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系統識別號 U0002-1807201300320700
中文論文名稱 商品ETF、期貨與現貨巿場之動態關聯性研究
英文論文名稱 The Analysis for Dynamic Relationship among Commodity ETF、Futures and Spot Markets
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士在職專班
系所名稱(英) Department of Banking and Finance
學年度 101
學期 2
出版年 102
研究生中文姓名 廖淑華
研究生英文姓名 Shu-Hua Liao
學號 700530297
學位類別 碩士
語文別 中文
口試日期 2013-06-21
論文頁數 81頁
口試委員 指導教授-邱建良
指導教授-蘇欣玫
委員-俞海琴
委員-邱建良
委員-莊益源
中文關鍵字 商品市場  動態關聯性  價格發現  VAR模型  雙變量EGARCH-DCC模型 
英文關鍵字 commodity market  dynamic relationship  price discovery  VAR model  bivariate EGARCH-DCC model 
學科別分類
中文摘要 近年來世界各國均致力開發指數化之新金融商品,自1993年發行第一檔ETF到2012年第一季,經歷了19年,全球的ETF由3檔成長至3,169檔、總資產規模由8億美元躍升至1.54兆美元,共成長近2,000倍。而其中實體商品的保值與增值效益,更結合ETF的證券化方式,提供給投資人投資門檻低、流動性高與風險分散效果的相對優勢。
本文以美國四類商品市場為研究對象,包括黃金、白銀、原油與天然氣,分析其ETF、期貨與現貨巿場三者之動態關聯性與價格發現功能的差異。在VAR模型的估計結果上,可發現到投資人對於金屬類與天然氣商品的價格發現功能,主要是以期貨市場來預測未來現貨市場,而利用現貨市場來觀察未來的ETF市場。且當市場間的關聯性愈密切時,將有助於投資人在其一市場的獲利性。其次,進一步考量異質變異數且動態條件相關係數下,利用雙變量EGARCH-DCC模型進行估計,有別於VAR模型之結果,顯示出投資人對於商品市場三者間皆有同質預期,並發現出金屬類商品的現貨市場與能源類商品的期貨(或ETF)市場資訊反應效率較佳。而進一步計算價格發現貢獻度(CFW),發現黃金與白銀等金屬類商品,其期貨的價格發現貢獻度較高;而原油與天然氣等能源類商品,則為ETF的貢獻度較高。
英文摘要 In recent years, many countries in the world have long been involved in developing new financial products related with index. Exchange-traded fund (ETF) as we know them were launch in 1993, after 19 years, ETF were rapidly bolving from “three” issues to “3,169” issues, with total assets rising from USD 80 million to USD 15.4 billion; the growth was around over 2,000 times.
Physical commodity linked with securitize of ETF is concerned with maintainenace and creation of wealth which provides the investors with the advantages of low threshold high liquidity and diverse risks.
This study was data from American commodity market such as gold, silver, crude oil and gas to analyze the difference of dynamic relationship and price discovery among commodity ETF, furthers and spot markets.
The findings of this study are as follows:
1. The experimental results obtained from VAR model are the investors obscene future ETF market by the spot market and also forecast future spot market by future market regarding the price discovery of the metals and nature gas moreover, the close relationship among the markets benefits profitability of the investors.
2. To use bivariate EGARCH-DCC model to analyze the dynamic correlation coefficient of heteroskedasticity:the results different from those of VAR model showed that the investors have the homogeneous expectation among the three commodity markets and also the spot market of mental commodity and future (or ETF) market of energy-related market have better response for market information. Furthermore, to calculate the contribution of price discovery, the results showed that the contribution of price discovery for the futures of gold and silver. The futures of metal and silver commodity had higher contribution of price discovery; nevertheless, the ETF of crude oil and nature gas energy-related had higher contribution of price discovery.
