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系統識別號 U0002-0406201011463200
中文論文名稱 全球金融海嘯期間之股市波動預測與風險值
英文論文名稱 Forecasting Volatility and Capturing Downside Risk in Financial Markets under the Subprime Mortgage Crisis
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士班
系所名稱(英) Department of Banking and Finance
學年度 98
學期 2
出版年 99
研究生中文姓名 張高瑩
研究生英文姓名 Kao-Ying Chang
學號 697530110
學位類別 碩士
語文別 英文
口試日期 2010-05-16
論文頁數 100頁
口試委員 指導教授-邱建良
共同指導教授-劉洪鈞
委員-李命志
委員-盧陽正
委員-邱哲修
中文關鍵字 風險值  次貸風暴  變幅  已實現波動  指數期貨  指數型股票基金 
英文關鍵字 Value-at-Risk  Subprime mortgage crisis  Range  Realized volatility  Index futures  Exchange Traded Fund 
學科別分類 學科別社會科學商學
中文摘要 本論文以台灣股價指數期貨及美國SPDRs自2001年至2008年之日資料為實證標的,全球金融海嘯(2008)為預測期間,進行波動性預測能力比較及風險值績效評估。若預測模型可以在金融危機期間具有良好表現,實務上應該具有相當的重要性。有別於傳統文獻大多使用報酬率的平方作為市場真實波動的代理變數,本論文改以PK變幅、GK變幅、RS變幅及已實現波動度(RV),並同時採用對稱與不對稱損失函數評估模型的波動性預測績效。更進一步加入已實現波動為基礎的風險值模型(RV-VaR),利用Kupiec(1995)提出之非條件涵蓋率檢定,比較RV-VaR與GARCH族為基礎的風險值模型之風險管理績效。
實證結果皆指出,以不對稱GARCH模型的波動性預測能力較佳,顯示不對稱的變異數方程式設定能提升波動預測績效,其中以EGARCH模型最佳,而GARCH模型表現最差。在風險值評估部份,台灣股價指數期貨的實證結果,發現RV-VaR模型有低估風險值之虞,以致未能通過回溯測試;反之,GARCH族模型卻能提供準確的風險值預測績效。而美國SPDR指數型股票基金的結果則顯示各模型皆通過回溯測試,其中以RV-VaR模型較能準確估算真實風險值。整體來說,EGARCH與RV-VaR 模型分別為TAIFEX與SPDRs的最佳模型,此結果可提供機構法人、執政當局、風險管理者、投資大眾在面對未來極端事件時的參考依據,並提升風險控管績效。
英文摘要 This thesis applies alternative GARCH-type models to daily volatility forecasting with Value-at-Risk (VaR) application to the Taiwanese stock index futures and Standard & Poor’s Depositary Receipts (SPDRs) that suffered the global financial tsunami that occurred during 2008. Instead of using squared returns as a proxy for true volatility, this thesis adopts four volatility proxy measures, the PK-range, GK-range, RS-range, and RV, for use in the empirical exercise. The volatility forecast evaluation is conducted with a variety of volatility proxies according to both symmetric and asymmetric types of loss functions regarding forecasting accuracy. These models are also evaluated in terms of their ability to provide adequate VaR estimates with the inclusion of realized-volatility-based VaR model. Moreover, the predictive performance of the RV-based VaR model is compared with various GARCH-based VaR models according to both unconditional coverage test (Kupiec,1995) and utility-based loss functions with respect to risk management practice.
Empirical results indicate that the EGARCH model provides the most accurate daily volatility forecasts, whereas the performances of the standard GARCH model are relatively poor. Such evidence suggests that asymmetry in volatility dynamics should be taken into account for forecasting financial markets volatility. Moreover, I find a consistent result that the forecasting performance of models remains constant across various volatility proxies for both empirical data in most cases. In the area of risk management,the RV-VaR model tends to underestimate VaR and has been rejected for lacking correct unconditional coverage for the TAIFEX returns data, while the GARCH genre of models is capable of providing satisfactory and reliable daily VaR forecasts. In particular, the asymmetric EGARCH model is the most preferred. For SPDRs case, while all models have passed the back-test, the RV-VaR is considered the optimal VaR model both for a regulator and for a firm at alternative confidence levels during the whole year of 2008. The empirical findings presented here provide crucial implications for market practitioners, such as, policy makers, institutional risk managers, and common investors in risk management.
論文目次 TABLE OF CONTENTS

