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Calculate The Financial Risk According To Extreme Value Theory

Posted on:2006-11-19Degree:MasterType:Thesis
Country:ChinaCandidate:W L GuiFull Text:PDF
GTID:2166360155470694Subject:Quantitative Economics
Abstract/Summary:PDF Full Text Request
Past several decades, the multifarious turbulence of the financial market let people's eyes shocking. People are concentrating on the research of the operation mechanism of the financial market in-depth, at the same time enhancing the dint degree of the direct finance management. Financial Risk Calculates Theory, Portfolio Theory and Asset Pricing Theory established the theoretical sill of management of modern finance. Three come down in one continuous line inseparably. Empress both regards the former as the foundation. The creation of each problem with valid solution of financial risk calculates will produce the profound influence to them. Regard this as the foundation, this research overview the main problem with solution including the "fat tail" distribution of financial asset return, the term of extreme value distribution, integrality of distribution and the extreme value distribution of the portfolio etc. The point is two ex- a problem thorough summary with valid solution. Used for the fulfillment research of the Chinese stock market is absolutely necessarily. Concretely, under the two frames of the theories model of the block sample maximum and POT(peaks over threshold), the article is laid emphasis in the estimate of the model parameter. The former since include maximum likelihood estimate of the model parameter to still include maximum likelihood point estimate of the model parameter and confidence intervals estimate of the profile log-likelihood etc. The latter since include maximum likelihood estimate of the GPD(generalized Pareto distribution ) model parameters to still include moment estimate etc. In moment estimate, we joined together the fulfillment of the moment estimate of the shape parameter1/a , analyzed the reason and the term of computing an and two rank moment estimates only in fulfillment. Finally, we present arithmetic flow chart of Hall twi-bootstrap to calculate the quantiles and the programme to generate random numbers. In the beginning, we intend to match logarithms rate of return of the composition index of Shenzhen withthe GARCH(1,1) model in the empirical research. Then we match the standardized residual with GPD. On the foundation of calculating the quantile of standardized residual and logarithms rate of return, we test the model with disobedient numbers which obey binomial distribution. The test results show the model the good effect of estimate and forecast.
Keywords/Search Tags:EVT, Extreme Value Theory, Binomial Distribution, Bootstrap, GARCH Model, Generalized Autoregressive Conditional Heteroskedasticity Model, GPD, Generalized Pareto Distribution, MLE, Maximum Likelihood Estimate
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