Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/20782
DC FieldValueLanguage
dc.contributor陳連勝zh_TW
dc.contributor吳中書zh_TW
dc.contributor徐茂炫zh_TW
dc.contributor.advisorMin-Jiun Suen_US
dc.contributor.advisor蘇明俊zh_TW
dc.contributor.author鄭羽軒zh_TW
dc.contributor.authorCheng, Yu-Hsuanen_US
dc.contributor.other中興大學zh_TW
dc.date2009zh_TW
dc.date.accessioned2014-06-06T07:14:31Z-
dc.date.available2014-06-06T07:14:31Z-
dc.identifierU0005-2906200818410900zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/20782-
dc.description.abstractThe paper examined the hedging effectiveness of optimal hedge ratios derived from four different models: ordinary least square model, a vector auto-regression, a vector error-correction model and a dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model. The hedged portfolio consisted of finance sector sub-index and finance sector index futures and electronics sector sub-index and electronics sector index futures. Daily observations covered the period 21 July 1999-31 December 2007. In both sectors for Taiwan sector index futures, hedge ratio derived from ordinary least square model provided greater risk reduction, whereas hedge ratio derived from DCC-GARCH model yielded higher utility. The results would be useful for risk managers dealing with Taiwan sector index futures.en_US
dc.description.abstract本文應用了四個不同統計模型(回歸模型、VAR模型、VECM模型及雙變量GARCH模型)估計最適期貨避險比例,而進而根據此避險比例比較不同模型的避險績效。研究資料來源為台灣電子類股指數、台灣金融類股指數、台灣電子類股指數期貨及台灣金融類股指數期貨,其資料區間為1999年7月21日至2007年12月31號日資料。實證結果顯示,回歸模型避險比例在電子及金融指數能夠最佳的降低風險,而DCC-GARCH模型在電子及金融指數能夠最佳的提升效能。zh_TW
dc.description.tableofcontentsTable of Contents 1 Introduction 1 1.1 Research background 1 1.2 Research objectives 2 1.3 Research targets 2 1.4 Research process 2 2 Literature Review 4 2.1 Futures contract 4 2.1.1 Definition of futures contract 4 2.1.2 Hedging with futures contract 4 2.2 Theory of hedging 5 2.2.1 Traditional hedging theory and Working''s hypothesis5 2.2.2 Portfolio approach hedging theory and hedging effectiveness 5 2.3 Vector autoregressive model 18 2.4 Vector error-correction model 18 2.5 Multivariate DCC-GARCH model 19 3 Research Methods 20 3.1 Research structure 20 3.2 Stationary process 22 3.3 Unit root test 22 3.3.1 Augment Dickey-Fuller (ADF) test 22 3.3.2 Phillips-Perron test 23 3.3.3 Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test 24 3.3.4 Lag length selection 25 3.4 Conventional OLS regression model 26 3.5 Bivariate vector autoregressive model (VAR) 26 3.6 Test for cointegration 27 3.6.1 The Engle-Granger two-step method 27 3.6.2 Johansen procedure 28 3.6.3 Test statistics for number of cointegrating relationships 30 3.7 Vector error correction model (VECM) 31 3.8 Autoregressive conditional heteroskedastic (ARCH) model 32 3.8.1 ARCH test 33 3.8.2 Constant conditional correlation (CCC) test 33 3.9 The constant conditional correlation model 34 3.10 The DCC Multivariate GARCH Model 34 3.10.1 Characteristics of DCC-GARCH model 35 3.10.2 Process of DCC-GARCH estimation 36 3.10.3 Method in parameter estimation 37 3.11 Hedge ratio calculation 38 3.11.1 Minimum variance comparison 38 3.11.2 Utility-based comparison 39 4 Data and Preliminary Analyses 40 4.1 Data selection and data period selection 40 4.2 History of index futures market 41 4.3 Variable name abbreviation 41 4.4 Descriptive statistics analysis 42 4.5 Unit root test results 42 4.6 OLS Regression model 44 4.7 Selection of optimal lag length 44 4.8 Vector autoregressive model 45 4.9 Tests