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研究生:邱毓珈
研究生(外文):Chiu,Yuchia
論文名稱:比較避險績效 – 以CSI300指數期現貨為例
論文名稱(外文):Comparing the Hedging Performance – Empirical evidence from CSI300 index and index futures
指導教授:蕭榮烈蕭榮烈引用關係
口試委員:邱建良邱靖博郭淑惠吳佩珊
口試日期:2012-06-12
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:國際企業研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:42
中文關鍵詞:最適避險比率滬深300指數期貨高頻資料
外文關鍵詞:Optimal Hedge RatioCSI300 Index FuturesHigh Frequency Data
相關次數:
  • 被引用被引用:1
  • 點閱點閱:191
  • 評分評分:
  • 下載下載:7
  • 收藏至我的研究室書目清單書目收藏:0
本文探討滬深300股價指數及滬深300指數期貨,以高頻率資料為主,並且應用5種計量模型計算出各別的最適避險比率,進一步以未避險模型為基準,衡量5種模型各別的避險績效。其中包含自然避險法、傳統靜態避險法、固定條件相關係數模型、動態條件相關係數模型以及不對稱動態條件相關係數模型。在考量報酬極大化以及風險極小化後,可得知固定條件相關係數模型及不對稱動態條件相關係數模型,分別於樣本內和樣本外為最適避險模型。而本篇論文所得之結論,不對稱動態條件相關係數模型應用於樣本外為最適避險模型,可以提供給滬深300指數期貨的投資者進行更有效的避險策略。
This study investigates five competing time series econometric models with high-frequency data on CSI300 Index and CSI300 Index Futures. For the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the naïve, the conventional static, the constant conditional correlation (CCC) GARCH, the dynamic conditional correlation (DCC) GARCH, and the asymmetric dynamic conditional correlation (ADCC) GARCH models. With regard to the reduction of variance in the returns of hedged portfolios, the results clearly show that the CCC GARCH and the ADCC GARCH have higher mean return and higher average variance reduction across hedged and unhedged positions in the in- sample and out-of-sample. In CSI300 stock index futures, the hedge ratio from ADCC GARCH model provides greater variance reduction in out-of-sample. These findings are helpful to risk managers dealing with China stock index futures.
Contents I
List of Tables II
List of Figures III
1. Introduction 1
2. Literature Review 4
3. Methodology 9
3.1 Tests of Time Series Data 9
3.1.1 Unit Root Test 9
3.1.2 Cointegrating Test 11
3.1.3 Series Autocorrelation Test 13
3.1.4 ARCH Effect Test 13
3.1.5 Asymmetric of Conditional Variance Test 14
3.2 Hedge Methodology 16
3.2.1 Naive one to one hedge ratio 16
3.2.2 OLS estimated hedge ratio 16
3.2.3 Constant conditional correlations model 17
3.2.4 Dynamic conditional correlations model 18
3.2.5 Asymmetric dynamic conditional correlation model 20
3.3 Evaluation of hedging effectiveness 21
4. Empirical Research and Analysis 23
4.1 Data Source 23
4.1.1 Trends of Stock index and Futures 24
4.1.2 Descriptive statistics 25
4.2 Unit Root Test and Johansen Cointegration Test 26
4.3 Asymmetric of Conditional Variance Test 29
4.4 Estimation of the Parameters 29
4.5 Evaluation of Hedging Effectiveness 32
5. Conclusions 37
References 39

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