
系統識別號 
U00021701200610380800 
中文論文名稱

風險衡量與預測以原油商品為例 
英文論文名稱

Risk Measuring and ForecastingThe Case of Crude Oil 
校院名稱 
淡江大學 
系所名稱(中) 
財務金融學系博士班 
系所名稱(英) 
Department of Banking and Finance 
學年度 
94 
學期 
1 
出版年 
95 
研究生中文姓名 
鄭婉秀 
研究生英文姓名 
WanHsiu Cheng 
學號 
890490013 
學位類別 
博士 
語文別 
英文 
口試日期 
20051224 
論文頁數 
75頁 
口試委員 
指導教授邱建良 指導教授李命志 委員梁發進 委員俞海琴 委員葉銀華 委員莊武仁 委員謝文良 委員李命志

中文關鍵字 
原油
風險值
拔靴法
跳躍
預測包含力

英文關鍵字 
Crude oil
VauleatRisk
Bootstrapping
Jump
Forecast encompassing

學科別分類 
學科別＞社會科學＞商學

中文摘要 
風險之衡量與預測為財務上重要的課題，但多數文獻皆集中探討金融商品上之風險與預測，對於能源商品之探討相對較為缺乏。然而受到供需不均、國際石油輸出組織政策及政治面干涉等因素影響，原油市場的價格波動極大，油價持續向上攀升，不僅對原油市場的交易造成影響，金融市場亦間接受到不小衝擊。有鑑於原油市場價格之高度波動性，因此探討原油商品的風險亦為重要的課題。本文除修正傳統方法在衡量與預測上之不足，更進一步研究與分析西德州(West Texas Intermediate, WTI)原油商品之風險值、跳躍波動以及在預測上所面臨的問題。
第一個主題探討風險值。風險值是財務上最為廣泛用來估計風險的指標，本文採用RiskMetrics及ARGARCH兩模型估計原油之風險值，結合移動視窗(rolling window)與拔靴法(bootstrapping)的方法進行估計。拔靴法解決了蒙地卡羅模擬法需要設定分配的假設及使用歷史模擬法恐有樣本資料不足的情形，因此晚近研究均以拔靴法取代財務上過去常用之蒙地卡羅模擬法與歷史模擬法。拔靴法的結果顯示在「估計期間外一天的預測 (onedayahead forecast)」風險值預測表現較佳，但「估計期間外十天的預測(tendaysahead forecast)」表現則不盡理想。另外，使用RiskMetrics或是ARGARCH模型估計之預測績效差異不大。
第二個主題探討加入跳躍值來衡量原油與汽油之波動性。傳統模型多建立在連續且平穩的擴散過程上，一旦有異常事件發生，價格產生劇烈波動，估計結果將產生偏誤。因此，當面臨波灣戰爭之重大事件時，考量具間斷特性的跳躍模型有其必要性，藉以正確估計波動性。本文依據Chan (2003)所提出之雙變量跳躍GARCH模型估計原油與汽油間之波動性與相關性，並進一步探討兩次波灣戰爭期間，在價格上所造成之衝擊。實證結果發現，原油與汽油之跳躍點幾乎相同，但波動共移性的程度卻逐漸下降，共變異數在第二次波灣戰爭時明顯低於第一次波灣戰爭；而在波灣戰爭期間，原油之波動性高且較為敏感。再者，因戰爭所引起之大幅度波動皆未持續很長一段時間，符合跳躍模型之特性，因此，以此模型來估計原油商品的波動性是較為恰當的。
第三個主題探討樣本內估計期間長短對樣本外預測正確性所產生之影響，截至目前為止，少有文章將重心放在此研究主題上。本文同時採用預測包含力檢定(forecast encompassing)與預測誤差檢驗(mean square forecast errors, MSE)為模型選取的準則。一般而言，在適切的模型下，估計樣本期間越長，資訊漸趨完整，估計與預測結果將越正確；過短的估計期間將產生估計與預測偏誤的結果。針對此主題，本文針對一有結構性轉變之時間序列資料建立兩個實證模型，一包含結構性轉變，代表模型設定正確；另一則無結構性轉變，代表模型設定錯誤。此外，本文採用移動視窗與遞迴(recursive)的預測方式進行估計與預測，以檢驗樣本內估計期間長短對預測結果之影響。實證結果發現，正確模型的預測誤差隨著估計期間的增加而較低。當樣本估計期間較短時，將導致接受錯誤決策的結果。最後，本文將此結論應用於避險績效上，亦有一致性的結果，即避險績效在遞迴預測方式下最佳。