for cointegration 47 4.10 Vector Error Correction Model 48 4.11 Constant conditional correlation (CCC) test 49 4.12 VECM-DCC-GARCH model 50 4.13 Hedging effectiveness comparison 53 4.13.1 Minimum variance comparison 54 4.13.2 Utility-based comparison 54 5 Conclusions and Recommendations 56 5.1 Conclusions 56 5.2 Recommendations for future researches 57 References 58 Appendices 62 Appendix A: Model Results 62 Appendix B: Spot and futures price series 69 Appendix C: Futures contract specification 71 List of Tables Table 2 - 1 Summary of hedging effectiveness related literature 11 Table 4 - 1 Code, variables, research period, data type and data source 40 Table 4 - 2 Variable names 41 Table 4 - 3 Descriptive statistics for returns 42 Table 4 - 4 Tests for unit roots 43 Table 4 - 5 OLS regression results for RELES 44 Table 4 - 6 OLS regression results for RFINS 44 Table 4 - 7 Lag length Selection 45 Table 4 - 8 Estimates from the Bivariate VAR(4) Model for electronics sector index 46 Table 4 - 9 Estimates from the Bivariate VAR(4) Model for finance sector index 46 Table 4 - 10 Choice of number of cointegrating relations using model selection 47 Table 4 - 11 Johansen's test for cointegration 47 Table 4 - 12 Vector error-correction model VECM(4) for Electronics sector index 48 Table 4 - 13 Vector error-correction model VECM(4) for Finance sector index 49 Table 4 - 14 Tse's test for constant correlation 49 Table 4 - 15 DCC-GARCH model - Mean equation for electronics sector index 50 Table 4 - 16 DCC-GARCH model - Mean equation for finance sector index 51 Table 4 - 17 DCC-GARCH Model - Variance Equation 52 Table 4 - 18 Risk-return hedging performance comparison54 Table 4 - 19 Utility-maximization hedging performances comparison 55 Table A - 1 OLS regression model for Electronics sector index 62 Table A - 2 OLS regression model for Finance sector index 62 Table A - 3 Vector autoregression (VAR) for Electronics sector index 63 Table A - 4 Vector autoregression (VAR) for Finance sector index 64 Table A - 5 Vector error-correction model (VECM) for Electronics sector index 65 Table A - 6 Vector error-correction model (VECM) for Finance sector index 66 Table A - 7 Dynamic conditional correlation GARCH for Electronics sector index 67 Table A - 8 Dynamic conditional correlation GARCH for Finance sector index 68 Table C - 1 Futures contract specification for Electronic sector index futures 70 Table C - 2 Futures contract specification for Finance sector index futures 70 List of Figures Figure 1 - 1 Research Process 3 Figure 3 - 1 Research Structure 21 Figure 4 - 1 Dynamic hedge ratios of Electronics sector sub-index 53 Figure 4 - 2 Dynamic hedge ratios of Finance sector sub-index 53 Figure B - 1 Spot price series for Electronic sector index 69 Figure B - 2 Futures price series for Electronics sector index 69 Figure B - 3 Spot price series for Finance sector index 70 Figure B - 4 Futures price series for Finance sector index 70en_US
dc.language.isoen_USzh_TW
dc.publisher企業管理學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2906200818410900en_US
dc.subjectHedging effectivenessen_US
dc.subject避險效率zh_TW
dc.subjectVECMen_US
dc.subjectDCC-GARCHen_US
dc.subject向量誤差修正模型zh_TW
dc.subjectDCC-GARCH模型zh_TW
dc.titleHedging Effectiveness of Taiwan Index Futures - Empirical Evidences from Electronics Sector and Finance Sectoren_US
dc.title台灣指數期貨避險績效-以電子及金融指數期貨為例zh_TW
dc.typeThesis and Dissertationzh_TW
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