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博碩士論文 etd-0530112-102008 詳細資訊
Title page for etd-0530112-102008
Volatility Forecasting of Crude Oil Future-Under Normal Mixture Model and NIG Mixture Model
Year, semester
Number of pages
Ming-chi Chen
Advisory Committee
Chen-chang Lee
Date of Exam
Date of Submission
Normal Mixture, NIG Mixture, GARCH, Volatility forecasting
本論文已被瀏覽 5788 次,被下載 315
The thesis/dissertation has been browsed 5788 times, has been downloaded 315 times.
本研究嘗試以常態混合GARCH模型( Normal Mixture GARCH Model)及NIG混合GARCH模型(Normal-inverse Gaissian Mixture GARCH Model)捕捉商品期貨市場之波動度行為。常態混合GARCH模型(以下簡稱為NM-GARCH模型)為一由二至數個常態分配以特定權重(mixing law)混合而成之模型,其變異數符合GARCH過程。NM-GARCH對於大部分具有高峰、厚尾現象之財務資料捕捉能力應較一般常態GARCH模型和Student’s t GARCH模型為佳。另外,NM-GARCH中權重較低之組成成分之變異數通常較高,而權重較高之組成成分波動度較小,說明了現實經濟情況中,大波動(shock)發生機率較小,小波動發生機率較高的現象,即在一般情況中不斷發生之波動幅度較平緩,較大的衝擊雖然幅度大,但較少發生。
This study attempts to capture the behavior of volatility in the commodity futures market by importing the normal mixture GARCH Model and the NIG mixture GARCH model (Normal-inverse Gaussian Mixture GARCH Model). Normal mixture GARCH Model (what follows called NM-GARCH Model) is a model mixed by two to several normal distributions with a specific weight portfolio, and its variance abide by GAECH process. The ability of capturing the financial data with leptokurtosis and fat-tail of NM-GARCH Model is better than Normal GARCH Model and Student’s t GARCH Model.。Also,The Variance of the factor with lower weight in NM-GARCH Model usually higher, and the volatility of the factor with higher weight is lower, which explains the situation happens in the real market that the probability of large fluctuations (shocks) is small, and the probability of small fluctuations are higher. Generally, the volatilities which keeping occurring in common cases are respectively flat, and the shocks usually bring large impacts but less frequent.
NIG Mixture Distribution is a distribution mixed by two to several weighted distributions, and the distribution of every factor abides by NIG Distribution. Compare to Normal Mixture Distribution, NIG Mixture Distribution takes the advantages of NIG Distribution into account, which can not only explain leptokurtosis and the deviation of data, but describe the fat-tail phenomenon more complete as well, because of the both tails of NIG Distribution decreasing slowly.
This study will apply the NM GARCH Model and NIG GARCH Model to the Volatility forecasting of the return rates in the crude oil futures market, and infer the predictive abilities of this two kinds of models are significantly better than other volatility model by implementing parameter estimation, forecasting, loss function and statistic significant test.
目次 Table of Contents
Abstract ii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Purpose of Research, Process and Structure 4
Chapter 2 Literature Review 7
2.1 Volatility Models and The Empirical Results 7
Chapter 3 Methodology 15
3.1 The Characteristics and Definitions of Each Probability Density Function 15
3.2 Data Analysis 23
3.3 Model Selection 34
Chapter 4 Empirical Research 38
4.1 Research Process and Structure 38
4.2 Parameters Estimation 44
4.3 The Size of Volatility and Probability 47
4.4 Forecasting and Performance Evaluation 50
Chapter 5 Conclusion and Recommendations 54
Reference 57

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