英文摘要 
Risk measuring and forecasting are important issues in finance, however most literature focuses on the financial assets, and fewer papers discuss energy assets. The Petroleum market is characterized as highly volatile, the imbalance of supply and demand, the strategies adopted by the Organization of Petroleum Exporting Countries (OPEC), the interference of politics and so on have all stimulated prices. The oil prices have climbed up steadily recently, and it has not only shocked the petroleum market traders, but also influenced the financial market as well, owing to the high volatility of the crude oil. Thus the investigation of the crude oil risk is an important issue. This thesis analyzes the valueatrisk, the jump volatilities, and the forecast problems in crude oil of West Texas Intermediate (WTI), which modifies the shortcomings of traditional models in measurements and forecasts.
The first topic is discussing the ValueatRisk (VaR). VaR is the most popular and attractive method of risk measuring. We estimate the VaR of the return on crude oil via RiskMetrics and the ARGARCH model using the rolling bootstrapping methods. We adopt the bootstrapping method rather than using the Monte Carlo simulation or the historical simulation method because traditionally they are methods to estimate VaR. Even though they are traditional methods but they actually have the two severe problems of distribution assumption, Monte Carlo simulation, and a short observation period, historical simulation. The empirical results demonstrate that the bootstrapping method outperforms the nobootstrapping method in the onedayahead VaR forecast but not in the tendaysahead forecasts. Furthermore, the performances of VaR forecasts are statistically indifferent in both the RiskMetrics and the ARGARCH models.
The second topic is estimating the volatility of crude oil and gasoline while considering jumps. Previous studies in the literature almost all assumed that time series data follows a smooth and continuous volatility process. However, the presence of abnormal events induce serious violate in price, and the diffusion models are misspecified statistically. Therefore, considering the jump model with discrete characteristics is necessary while facing the abnormal events like two Gulf Wars. We further employ a correlated bivariate Poisson GARCH model suggested by Chan (2003) to investigate the relationship between the volatility of crude oil and gasoline; especially during the period of the Gulf War. We find that greater jumps occurring in crude oil returns will appear in gasoline returns at the same time, but the magnitude of the comovements in volatility falls. The covariance is relatively smaller in the Second Gulf War compared to the first conflict. The volatility of crude oil is more sensitive than gasoline during the periods of wars. Furthermore, the jump that occurred by the war did not lead both spot prices to a high persistent level for a long period, which fits the feature of the jump models.
The third topic investigates an essential problem of how to determine the estimation period in forecasting. Until now, less attention has been given to the problem of determining the appropriate estimation periods. Using the forecast encompassing and accuracy test, this investigation discusses the importance of considering the overall useful information in the insample period. An excessively short sample period will increase the variance of the parameter estimation and bias the outofsample forecasts. This study further constructs a nested linear regression model, either with or without the structural change, depending on the existence of a break, and comparing the performance of the two versions of the model for each estimation period and forecast scheme. The empirical results demonstrate that forecasts under the correct model reduces both measurement loss and the mean square forecast as we increase the insample estimation period. For the forecast accuracy and encompassing tests, the use of fewer observations in making an estimate could easily lead to wrong decisions and the acceptance of the wrong model. Finally, these results are also consistent with the hedge effectiveness, namely that the effectiveness is better under the recursive scheme in terms of considering all useful information.

