示意圖	36
【圖4-3】反向1倍ETF-架構示意圖	38
【圖4-4】滬深300指數及槓桿/反向ETF走勢圖	39
【圖4-5】全期間投資組合報酬圖	50
【圖4-6】期間I投資組合報酬圖	50
【圖4-7】期間II投資組合報酬圖	51

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