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Value-at-risk analysis of portfolio return model using independent component analysis and Gaussian mixture model.

Sen Sui. / Thesis submitted in: August 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 88-92). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.iv / Dedication --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objective --- p.1 / Chapter 1.2 --- Contributions --- p.4 / Chapter 1.3 --- Thesis Organization --- p.5 / Chapter 2 --- Background of Risk Management --- p.7 / Chapter 2.1 --- Measuring Return --- p.8 / Chapter 2.2 --- Objectives of Risk Measurement --- p.11 / Chapter 2.3 --- Simple Statistics for Measurement of Risk --- p.15 / Chapter 2.4 --- Methods for Value-at-Risk Measurement --- p.16 / Chapter 2.5 --- Conditional VaR --- p.18 / Chapter 2.6 --- Portfolio VaR Methods --- p.18 / Chapter 2.7 --- Coherent Risk Measure --- p.20 / Chapter 2.8 --- Summary --- p.22 / Chapter 3 --- Selection of Independent Factors for VaR Computation --- p.23 / Chapter 3.1 --- Mixture Convolution Approach Restated --- p.24 / Chapter 3.2 --- Procedure for Selection and Evaluation --- p.26 / Chapter 3.2.1 --- Data Preparation --- p.26 / Chapter 3.2.2 --- ICA Using JADE --- p.27 / Chapter 3.2.3 --- Factor Statistics --- p.28 / Chapter 3.2.4 --- Factor Selection --- p.29 / Chapter 3.2.5 --- Reconstruction and VaR Computation --- p.30 / Chapter 3.3 --- Result and Comparison --- p.30 / Chapter 3.4 --- Problem of Using Kurtosis and Skewness --- p.40 / Chapter 3.5 --- Summary --- p.43 / Chapter 4 --- Mixture of Gaussians and Value-at-Risk Computation --- p.45 / Chapter 4.1 --- Complexity of VaR Computation --- p.45 / Chapter 4.1.1 --- Factor Selection Criteria and Convolution Complexity --- p.46 / Chapter 4.1.2 --- Sensitivity of VaR Estimation to Gaussian Components --- p.47 / Chapter 4.2 --- Gaussian Mixture Model --- p.52 / Chapter 4.2.1 --- Concept and Justification --- p.52 / Chapter 4.2.2 --- Formulation and Method --- p.53 / Chapter 4.2.3 --- Result and Evaluation of Fitness --- p.55 / Chapter 4.2.4 --- Evaluation of Fitness using Z-Transform --- p.56 / Chapter 4.2.5 --- Evaluation of Fitness using VaR --- p.58 / Chapter 4.3 --- VaR Estimation using Convoluted Mixtures --- p.60 / Chapter 4.3.1 --- Portfolio Returns by Convolution --- p.61 / Chapter 4.3.2 --- VaR Estimation of Portfolio Returns --- p.64 / Chapter 4.3.3 --- Result and Analysis --- p.64 / Chapter 4.4 --- Summary --- p.68 / Chapter 5 --- VaR for Portfolio Optimization and Management --- p.69 / Chapter 5.1 --- Review of Concepts and Methods --- p.69 / Chapter 5.2 --- Portfolio Optimization Using VaR --- p.72 / Chapter 5.3 --- Contribution of the VaR by ICA/GMM --- p.76 / Chapter 5.4 --- Summary --- p.79 / Chapter 6 --- Conclusion --- p.80 / Chapter 6.1 --- Future Work --- p.82 / Chapter A --- Independent Component Analysis --- p.83 / Chapter B --- Gaussian Mixture Model --- p.85 / Bibliography --- p.88

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324773
Date January 2004
ContributorsSui, Sen., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, xi, 92 leaves : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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