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- W280581976 abstract "The methodology of Value-at-Risk (VaR), a measure of risk of loss that has been predominantly used by banks and investment companies since the 1987 stock market crash, has been used for 20 years and is now a widely accepted risk metric for investments. However, this methodology has been taken at face value for too long by many institutions that focus on the ‘normal’ manageable risks near the center of the distribution and tend to accept the extreme tail values as unmanageable risk. With recent events that have occurred in the stock market, there has been the need to introduce a compelling need for market participants to measure their portfolio’s risk with consideration to market conditions that surround them. It is important to understand the true nature of the beasts that may exist in portfolios by understanding what tools are available to help better describe the true risks associated with the current market conditions. Studies [Black, F. and M. Scholes, Ellis, Fama, Markowitz] suggest that conducting statistical analysis on historical data allows the development of estimators of risk which are conditional on economic conditions. Using simulation with the combinations of the most relevant market distributions, it is possible to compare the performance of three different VaR computations: 1) the standard VaR calculation which assumes a normal distribution, 2) Conditional Value-at-Risk (CVaR) , which is the average loss subject to the condition that the loss exceeds VaR and 3) a method using extreme value copulas (Copulas formulate a multivariate distribution using various general types of dependence). A best practice for VaR methodology can then be identified by comparing the theoretical VaR of a original data set with the three estimated VaRs from a sample data set. In summary, this project’s methodology consists of the following steps. First, a study is conducted of the accuracy of three VaR computations based on different assumptions. Then, the best method is selected for a variety of dependence structures market conditions, providing investors a more realistic view of the overall risks to assets being held in the marketplace." @default.
- W280581976 created "2016-06-24" @default.
- W280581976 creator A5037919937 @default.
- W280581976 date "2009-01-01" @default.
- W280581976 modified "2023-09-26" @default.
- W280581976 title "Risk Metric Allocation Methodology in Financial Markets" @default.
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- W280581976 doi "https://doi.org/10.2139/ssrn.2103310" @default.
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