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Methodical And Applicable Research About Dynamic Correlation Of Financial Assets

Posted on:2008-05-08Degree:DoctorType:Dissertation
Country:ChinaCandidate:L ZhangFull Text:PDF
GTID:1119360242479158Subject:Financial engineering
Abstract/Summary:PDF Full Text Request
Modeling the temporal dependence in the second order moments and forecasting future volatility have key relevance in many financial econometric issues such as risk evaluation, derivatives pricing, and optimal portfolio choice. We review different specifications of dynamic correlation. They differ in various aspects. We distinguish three approaches for constructing correlation models: (1) multivariate time series; (2) multivariate stochastic process; (3) copula theory.The dissertation includes five sections. After the brief introduction (chapter one), in chapter two, the dissertation reviews the overseas development of multivariate correlation, and then comments the advantage, disadvantage and applicability of different models. Then from chapter three to chapter four, the dissertation uses the different multivariate GARCH model, including ADCC, CCC, and Riskmetrics method, to study the forecast and risk management of Chinese Indices Portfolio. The empirical results show that: ADCC multivariate GARCH model is best among them. This result offers theoretic support to portfolio and risk management.Chapter five presents a new approach to the modeling of the conditional correlation matrix in the large cross-sectional dimension based on Cholesky method. Many multivariate GARCH models have been developed in the recent years to model the conditional second moments. However, all of them must make the trade-off between parameters' parsimony and richness in the description of the second order moments dynamics. In fact, the number of parameters of a fairly rich multivariate volatility model soon becomes large enough to render estimation infeasible. The key feature of the SCC based on Cholesky is the decomposition of the conditional correlation matrix into the product of a sequence of matrices with desirable characteristics. Then a highly dimensional and intractable optimization problem is converted into a series of simple and feasible estimations. Moreover, the latest methods dealing with two dimensional correlation matrix can combine with SCC to solve complex problems. In a word, the dissertation studies forecast of dynamic correlation and application in financial risk. And then the dissertation introduces SCC method based on Cholesky decomposition to handle a highly dimensional correlation matrix and gives theory result.
Keywords/Search Tags:multivariate GARCH, SCC method, forecast and risk
PDF Full Text Request
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