本論文著重在風險值的衡量與選擇權定價，共包含三個部份，分別為「石油衝擊下預測黃金市場之風險值」、「一般化偏態誤差分配預測美國原油市場之風險值」與「一般化偏態誤差分配的選擇權定價」。將此三部份的內容簡述如下：
第一部分在考慮石油波動性與使用較彈性化模型如BHK-PGARCH-HV等一系列模型來探討黃金市場之風險值。石油波動性採用ARJI模型估計並將此分成隨機與跳躍波動。最後以滾動視窗法預測樣本外之風險值，由實證結果可知將波動分成隨機與跳躍並考慮高波動之BHK-PGARCH-HV模型預測能力最佳，也就是說高波動與跳躍波動在黃金市場之風險值預測時是不可以忽略。
第二部份中我們以複合辛普森數值積分法，來計算一般化偏態誤差分配(SGED)之尾部臨界值，並使用ARJI-N 和ARJI-SGED模型來對布倫特與西德州原油日現貨價預測樣本外之風險值。由實證結果可知布倫特原油呈現稍微左偏而西德州原油呈現稍微右偏，因此ARJI-N模型會高估布倫特原油真實的風險值而會低估西德州原油真實的風險值。此發現可說明一般化偏態誤差分配在美國原油市場樣本外風險值預測之重要性。
第三部份本文假設金融資產之對數價格服從一般化偏態誤差分配(SGED)下推導歐式選擇權買權之定價公式，然後再使用第二部份中所用複合辛普森數值積分法求SGED分配及其特例分配在不同偏態與峰態組合下選擇權買權之價格，再分別探討偏態與峰態對Black-Scholes偏誤之影響。由數值分析結果，我們可得以下結果：在定價過程存在非對稱現象；對任意 ( )值，過度定價或不足定價程度會隨 ( )絕對值增加（減少）而增加；有關峰態對Black-Scholes偏誤之影響，當 =2時，Black-Scholes在齊質偏誤點左側會對具有負（正）偏態產生過度定價（不足定價），在齊質偏誤點右側會對具有負（正）偏態產生不足定價（過度定價）。當 =1.5 及 1.0時，過度區域會隨偏態係數 由  增加至 0.25而往右移動。過度定價或不足定價之程度也會隨 由2減少至1而增加。有關偏態對Black-Scholes偏誤之影響，當 = 0時，Black-Scholes在左邊的齊質偏誤點左側及右邊的齊質偏誤點右側會產生不足定價，在這兩個齊質偏誤點間之區域會產生過度定價情形。當 = 時，過度區域會隨 由2減少至1而往右（左）移動致使過度區域範圍變大。以上的發現將有助於解釋各種已知Black-Scholes的偏誤現象。

This study focuses on VaR measurement and Option pricing, and it contains
three parts. The first part is titled “Value-at-Risk Forecasts in Gold Market under Oil Shocks”, the second part is named “Value-at-Risk Forecasts in U.S. Crude Oil Market with Skewed Generalized Error Distributions.”, and the last one is “Option Pricing with Skewed Generalized Error Distributions.”
A brief introduction of these three parts is described as follow: The first part investigates the value-at-risk in gold markets by considering both oil volatilities and the flexible model construction. The oil volatility is estimated using the dynamic jump model, and the volatility is distinguished further into stochastic and jump volatility. The flexible models include the BHK and PGARCH models. Finally, by combining the data with the rolling window approach, the appropriate out-of-sample VaR estimates are clearly obtained in this paper. The empirical results demonstrate that the  BHK-PGARCH-HV-type model, which distinguish both the crude oil volatility and focus on the high volatilities, perform best in this paper. That is to say, the high volatility and jump volatility cannot be ignored in forecasting gold VaR.
In the second part, we propose a composite Simpson’s rule, a numerical integral method, for estimating quantiles on the skewed generalized error distribution (SGED). Daily spot prices of Brent and WTI crude oil are used to examine the one-day-ahead VaR forecasting performance of the ARJI-N and ARJI-SGED models. Empirical results show that Brent crude oil exhibits slightly skewed to the left while WTI exhibits slightly skewed to the right. Therefore the ARJI-N model may overestimate the true VaR for Brent crude oil and underestimate the true VaR for WTI crude oil. These findings demonstrate that the use of SGED distribution, which explicitly accommodates both skewness and kurtosis, is essential for out-of-sample VaR forecasting in U.S. oil markets.
The last part presents a novel option-pricing model based on the Skewed Generalized Error Distribution (SGED). A composite Simpson’s rule is used to acquire numerical results under the SGED and its degenerative distributions with varying degrees of skewness and kurtosis. The impact of skewness and kurtosis on Black-Scholes biases is investigated. The following analytical results are based on numerical analyses. Some asymmetrical phenomena exist. For any  ( ), the extent of overpricing or underpricing increases when the absolute value of  ( ) increases (decreases). For the impact of skewness, when  =2, the Black-Scholes model overprices (underprices) the options price for a negative (positive)   on the left of the homo-bias point, whereas the model underprices (overprices) for a negative (positive)   on the right of the homo-bias point. For  = 1.5 and 1.0, the overpricing areas shift to the right when the value of   increases from   to 0.25. The degree of underpricing or overpricing increases when   decreases from 2.0 to 1.0. For the impact of kurtosis, when  = 0, the Black-Scholes model underprices the options price on the left of the left homo-bias point and on the right of the right homo-bias point, and overprices between these two points. For  =  (0.2), the overpriced areas shift to the right (left) and then increase in size when   decreases from 2.0 to 1.0. This survey will help explain the various known Black-Scholes biases.

