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Mathematical methods of market risk valuation in application to Russian stock market

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  • Andrey M. Boyarshinov

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    (Computational Mathematics and Mechanics Perm State Technical University)

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    Abstract

    This work is dedicated to comparative analysis and estimation of quantitative methods of risk valuation in application to Russian stock market, which include historical simulation, exponentially-weighted historical simulation, variance-covariance models with adaptive covariance matrix, variance-covariance models with exponentially-weighted adaptive covariance matrix, models based on GARCH(1,1), and Monte-Carlo Models. For the purpose of implementation of these models, algorithms of risk valuation have been developed on the basis of Value-at-Risk methodic as one of the most widely used and in accordance to RiskMetrics standards. Algorithms are implemented with usage of development environment of specialized decision support system software “Prognoz. Market risk†based on “Prognoz†analytical suite. The Decision support system mentioned above allows using different methods of risk measures calculation including standard and complex non-trivial methods. It also provides a capability to be individually tuned to better suit users’ requirements. For the purpose of backtesting of developed algorithms market risk measures were calculated using open data from Russian stock market (MICEX). For mentioned risk measures figures of quality and effectiveness were calculated, including average VaR exception value, average uncovered losses to VaR ratio, maximum loss to VaR ratio, average unused reserves, and multiplier to obtain coverage. Acquired results allowed distinguishing models which are insufficiently adequate if used in current situation on Russian stock market: models which use exponentially weighted historical simulation and some of models using variance-covariance approach. Other models can be taken as adequate with the significance level of 1% and 5%

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    File URL: http://repec.org/sce2006/up.21415.1139935859.pdf
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    Bibliographic Info

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 127.

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    Date of creation: 04 Jul 2006
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    Handle: RePEc:sce:scecfa:127

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