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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李顯峰 | |
dc.contributor.author | Chen-Wen Yu | en |
dc.contributor.author | 余振文 | zh_TW |
dc.date.accessioned | 2021-06-17T09:09:31Z | - |
dc.date.available | 2024-11-04 | |
dc.date.copyright | 2019-11-04 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-10-15 | |
dc.identifier.citation | 中文文獻
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X., & Labys, P., (2001b), “The distribution of realized exchange rate volatility,” Journal of American Statistical Association, 96, No. 453, 42-57. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625. Beder, T. S. (1995). VaR: Seductive but dangerous. Financial Analysts Journal, 51(5), 12-24. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. Bollerslev, T., Gibson, M., & Zhou, H. (2011). Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities. Journal of Econometrics, 160(1), 235-245. Christensen, K., & Podolskij, M. (2007). Realized range-based estimation of integrated variance. Journal of Econometrics, 141(2), 323-349. Duffie, D., & Pan, J. (1997). An overview of value at risk. Journal of Derivatives, 4(3), 7-49. Ederington, L. H., & Guan, W. (2002). Is implied volatility an informationally efficient and effective predictor of future volatility?. Journal of Risk, 4(3), 29-46. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987-1007. Engel, J., & Gizycki, M. (1999). Conservatism, accuracy and efficiency: comparing value-at-risk models. APRA. Finzi, D., Blankson, J., Siliciano, J. D., Margolick, J. B., Chadwick, K., Pierson, T., ... & Quinn, T. C. (1999). Latent infection of CD4+ T cells provides a mechanism for lifelong persistence of HIV-1, even in patients on effective combination therapy. Nature Medicine, 5(5), 512. Forsberg, L., & Bollerslev, T. (2002). Bridging the gap between the distribution of realized (ECU) volatility and ARCH modelling (of the Euro): the GARCH‐NIG model. Journal of Applied Econometrics, 17(5), 535-548. Geweke, J., & Porter‐Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4(4), 221-238. Giot, P., & Laurent, S. (2004). Modelling daily value-at-risk using realized volatility and ARCH type models. Journal of Empirical Finance, 11(3), 379-398. Golub, B. W., & Tilman, L. M. (1997). Measuring yield curve risk using principal components analysis, value at risk, and key rate durations. Journal of Portfolio Management, 23(4), 72-84. Hoelscher, D. S., Taylor, M. W., & Klueh, U. H. (2006). The design and implementation of deposit insurance systems, International Monetary Fund, 251, Jacquier, E., Polson, N. G., & Rossi, P. E. (2002). Bayesian analysis of stochastic volatility models. Journal of Business & Economic Statistics, 20(1), 69-87. McNeil, A. J., & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7(3-4), 271-300. Melino, A., & Turnbull, S. M. (1990). Pricing foreign currency options with stochastic volatility. Journal of Econometrics, 45(1), 239-265. Morgan, J. P. & Reuters (1996). RiskMetrics Technical Document. Retrieved from the World Wide Web: www. jpmorgan. com Koopman, S. J., Jungbacker, B., & Hol, E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance, 12(3), 445-475. Kupiec, P. (1995). Techniques for verifying the accuracy of risk measurement models. The J. of Derivatives, 3(2). Poon, S. H., & Granger, C. W.( 2003), Forecasting volatility in financial markets: a review. Journal of Economic Literature, 41,478-539. Pan, J., & Poteshman, A. (2006). The information in option volume for future stock prices. The Review of Financial Studies., 19(3), 871–908. Pong, S., Shackleton, M. B., & Taylor, S. J. Xu, X., (2004). Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models. Journal of Banking Finance, 28, 2541-2563. Pong, S., Shackleton, M. B., & Taylor, S. J. (2008). Distinguishing short and long memory volatility specifications. The Econometrics Journal, 11(3), 617-637. Pritsker, M. (1997). Evaluating value at risk methodologies: accuracy versus computational time. Journal of Financial Services Research, 12(2-3), 201-242. Pritsker, M. (2006). The hidden dangers of historical simulation. Journal of Banking & Finance, 30(2), 561-582. Scherrer, U., Randin, D., Tappy, L., Vollenweider, P., Jequier, E., & Nicod, P. (1994). Body fat and sympathetic nerve activity in healthy subjects. Circulation, 89(6), 2634-2640. Yahoo Finance APIs:http://finance.yahoo.com/d/quotes.csv?s= | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74884 | - |
dc.description.abstract | 近二十年來,國際知名的金融機構相繼發生重大金融危機,如2009年次貸危機擴散傳遞速度比以往都快,80年代延續好幾年,90年代延續數個月,2007年9月15日到10月7日間倒了超過14家銀行,24個國家身受其害。為何如此快?主因是隨著衍生性金融商品不斷創新,金融機構所面臨的風險也跟著增加,一般而言,高報酬會帶來相對高風險,因此如何有效的監督與控制風險,政府當局,首要就是風險評估及風險管理。
以往研究對於波動度的測量,是以傳統波動度(標準差)來計算,本研究資料期間為2003年4月1日至2019年3月30日分鐘資料,透過算術平均數計算出5分鐘資料,採realized volatility) (將日內報酬率平方加總即得到波動度的估計值)並配合高頻資料,將已實現波動率當成外生變數應用在GARCH模型與AFRIMA模型。 比較各模型在樣本內適合度檢定、各模型樣本外預測績效。最後透過移動視窗分析法,從樣本外VaR預測以找出255天、510天及1000天,信賴水準在90%、95%與99%下,尋找最適合的參數,並進行回溯測試。 本研究驗證臺指期貨的已實現波動度具有緩長記憶的效果,在不同參數的ARFIMA(p, d, q)模型中,d值分別介於0.2779~0.483並顯著。在樣本內模型配適度的比較中,在AFRIMA模型中,AHT實施前以ARFIMA(1, 1)、AHT實施後以ARFIMA(2,2)為最配適模型;而樣本外預測績效。MSE、RMSE之中,以ARFIMA(1,2)、ARFIMA(2,2)模型誤差小,為最配適模型。 在樣本內模型配適度的比較中,在GARCH模型中,AHT實施前以GARCH(1,2)、AHT實施後以GARCH(1,1)為最配適模型;而樣本外預測績效。MSE、RMSE,MAE之中,以GARCH(1,1)模型誤差小,為最配適模型。 AHT制度實施前,信賴水準99%及95%下,不論是用ARFIMA模型或GARCH-RV模型皆無通過回溯測試,在AHT制度實施前僅有GARCH-RV在預測天期為255天、510天,信賴水準90%下,有通過回溯測試。 AHT制度實施後,在90%及95%的信賴水準下,不論是ARFIMA模型或GARCH-RV模型皆有通過回溯測式;在99%的信賴水準下,模型皆無法通過回溯測試。 | zh_TW |
dc.description.abstract | Different from previous research about volatility model. This study adopted realized volatility model combine high frequency data, the variance of realized volatility is an exogenous variable, that added the GARCH model and the AFRIMA model.