TABLE OF CONTENTS
                                                          ACKNOWLEDGEMENT     i
ABSTRACT IN CHINESE ii
ABSTRACT IN ENGLISH iv
LIST OF TABLES      ix
LIST OF FIGURES     x

PART I Value-at-Risk Forecasts in Gold Market under Oil Shocks 1
ABSTRACT 2
CHAPTER
1. Introduction 3
1.1 Motivations and Objectives 3
1.2 Flow Chart  4
2. Literature Review 5
2.1 Literature review of the gold market 5
2.2 Volatility measurement in the oil market	 6
3. Econometric Methodology 8
3.1 Autoregressive Conditional Jump Intensity (ARJI) model for crude oil Volatility 8
3.2 The variety of BHK model 10
4. Model-based VaR Estimates and Evaluation Methods 13
4.1 Value-at-Risk (VaR) definition	 13
4.2 Evaluation Methods  13   
4.2.1 General Loss Functions	 13
4.2.2 Binary Loss Function 14
4.2.3 Quadratic Loss Function 14
4.2.4 LR test of unconditional coverage 14
5. Empirical Results and Analysis 16
5.1 Data and descriptive statistics 16
5.2 Empirical Results 17
5.2.1 The empirical results of ARJI model for crude oil volatility 17
5.2.2 The empirical results of six different models for gold returns 18
5.2.3 The measurements of out-of-sample VaR forecasting 19
6. Conclusions 26
BIBLIOGRAPHY  27

PART II Value-at-Risk Forecasts in U.S. Crude Oil Market with Skewed Generalized Error Distributions 29
ABSTRACT 30
CHAPTER
1. Introduction 31
1.1 Motivations and Objectives 31
1.2 Flow Chart 32
2. Literature Review 33
3. Econometric Methodology 35
3.1 ARJI-N model 35
3.2 ARJI-SGED model 37
4. Model-based VaR Estimates and Evaluation Methods 39
4.1 Value-at-Risk (VaR) definition	 39
4.2 Evaluation Methods 39
4.2.1 Binary Loss Function 40
4.2.2 Quadratic Loss Function 40
4.2.3 LR test of unconditional coverage 40
5. Empirical Results. And Analysis 42
5.1 Data and descriptive statistics 42
5.2 Empirical Results 43
5.2.1 The empirical results of ARJI-N and ARJI-SGED models for Brent and WTI crude oil price 43
5.2.2 The measurements of out-of-sample VaR forecasting 55
6. Conclusions 62
BIBLIOGRAPHY 63
Appendix A  65

PART III Options Pricing with Skewed Generalized Error Distributions 66
ABSTRACT 67
CHAPTER
1. Introduction 68
1.1 Motivations and Objectives 68
1.2 Flow Chart 69
2. Literature Review 70
3. SGED Distribution and its Degenerative Distributions 73
3.1 Skewed Generalized Error Distribution (SGED) 73
3.2 Skew Laplace Distribution (SLD) 74
3.3 Skew Normal Distribution (SND) 75
3.4 Generalized Error Distribution (GED) 75
4. Options Pricing under SGED Distribution 77
4.1 Geometric Brownian motion 77
4.2 Options Pricing under SGED Distribution 78
5. Numerical Results and Analysis 81
5.1 The impact of kurtosis on Black-Scholes biases 82
5.1.1 kappa=2.0 (SND) 82
5.1.2 kappa=1.5  86
5.1.3 kappa=1.0 (SLD) 89
5.2 The impact of skewness on Black-Scholes biases 93
5.2.1 lambda=0.0 (GED) 93
5.2.2 lambda=-0.2 97
5.2.3 lambda=0.2  101
6. Conclusions 104 
BIBLIOGRAPHY 105
Appendix 107

