| DC 欄位 |
值 |
語言 |
| DC.contributor | 統計研究所 | zh_TW |
| DC.creator | 曹展誌 | zh_TW |
| DC.creator | Jhan-Jhih Cao | en_US |
| dc.date.accessioned | 2009-6-22T07:39:07Z | |
| dc.date.available | 2009-6-22T07:39:07Z | |
| dc.date.issued | 2009 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=952205016 | |
| dc.contributor.department | 統計研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 近年來，風險管理成為一門重要的議題，而風險值乃是用來衡量市場風險的一個指標。本文將會比較由 Delta-Noraml 近似法、Delta-Gamma 近似法、蒙地卡羅模擬法、歷史模擬法所計算出之風險值之準確性與保守性，進一步配合有母數及無母數的 Bootstrap 重複抽樣法，試圖提升所估計之風險值的準確性。選定台灣的股票市場作為實證研究的對象，自台灣經濟新報取得12檔股票兩年的歷史收盤價，透過文中提及之方法來計算風險值，進一步評估各方法的準確性與保守性。
在估計風險值的過程中，波動性的估計是必要的，在此使用兩種方法：均等權重移動平均法和加權指數移動平均法。在針對 DVEC-GARCH 模型的模擬研究結果中，這兩種波動性估計方法沒有明顯的差異；然而，實證研究的結果卻顯現出使用指數加權移動平均法估計波動性的方法準確性較高，較能符合實際情況。普遍來說，有使用 Bootstrap 重複抽樣法來計算風險值的方法會比沒有使用的來的好，且就平均相對偏差（mean relative bias, MRB）而言，使用無母數 Bootstrap 重複抽樣法的估計法會顯得保守許多，所以本文最後建議使用者在本文模型下採用無母數 Bootstrap 重複抽樣法配合Delta-Normal 近似法來估計風險值。
| zh_TW |
| dc.description.abstract | In recent years, risk management has become an important topic, and value at risk (VaR) is a powerful tool for assessing market risk. This research adopts four methods, including the delta-noraml approximation, the delta-gamma approximation, the Monte Carlo simulation method and the historical simulation method to obtain the measures of accuracy and conservatism. Together with parametric and nonparametric bootstrap resampling methods, we further make valid improvements in estimation of VaR. An empirical study applying historical stock closing prices of twelve Taiwan companies over the past two years is provided to compare the performance of each approach.
In the process of estimating VaR, two methods, equally weighted moving average and exponentially weighted moving-average (EWMA), are applied to estimate the volatility, which is necessary. According to simulation studies with data generated based on the DVEC-GARCH model, there does not appear to be signi?cant difference in outcomes between both. However, empirical studies show that estimating volatility via the EWMA method achieves higher accuracy and applicability. In general, it is improved to employ the bootstrap resmalping method, and in view of the mean relative bias (MRB), we have a more conservative conclusion with a nonparametric bootstrap method. As a result, using the delta-normal approximation method to estimate VaR along with nonparametric bootstrapping is recommendable in this research.
| en_US |
| DC.subject | DVEC-GARCH模型 | zh_TW |
| DC.subject | Delta-Gamma 近似法 | zh_TW |
| DC.subject | Delta-Normal 近似法 | zh_TW |
| DC.subject | 風險值 | zh_TW |
| DC.subject | 蒙地卡羅模擬法 | zh_TW |
| DC.subject | Bootstrap 重複抽樣法 | zh_TW |
| DC.subject | 歷史模擬法 | zh_TW |
| DC.subject | value at risk | en_US |
| DC.subject | VaR | en_US |
| DC.subject | delta-normal approximation | en_US |
| DC.subject | delta-gamma approximation | en_US |
| DC.subject | Monte Carlo simulation | en_US |
| DC.subject | historical simulation | en_US |
| DC.subject | bootstrap method | en_US |
| DC.subject | DVEC-GARCH model | en_US |
| DC.title | 在DVEC-GARCH模型下風險值的計算與實證研究 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Evaluation and Empirical Study for Value at Risk Based on the DVEC-GARCH Model | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |