臺灣博碩士論文加值系統

本研究以原油期貨、原油現貨與股價指數探討彼此間波動度的領先落後關係。研究期間為1990年至2007年，利用GARCH模型、自我向量迴歸(VAR)、衝擊反應函數與預測誤差變異分解研究它們之間的關係。實證結果如下： (1)任一國家中，在VAR模型原油期貨波動度領先原油現貨波動度；(2)在加拿大與中國，原油現貨波動度領先股價指數波動度；(3)在俄羅斯，原油期貨波動度領先股價指數波動度；(4)在日本、台灣與美國，股價指數波動度不會受到原油現貨波動度與原油期貨波動度所變動；(5)在衝擊反應，原油現貨波動度受到原油期貨波動度的衝擊只有在前二期有顯著的反應；(6)由預測誤差變異分解可知，解釋原油期貨波動度的變動最重要的因素為原油現貨波動度。

This paper investigates the lead-lag relationship between dynamic volatility of Brent crude oil spot, crude oil futures and those of stock indices in Canada, China, Russia, U.S., Japan and Taiwan for the period 1990-2007. The paper utilized the GARCH model, Vector autoregression (VAR) model, Impulse Response analysis and Forecast Error Variance Decomposition to analyze their relationship. The findings in the paper as follows: First, volatility of crude oil futures leads crude oil spot in VAR model for each country. Second, in Canada and China, volatility of crude oil spot leads that of stock index. Third, in Russia, volatility of crude oil futures leads that of stock index. Forth, in Japan, Taiwan and U.S., volatility of stock index does not be affected by those of crude oil futures and crude oil spot. Fifth, according to the results of impulse response analysis, volatility of crude oil spot has a significant response in the period 2 by the shock from that of crude oil futures. Sixth, volatility of crude oil spot is the main element to explain volatility of crude oil futures in forecasting error variance decomposition.

摘要 i
Abstract ii
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
Chapter 2 Literature review 4
2.1 Oil price and Economic activity 4
2.2 Oil price and Stock market 5
Chapter 3 Data 10
3.1 Brent Crude oil 10
3.2 Stock Index 11
3.3 Country Crude Oil Briefs 11
3.4 Descriptive statistics 15
Chapter 4 Model 16
4.1 Unit Root test 16
4.2 GARCH model 17
4.3 The Vector autoregression (VAR) model 22
4.4 Granger causality test 23
4.5 Impulse Response analysis and Forecast Error Variance Decomposition 24
Chapter 5 Empirical Results 27
5.1 Unit Root Test 27
5.2 The Vector autoregression (VAR) model 28
5.3 Granger causality test 29
5.4 Impulse Response analysis 32
5.6 Forecast Error Variance Decomposition 33
Chapter 6 Conclusions 46
References 48

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