LIST OF TABLES
PART I
Table 1-1. Descriptive statistics of daily return 16
Table 1-2. Empirical results of the ARJI model for crude oil 18
Table 1-3. Empirical results of various models 19
Table 1-4. The out-of-sample forecasting of VaR 21
 
PART II
Table 2-1. Descriptive statistics of daily return(in sample period) 42
Table 2-2. Estimation results of ARJI-N and ARJI-SGED models for Brent and 
WTI crude oil 44
Table 2-3. Descriptive statistics of model parameters during rolling period for 
Brent crude oil	 46
Table 2-4. Descriptive statistics of model parameters during rolling period for 
WTI crude oil 47
Table 2-5. Out-of-sample VaR performance 57
Table 2-A1. Quantiles of SGED distribution with various combinations (kappa,lambda) 
at alternate levels 65

PART III
Table 3-1A. Option Price with % in-the-Money and Skewness(Skew Normal 
Distribution) 83
Table 3-1B Black-Scholes Bias in $ due to Skew Normal Distribution 83
Table 3-1C Black-Scholes Bias in % due to Skew Normal Distribution 84
Table 3-2A. Option Price with % in-the-Money and Skewness(Skew Laplace 
Distribution) 90
Table 3-2B Black-Scholes Bias in $ due to Skew Laplace Distribution	 90
Table 3-2C Black-Scholes Bias in % due to Skew Laplace Distribution 90
Table 3-3A. Option Price with % in-the-Money and Kappa(GED Distribution) 94
Table 3-3B Black-Scholes Bias in $ due to GED Distribution 94
Table 3-3C Black-Scholes Bias in % due to GED Distribution 95
Table 3-4A Option Price with % in-the-Money and Kappa (SGED Distribution) 98
Table 3-4B. Black-Scholes Bias in $ due to SGED Distribution (lambda=-0.2) 98
Table 3-4C. Black-Scholes Bias in % due to SGED Distribution (lambda=-0.2) 98

LIST OF FIGURES
PART I
Figure 1-1.The time series plot of gold and crude oil prices 17
Figure 1-2. Conditional Variance Components, Crude Oil 18
Figure 1-3. Returns and VaR forecasts at different confidence levels with a sequence of GARCH, GARCH-HV, GARCH-HV-A, PGARCH, PGARCH-HV, and PGARCH-HV-A model 23
Figure 1-4. Returns and VaR forecasts under each model at a sequence of 90% , 95%, 99%, and 99.5%confidence levels 25

PART II
Figure 2-1. The time series plot of Brent and WTI crude oil price 43
Figure 2-2. The trend of parameters estimates of ARJI-N and ARJI-SGED models during rolling period for Brent crude oil 51
Figure 2-3. The trend of parameters estimates of ARJI-N and ARJI-SGED models during rolling period for WTI crude oil 55
Figure 2-4. Returns and VaR forecasts under ARJI-N and ARJI-SGED models at a sequence of 90%, 95%, and 99% confidence levels for Brent Crude oil 58
Figure 2-5. Returns and VaR forecasts under ARJI-N and ARJI-SGED models at a sequence of 90%, 95%, and 99% confidence levels for WTI Crude oil 59
Figure 2-6. Returns and VaR forecasts at different confidence levels with a sequence of ARJI-N and ARJI-SGED model for Brent Crude oil 60
Figure 2-7. Returns and VaR forecasts at different confidence levels with a sequence of ARJI-N and ARJI-SGED model for WTI Crude oil 61

 PART III
Figure 3-1. Black-Scholes Bias in $ due to kappa=2.0 85
Figure 3-2. Black-Scholes Bias in % due to kappa=2.0 86
Figure 3-3. Black-Scholes Bias in $ due to kappa=1.5 88
Figure 3-4. Black-Scholes Bias in % due to kappa=1.5 89
Figure 3-5. Black-Scholes Bias in $ due to kappa=1.0 92
Figure 3-6. Black-Scholes Bias in % due to kappa=1.0 93
Figure 3-7. Black-Scholes Bias in $ due to lambda=0.0 96
Figure 3-8. Black-Scholes Bias in % due to lambda=0.0 97
Figure 3-9. Black-Scholes Bias in $ due to lambda=-0.2 100
Figure 3-10. Black-Scholes Bias in % due to lambda=-0.2	 101
Figure 3-11. Black-Scholes Bias in $ due to lambda=0.2 103
Figure 3-12. Black-Scholes Bias in % due to lambda=0.2 103

PART I
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