This study aims to investigate whether the after-hours trading (AHT) system the changes of the value at risk (VaR) of the Taiwan stock index futures before and after the AHT system opened. and be used to estimate the VaR with the window rolling method by four models which are ARFIMA and GARCH-RV models. The sample were retrieved 5 minutes in data from TAIFEX, and the research period is from Apr 1, 2003 to March 29, 2019, totally 306,760 observations. The whole data is divided into two periods which are the normal-trading time before the AHT system opened, the normal-trading time after the AHT system opened. In the empirical part, the real volatility model has a slow memory effect, For In- Sample-Test , ARFIMA(1, 1)、GARCH(1,2) are best fit model in time before the AHT system. ARFIMA(2,2) GARCH(1,1) are best fit model in time after the AHT system. For Out- Sample-Test, The standard error of the mean at ARFIMA(1,2)、ARFIMA(2,2) ,GARCH(1,1) are best fit model in MSE, RMSE. Further, under the confidence level of 99% and 95%, ARFIMA model or GARCH-RV model unacceptable back-testing in time before the AHT system. However ARFIMA model or GARCH-RV model acceptable back-testing at time before the AHT system in confidence level of 95% and 90% | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T09:09:31Z (GMT). No. of bitstreams: 1 ntu-108-P06323018-1.pdf: 1582764 bytes, checksum: 6ecfddc8902b60c0b58957999dbf93db (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 目錄
口試委員會審定書 ii 誌謝 iii 中文摘要 iv ABSTRACT v 目錄 vi 圖目錄 viii 表目錄 ix 第一章 緒論 1 1.1研究背景 1 1.2研究動機 1 1.3研究目的 2 1.4本文章節架構 3 第二章 文獻回顧 4 2.1波動率(Volatility)模型相關文獻 4 2.2風險值(Value at Risk) 衡量之相關文獻 7 2.2.1變異數-共變異數法 (Variance-covariance Approach)已 9 2.2.2歷史模擬法(Historical Simulation) 10 2.2.3蒙地卡羅模擬法(Monte Carlo Simulation) 11 第三章 研究資料與方法 15 3.1資料描述 15 3.2波動度的估計方法 15 3.2.1已實現波動度直接建立模型: 15 3.2.2已實現波動率間接建立模型: 18 3.3波動率預測值計算方法 19 3.4各種波動性模型間的預測力比較衡量誤差 20 3.5 VaR預測與評價方法 21 3.5.1波動預測值與VaR的計算方法 21 3.5.2 Kupiec LR檢驗 22 第四章 實證結果 24 4.1單根檢定(unit root test) 25 4.2各種模型的基本統計量 26 4.3不同參數ARFIMA模型的比較 29 4.3.1 ARFIMA模型樣本內適合度檢定 29 4.3.2 ARFIMA模型樣本外預測績效 30 4.4不同參數GARCH-RV模型的比較 31 4.4.1 GARCH-RV模型樣本內適合度檢定 31 4.4.2 GARCH-RV模型樣本外預測績效 32 4.5比較各模型風險值在樣本外預測精準度 33 第五章 結論與建議 35 5.1研究結論 35 5.2研究建議 36 參考文獻 38 | |
dc.language.iso | zh-TW | |
dc.title | 台灣指數期貨盤後交易開放對風險值的影響 | zh_TW |
dc.title | The Influence of the After-Hours Trading System Value at Risk of Taiwan Index Futures | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 謝德宗,林惠玲 | |
dc.subject.keyword | 風險值(VaR),盤後交易,已實現波動率,部份整合自我迴歸模型, | zh_TW |
dc.subject.keyword | Value at risk,After-hours trading system,ARFIMA,GARCH-RV, | en |
dc.relation.page | 41 | |
dc.identifier.doi | 10.6342/NTU201904204 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-10-16 | |
dc.contributor.author-college | 社會科學院 | zh_TW |
dc.contributor.author-dept | 經濟學研究所 | zh_TW |
顯示於系所單位: | 經濟學系 |
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