論文目次 目 錄
中文摘要......................................................................................................................Ⅰ
英文摘要.....................................................................................................................Ⅲ
目錄.......................................................................................................................Ⅴ
表目錄......................................................................................................................Ⅶ
圖目錄......................................................................................................................Ⅷ
第一章 緒論............................................................................................................ 1
第一節 研究背景與動機 ........................................................................................ 1
第二節 研究目的 .................................................................................................... 4
第三節 研究架構 .................................................................................................... 5
第二章 商品ETF介紹與文獻探討 ............................................................................ 7
第一節 商品ETF介紹(以美國商品ETF巿場為例)............................................ 7
第二節 商品現貨巿場之相關文獻 ...................................................................... 12
第三節 商品ETF巿場之相關文獻 ..................................................................... 14
第四節 ETF、現貨與期貨巿場之相關文獻 ....................................................... 15
第三章 研究方法 ..................................................................................................... 27
第一節 研究對象及研究期間 .............................................................................. 27
第二節 單根檢定 .................................................................................................. 31
第三節 ARCH效果檢定 ...................................................................................... 35
VI
第四節 實證模型 ................................................................................................. 37
第四章 實證結果與分析 ......................................................................................... 50
第一節 資料處理 .................................................................................................. 50
第二節 基本統計量分析 ...................................................................................... 51
第三節 單根檢定結果 .......................................................................................... 55
第四節 實證結果分析 .......................................................................................... 58
第五章 結論與建議 ................................................................................................. 74
第一節 結論 .......................................................................................................... 74
第二節 未來研究建議 .......................................................................................... 75
參考文獻...................................................................................................................... 76

表 目 錄
【表3-1-1】貴重金屬與能源類ETF之契約....………..……………………………28
【表3-1-2】貴重金屬與能源類商品….………………..……………………………30
【表4-2-1】各類商品指數之基本統計量……….………..…………………………52
【表4-3-1】各類商品指數原始序列之單根檢定結果.……..………………………56
【表4-3-2】各類商品指數一階差分序列之單根檢定結果…...……………………57
【表4-4-1】黃金商品的VAR模型實證結果表…………………………………….60
【表4-4-2】白銀商品的VAR模型實證結果表……………………………………61
【表4-4-3】原油商品的VAR模型實證結果表……………………………………62
【表4-4-4】天然氣商品的VAR模型實證結果表………………………………….63
【表4-4-5】黃金商品的雙變量GARCH-DCC模型實證結果表………………….66
【表4-4-6】白銀商品的雙變量GARCH-DCC模型實證結果表………………….68
【表4-4-7】原油商品的雙變量GARCH-DCC模型實證結果表………………….70
【表4-4-8】天然氣商品的雙變量GARCH-DCC模型實證結果表……………….72

圖 目 錄
【圖1-3-1】本論文研究架構圖..……….……………………………………………6
【圖2-1-1】美國商品ETF市場發展趨勢..…………………………………………8
【圖2-1-2】商品ETF數量與規模在整個ETF市場中的比例…...............…………8
【圖2-1-3】商品ETF數量結構比………………………………...............…………9
【圖2-1-4】商品ETF金額結構比…...………………………………………………10
【圖4-2-1】黃金近月期貨與兩檔黃金ETF(GLD與IAU)的價格走勢圖..............53
【圖4-2-2】白銀近月期貨與兩檔黃金 ETF(SLV與DBS)的價格走勢圖…...........53
【圖4-2-3】原油近月期貨與兩檔原油ETF(USO與UHN)的價格走勢圖…..........54
【圖4-2-4】天然氣近月期貨與ETF(UNG)的價格走勢圖........................................54
參考文獻 一、國內文獻
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2.何文榮與曾見文(2007),「台灣50指數ETF價格發現之研究」,華人經濟研究,第五卷第一期,頁87-107。
3.吳明哲、邱國欽、黃佩柔與許寶文(2011),「台灣指數股票型基金之績效表現與超額報酬分析」,財金論文叢刊,第十五期,頁71-80。
4.許光華、張哲郎、李見發與嚴宗銘(2007),「金融商品價格關聯性之研究」,朝陽學報,第十二期,頁123-143。
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