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博碩士論文 etd-0708111-175020 詳細資訊
Title page for etd-0708111-175020
Forecasting Volatility for commodity futures using fat-tailed model
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leptokurtic, fat-tailed, volatility forecast, SGED
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This paper considers the high-moments and uses the skew generalized error distribution (SGED) to explain the financial market data which have leptokurtic, fat-tailed and skewness. And we compare performance with the commonly used symmetrical distribution model such as normal distribution, student’s t distribution and generalized error distribution (GED). To research when returns of asset have leptokurtic and fat-tailed phenomena, what model has better predictive power for volatility forecasting?
The empirical procedure is as follows: First step, make the descriptive statistics of raw data, and know that the GARCH effect should be considered, followed by selecting the optimal order of ARMA-GARCH. The second steps, make the parameter estimations of full-sample, and pick up the best model. Finally, forecast out-of-sample volatility for 1-day, 2-day, 5-day, 10-day and 20-day respectively, not only use different loss function to measure the performance, but also use DM test to compare the relative predictive power of the models under the different error distribution.
目次 Table of Contents
論文審定書 i
誌 謝 ii
摘 要 iii
Abstract iv
1. Introduction 1
1.1 Motivations 1
1.2 Importance of metal, oil and agricultural product markets 2
2. Literature Review 7
3. Methodology 12
3.1 Time-series forecasting 12
3.2 Student’s t Distribution 13
3.3 General Error Distribution 13
3.4 Skew Generalized Error Distribution 14
3.5 Forecasting methodology 15
4. Empirical results 19
4.1 Data and descriptive statistics 19
4.2 Order selection of ARMA-GARCH 23
4.3 Full sample estimation 28
4.4 Out-of-sample forecast evaluation 32
5. Conclusions 46
References 48
參考文獻 References
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