論文目次 
TABLE OF CONTENTS
Page
ACKNOWLEDGEMENT i
ABSTRACT IN CHINESE ii
ABSTRACT IN ENGLISH iv
LIST OF TABLES xi
LIST OF FIGURES xii
PART I
Modeling ValueatRisk for Oil Prices Using a Bootstrapping Approach
ABSTRACT 2
CHAPTER
1. Introduction 3
1.1 Motivations and Objectives 3
1.2 Flow Chart 5
2. Literature Review 6
2.1 ValueatRisk in Crude Oil 6
2.2 Calculating the VaR 6
2.3 Observation Periods and OutofSample Forecast 9
3. Measurement of ValueatRisk 11
3.1 Definition of VaR 11
3.2 Statistical Tests 11
3.3 Estimate Methodology: Bootstrapping 12
3.4 Empirical Models 13
3.5 Observation Periods and OutofSample Forecast in Rolling Window 15
4. Empirical Results 16
4.1 Data 16
4.2 Estimated Results of the RiskMetrics Method: Mean of the Decay Factor
16
4.3 Estimated Results and the Adaptation of the AR(1)GARCH(1,1) Method
17
4.4 Failure Rates and the Performance of VaR Models 19
5. Conclusions 23
Bibliography 24
PART II
Correlated Jumps in Crude Oil and Gasoline during the Gulf War
ABSTRACT 28
CHAPTER
1. Introduction 29
1.1 Motivations and Objectives 29
1.2 Flow Chart 32
2. Literature Review 33
2.1 The Jump Model 33
2.2 The Bivariate Jump Model 34
3. Correlated Bivariate Poisson GARCH Model (CBPGARCH) 36
3.1 Definition 36
3.2 The Probability Function of Jump and the Jump Intensity 37
3.3 GARCH Function 38
4. Empirical Results 40
4.1 Data 40
4.2 The Empirical Results of the CBPGARCH Model 41
4.3 The Effects of Wars and Politics 45
4.4 The Covariance between Crude Oil and Gasoline 48
5. Conclusions 50
Bibliography 51
PART III
Enhancing the Forecast Accuracy by Using Long Estimation Periods
ABSTRACT 55
CHAPTER
1. Introduction 56
1.1 Motivations and Objectives 56
1.2 Flow Chart 58
2. Literature Review 59
2.1 Sample Period Length 59
2.2 Rolling Scheme vs. Recursive Scheme 59
2.3 Constructing Model with Structural Break 60
2.4 OutofSample Forecast Error Tests 61
3. Methodology 63
3.1 The ModelSetting and Forecast Scheme 63
3.2 Forecast Accuracy and Forecast Encompassing Tests 63
4. Empirical Results 65
4.1 Data and the Empirical Model 65
4.2 The Empirical Results 66
4.2.1 Under the Rolling Scheme 66
4.2.2 Under the Recursive Scheme 68
4.3 Application in Hedge Effectiveness 69
5. Conclusions 72
Bibliography 73
LIST OF TABLE Page
PART I
Table 1. The mean of λ with the RiskMetrics method 16
Table 2. RESET test 17
Table 3. The empirical results of the AR(1)GARCH(1,1) model 18
Table 4. The number of failure and the failure rates 20
Table 5. Failure rate test 21
PART II
Table 1. Descriptive statistics 40
Table 2. Empirical results of CBP model 42
Table 3. The covariance of crude oil and gasoline price in different periods 49
PART III
Table 1. Empirical results under the rolling scheme 67
Table 2. Empirical results under the recursive scheme 69
Table 3. Hedge effectiveness 71
LIST OF FIGURES
Page
PART I
Figure 1. The time series plot of crude oil prices from 1994/1/1 to 2004/7/31 3
Figure 2. The definition of observation periods and holding period 15
Figure 3. Crude oil returns and VaR with onedayahead forecast and 500days
observation periods 21
Figure 4. Crude oil returns and VaR with tendaysahead forecast and 500days
observation periods 22
PART II
Figure 1. The time series plot of the spot price of crude oil and gasoline 30
Figure 2. Returns of crude oil and gasoline 41
Figure 3. Time varying jump intensities in crude oil and gasoline 44
Figure 4. Correlated jump intensities and jump counter correlations 44
Figure 5. Jump variance in crude oil and gasoline 45
Figure 6. Jump covariance in crude oil and gasoline 45
Figure 7. The jump conditional variances and covariance during Gulf War I 48
Figure 8. The jump conditional variances and covariance during Gulf War II 48

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