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VaR Models And Application Of VaR Technique In Portfolio Risk Management

Posted on:2005-07-09Degree:MasterType:Thesis
Country:ChinaCandidate:G X ZhangFull Text:PDF
GTID:2156360122966950Subject:Technical Economics and Management
Abstract/Summary:PDF Full Text Request
Recent financial disasters have emphasized the importance of effective risk management for financial institutions. The increased volatility of financial markets during the last decade has induced researchers, practitioners and regulators to develop more sophisticated risk management tools. Value at Risk (VaR) technique is a new risk management method that has been developed in 1990' s. It has become the standard measure of market risk employed by financial institutions and their regulators. It is an estimate of how much a certain portfolio can lose within a given time period and at a given confidence level.Beder(1995) applies eight common VaR methodologies to three hypothetical portfolios. The result shows that the differences among these methods can be very large. There is a need for a statistical approach to estimation and model selection. So, one of our main objectives of this paper is to survey the recent developments in VaR modeling. Firstly, I systematically discuss two types of latest developed VaR models. One is the EVT-based VaR model (including GEV model and GPD model), the other is the Quantile Regression VaR model. Secondly, I evaluate predictive performance of a selection of VaR models for Chinese stock market data. These VaR models include RiskMetrics method, historical simulation, Monte Carlo method, and the three recent models based on quantile regression and extreme value theory. I apply these six VaR models to the Chinese stock market index and compared their performance in terms of three various criteria.Because VaR methods can not only be used for information reporting, but also can be used for resource allocating and performance assessing to the whole institution. This process starts with adjusting returns for risk (RAROC). So the other main objective of this paper is to survey the application of VaR in portfolio risk management. In the last part of this paper, I choose investment funds in our stock market as empirical abject to test and verify how to apply VaR technique in portfolio risk management efficiently with some transaction data gained from domestic stock analytical software.
Keywords/Search Tags:Market Risk, VaR Technique, Extreme Value Theory, Quantile-Regression
PDF Full Text Request
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