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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 財務金融學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70436
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor葉小蓁
dc.contributor.authorChia-Hung Yehen
dc.contributor.author葉家宏zh_TW
dc.date.accessioned2021-06-17T04:28:09Z-
dc.date.available2021-08-15
dc.date.copyright2018-08-15
dc.date.issued2018
dc.date.submitted2018-08-13
dc.identifier.citation1. A.Pagan (1996) The Econometrics of Financial Market, Journal of Empirical Finance.
2. D.Duffie and J.Pan (1997) An Overview of Value at Risk, The Journal of Derivatives. 3. I. Khindanova, S. Rachev and E. Schwartz (2001) Stable Modeling of Value at Risk, Mathematical and Computer Modeling. 4. J.H McCulloch (1986) Simple Consistent Estimators of Stable Distribution Parameters', Communications in Statistics, Simulation and Computation. 5. J.P Nolan (1996) Parameterizations and Modes of Stable Distributions, Statistics & Probability Letters. 6. J.P Nolan (1998) Multivariate Stable Densities as Functions of One Dimensional Projections, Journal of Multivariate Analysis. 7. RCont (2006) Volatility Clustering in Financial Markets :Empirical Facts and Agent–Based Models, Long Memory in Economics. 8. R.F. Engle (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica. 9. S.Rachev and S.Mittnik (2000) Stable Paretian Modeling, Series in Financial Economics and Quantitative Analysis. 10. T.Bollerslev (1986) Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics. 11. T.S. Beder (1995) VAR: Seductive but Dangerous, CFA Institute Financial Analysts Journal.
12. W.H DuMouchel (1971) On the Asymptotic Normality of Maximum Likelihood Estimates when Sampling from a Stable Distribution, The Analysis of Statistics.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70436-
dc.description.abstract本文參考I. Khindanova, S. Rachev and E. Schwartz (2001)所提出的論點:認為金融市場的資料序列普遍存在厚尾的現象。因此,在傳統的高斯分配的假設之下,極端事件發生的機率容易被低估,亦造成風險值 (Value-at-Risk, VaR)的估算過於樂觀。本文驗證台灣的金融市場亦普遍存在厚尾的現象,並參考I. Khindanova, S. Rachev and E. Schwartz (2001)中所述的α-穩定分配模型配適台灣金融市場的實際資料,期望能解決錯估尾端機率而造成的資產價值損失。zh_TW
dc.description.abstractThe value-at-risk measurement (VaR) is an important indicator when a financial institution wishes to evaluate its exposure to market risks. Referring to I. Khindanova, S. Rachev and E. Schwartz (2001), the traditional Gaussian estimation of VaR generally overlooks the heavy-tailed properties exhibited financial data series, thereby incurring unexpected losses under extreme circumstances. This paper follows I. Khindanova, S. Rachev and E. Schwartz (2001) to show that heavy-tailed properties exist for Taiwan’s financial market as well and aims to prove that this issue can be effectively solved by fitting the financial data to an α-stable distribution.en
dc.description.provenanceMade available in DSpace on 2021-06-17T04:28:09Z (GMT). No. of bitstreams: 1
ntu-107-R05723062-1.pdf: 2772651 bytes, checksum: 1f379f261966b05844a10251d7e994f9 (MD5)
Previous issue date: 2018
en
dc.description.tableofcontents中文摘要 ........................................................................................................................... I
ABSTRACT ................................................................................................................... II
LIST OF FIGURES ...................................................................................................... IV
LIST OF TABLES .......................................................................................................... V
1. INTRODUCTION .................................................................................................. 1
2. LITERATURE REVIEW ...................................................................................... 3
2.1 DEFINITION AND IMPORTANCE OF VALUE AT RISK (VAR) .................................. 3
2.2 TRADITIONAL COMPUTATION OF VAR ................................................................. 4
2.3 STABLE MODELING OF VAR ................................................................................ 10
2.4 COMPARISON OF TRADITIONAL AND STABLE MODELING .................................. 15
2.5 RESEARCH QUESTIONS ........................................................................................ 17
3. DATA, METHODOLOGY, AND EXPERIMENTAL FRAMEWORK ......... 17
3.1 DATA DESCRIPTION ............................................................................................. 17
3.2 METHODOLOGIES AND DISCUSSION .................................................................... 19
3.3 EXPERIMENTAL FRAMEWORK ............................................................................. 24
4. EMPIRICAL RESULTS AND FINDINGS ....................................................... 31
4.1 ARE HEAVY-TAILS IDENTIFIABLE IN TAIWAN’S FINANCIAL MARKET?............ 31
4.2 COMPARISON OF STABLE MODELING AND TRADITIONAL MODELING .............. 35
4.3 VAR CALCULATION UNDER Α-STABLE DISTRIBUTION ...................................... 41
5. CONCLUSION ..................................................................................................... 55
REFERENCE ............................................................................................................... 56
dc.language.isoen
dc.subjectα-穩定分配zh_TW
dc.subject風險值zh_TW
dc.subject厚尾zh_TW
dc.subject機率分布配適zh_TW
dc.subjectHeavy-Tailen
dc.subjectStable Paretian Modelingen
dc.subjectVaRen
dc.subjectα-Stable Distributionen
dc.title穩定分配模型的風險值計算 -以台灣指數與外匯市場之資料為佐證zh_TW
dc.titleComputing Value at Risk Under α-Stable Distributions -Empirical Evidence from Taiwan’s Financial Marketen
dc.typeThesis
dc.date.schoolyear106-2
dc.description.degree碩士
dc.contributor.oralexamcommittee石百達,許耀文
dc.subject.keyword風險值,厚尾,α-穩定分配,機率分布配適,zh_TW
dc.subject.keywordVaR,Heavy-Tail,Stable Paretian Modeling,α-Stable Distribution,en
dc.relation.page56
dc.identifier.doi10.6342/NTU201803250
dc.rights.note有償授權
dc.date.accepted2018-08-14
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept財務金融學研究所zh_TW
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