ACKNOWLEDGEMENTS i
ABSTRACT IN CHINESE ii
ABSTRACT IN ENGLISH iii
LIST OF TABLES viii
LIST OF FIGURES ix

CHAPTER
1.Introduction 1
1.1 Research Background 1
1.1.1 Lessons from Long-Term Capital Management Crisis 1
1.1.2 The Subprime Mortgage Storm in America 4
1.1.3 The Expatiation of ETFs and Futures 7
1.2 Motivations 9
1.3 Objectives 12
1.4 Flow Chart 14
2.Literature Review 15
2.1 Volatility Forecasting 15
2.1.1 Volatility Proxy Measures 15
2.1.2 Related Literature Review 18
2.2 Applications to Risk Management: Value-at-Risk 22
2.2.1 The definition of Value-at-Risk 22
2.2.2 Related Literature Review 24
2.3 Contribution of the Thesis 34
3. Data Description and Preliminary Analysis 36
3.1 Data Description 36
3.1.1 Taiwanese Stock Index Futures 37
3.1.2 Standard and Poor's Depositary Receipts 37
3.2 Preliminary Analysis of Empirical Data 39
3.2.1 Descriptive Statistics 39
3.2.2 Descriptive Graphs 44
4. Econometric Frameworks 48
4.1 Various Adaptations of GARCH Models 48
4.1.1 GARCH (1,1) Model 48
4.1.2 GJR-GARCH Model 49
4.1.3 QGARCH Model 50
4.1.4 EGARCH Model 50
4.2 The Skewed Generalized T (SGT) Distribution 51
4.3 Alternative Volatility Proxies 53
4.4 Performance Evaluation for Volatility Forecasting Models 54
4.4.1 R-squared from the Mincer and Zarnowitz (1969) Regression 54
4.4.2 Symmetric Loss Functions 55
4.4.3 Asymmetric Loss Function 55
4.5 Model-Based VaRs and their Evaluations 56
4.5.1 The LR Test for Unconditional Coverage 56
4.5.2 The Utility-Based Loss Functions 57
4.5.2.1 Regulatory Loss Function (RLF) 57
4.5.2.2 Firm Loss Function (FLF) 58
5.Empirical Results and Analysis 59
5.1 Model estimates and diagnostic test 59
5.2 Analyzing Volatility forecasting performance of models62
5.2.1 R-squared from Mincer and Zarnowitz (1969) regression 62
5.2.2 Forecasting performance based on symmetric loss errors 63
5.2.3 Forecasting performance based on asymmetric loss errors 66
5.3 Analyzing predictive accuracy of the model-based VaR 75
5.3.1 VaR performance for various models at the 99% confidence level 75
5.3.2 VaR performance for various models at the 99.5% confidence level 81
6.Conclusions and Suggestions 87
BIBLIOGRAPHY 91


LIST OF TABLES

Table 3.1 The Contract Specifications of the Taiwanese Stock Index Futures36
Table 3.2 The Specifications of the SPDRs 39
Table 3.3 Descriptive Statistics of Daily TAIFEX Futures and SPDRs Returns 43
Table 3.4 Descriptive Statistics of alternative volatility proxies for TAIFEX Futures and SPDRs 44
Table 5.1 Estimation results of various GARCH models 61
Table 5.2 The Mincer and Zarnowitz (1969) regression test results 62
Table 5.3 Out-of-sample forecasting performance based on symmetric loss errors:
The case for TAIFEX 64
Table 5.4 Out-of-sample forecasting performance based on symmetric loss errors:
The case for SPDRs 65
Table 5.5 Out-of-sample forecasting performance based on asymmetric loss errors:
The case for TAIFEX 68
Table 5.6 Out-of-sample forecasting performance based on asymmetric loss errors:
The case for SPDRs 69
Table 5.7 Value-at-Risk forecasting results for TAIFEX at the 99% confidence level78
Table 5.8 Value-at-Risk forecasting results for SPDRs at the 99% confidence level 78
Table 5.9 Value-at-Risk forecasting results for TAIFEX at the 99.5% confidence level 83
Table 5.10 Value-at-Risk forecasting results for SPDRs at the 99.5% confidence level 83


LIST OF FIGURES

Figure 3.1 Daily TAIFEX futures prices in level 41
Figure 3.2 Daily SPDRs prices in level 41
Figure 3.3 Daily TAIFEX futures returns 45
Figure 3.4 Daily SPDRs returns 45
Figure 3.5 Daily TAIFEX futures QQ-plot 46
Figure 3.6 Daily SPDRs QQ-plot 46
Figure 3.7 Daily TAIFEX futures returns density 47
Figure 3.8 Daily SPDRs returns density 47
Figure 5.1 PK versus various model-based volatility forecasts for TAIFEX 70
Figure 5.2 GK versus various model-based volatility forecasts for TAIFEX 71
Figure 5.3 RS versus various model-based volatility forecasts for TAIFEX 71
Figure 5.4 RV versus various model-based volatility forecasts for TAIFEX 72
Figure 5.5 PK versus various model-based volatility forecasts for SPDRs 72
Figure 5.6 GK versus various model-based volatility forecasts for SPDRs 73
Figure 5.7 RS versus various model-based volatility forecasts for SPDRs 73
Figure 5.8 RV versus various model-based volatility forecasts for SPDRs 74
Figure 5.9 Daily returns versus the EGARCH-based VaR forecasts at the 99% confidence level:The case of TAIFEX 80
Figure 5.10 Daily returns versus the RV-based VaR forecasts at the 99% confidence level:The case of SPDRs 80
Figure 5.11 Daily returns versus the EGARCH-based VaR forecasts at the 99.5% confidence level:The case of TAIFEX 85
Figure 5.12 Daily returns versus the RV-based VaR forecasts at the 99.5% confidence level:The case of SPDRs 85











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