| dc.contributor.advisor | 陳威光<br>林靖庭 | zh_TW |
| dc.contributor.advisor | Chen , Wei Kuang<br>Lin, Ching Ting | en_US |
| dc.contributor.author (Authors) | 劉正傑 | zh_TW |
| dc.contributor.author (Authors) | Liu, Cheng Chieh | en_US |
| dc.creator (作者) | 劉正傑 | zh_TW |
| dc.creator (作者) | Liu, Cheng Chieh | en_US |
| dc.date (日期) | 2016 | en_US |
| dc.date.accessioned | 1-Jul-2016 15:01:30 (UTC+8) | - |
| dc.date.available | 1-Jul-2016 15:01:30 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Jul-2016 15:01:30 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0103352027 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/98572 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融學系 | zh_TW |
| dc.description (描述) | 103352027 | zh_TW |
| dc.description.abstract (摘要) | 從VaR概念興起之後，風險值已經成為估計風險的重要指標，而從VaR到日內風險值(IVaR)，風險值的估計越來越精細。雖然風險值在估計大多數商品的隔夜風險值上有顯著的效果，但隨著現代化交易方式的進步，流動性的風險因素在實際交易時往往占有顯著的地位。因此只有估計價格的變動並不足以應付現實的問題。故本文在原有的日內風險值基礎上加上流動性的因素，並比較加入流動性風險的效益。本篇論文引進了布達佩斯交易所的流動性計算方式，並以此計算台股期貨風險值的定義方式。首先我們以此計算流動性風險在日內風險值中所佔的比率，並分析不同交易量的股票期貨的比率。本文也藉由回溯測試計算出實際報酬率超過流動性風險值的次數，並將之定義為穿透事件。在計算這個穿透的結果中，我們分成沒有流動性的日內風險值及有流動性日內風險值。本文於是藉由概似比率檢定檢驗實際穿透機率是不是能符合模型設定的理論穿透機率，並以此判斷日內風險值會不會有低估風險的情況。本文發現在六個股票期貨中，流動性風險比率最低的是台積電，約佔日內風險值的1.1%、而最高的台達電約佔了14%。在穿透比率的部分，我們發現不加入流動性的日內風險值明顯會低估實際上的風險。反之，流動性日內風險值雖然在高穿透機率時會高估風險，在低穿透機率的尾端事件上卻能有效的估計風險值。 | zh_TW |
| dc.description.abstract (摘要) | With the development of Value-at-Risk(VaR), it has become an important indicator of risk estimation. We define more and more delicate model from VaR to Intraday VaR(IVaR) , even though these indicators do work significantly on overnight-risk estimation of most of products, the liquidity risk is the neglected titanic players on daily trading mechanics. To be more realistic, we modify the old IVaR model by adding liquidity factor, and compare the effect.This paper introduces the liquidity computation algorithm from Budapest Stock Exchange, and proffers a definition of liquidity-adjusted IVaR (LIVaR) of Taiwan futures markets. We first compute the percentage of liquidity risk out of IVaR, finding out the difference between six stock futures that have their own trading volume. Through the back-testing, we calculate the times real rate of return exceeds LIVaR. With the result, we test the performances of this indicator by implying LR-test.We find out that in these six stock futures, the percentages of liquidity go from lowest 1.1% (FRF) to highest 14%(CDF). On the part of percentage of failures, we do realize that computing IVaR without liquidity factor will under-estimate the realistic risk, which can be proved by rejecting the alternative hypothesis. On the other hand, even though LIVaR will over-estimate the risk on high percentage of failures event, it works significantly well on tail event with low failures rate. | en_US |
| dc.description.tableofcontents | 一、緒論 11.1 研究背景與動機 11.2 研究目的 2二、文獻回顧 32.1 日內風險值 32.2 流動性風險 5三、研究方法 73.1 研究資料 73.2 理論模型 83.2.2 Tick-by-Tick報酬 113.2.3 流動性風險 113.3 比較與檢驗 163.3.1 流動性風險溢值 163.3.2 回溯測試 173.4 模擬方式 18四、模擬結果 204.1 流動性風險溢值結果 204.2 回溯測試結果 23五、結論 30附表 32參考文獻 37 | zh_TW |
| dc.format.extent | 980778 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0103352027 | en_US |
| dc.subject (關鍵詞) | 風險管理 | zh_TW |
| dc.subject (關鍵詞) | 日內風險值 | zh_TW |
| dc.subject (關鍵詞) | 流動性風險 | zh_TW |
| dc.subject (關鍵詞) | Risk Management | en_US |
| dc.subject (關鍵詞) | Intraday Value-at-Risk | en_US |
| dc.subject (關鍵詞) | Liquidity Risk | en_US |
| dc.title (題名) | 期貨日內風險之估計:加入流動性之影響 | zh_TW |
| dc.title (題名) | Liquidity-Adjusted Intraday Value-at-Risk : Evidence from Taiwan Futures Markets | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | Dionne, Georges, Pierre Duchesne, and Maria Pacurar. "Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange." Journal of Empirical Finance 16.5 (2009): 777-792.Engle, Robert F., and Jeffrey R. Russell. "Autoregressive conditional duration: a new model for irregularly spaced transaction data." Econometrica (1998): 1127-1162.Groß‐KlußMann, Axel, and Nikolaus Hautsch. "Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models." Journal of Forecasting 32.8 (2013): 724-742.Gyarmati, Ákos, Márton Michaletzky, and Kata Váradi. "The Budapest Liquidity Measure and its Application Liquidity Risk in VaR Measures." (2011).Dionne, Georges, Maria Pacurar, and Xiaozhou Zhou. "Liquidity-adjusted Intraday Value at Risk modeling and risk management: An application to data from Deutsche Börse." Journal of Banking & Finance 59 (2015): 202-219.Qi, Jun, and Wing Lon Ng. "Liquidity adjusted intraday value at risk." Proceedings of the World Congress on Engineering. Vol. 2. (2009):1-7.eas Heinen, Andr. "Modeling Time Series Count Data: An Autoregressive Conditional Poisson Model." (2000).Liu, Shouwei, and Yiu-Kuen Tse. "Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach." Journal of Econometrics 189.2 (2015): 437-446.Hurlin, Christophe, Gilbert Colletaz, and Sessi Tokpavi. "Irregularly spaced intraday value at risk (ISIVaR) models: Forecasting and predictive abilities." (2007).Engle, Robert F., and Simone Manganelli. "CAViaR: Conditional autoregressive value at risk by regression quantiles." Journal of Business & Economic Statistics 22.4 (2004): 367-381. | zh_TW |
