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The Evaluation Of Risk Regarding Insurance. Statistical Methods Of Risk Dissipation

Author

Listed:
  • Mihai Aristotel Ungureanu

    (“Romanian American” University of Bucharest, Romania)

  • Mihaela Gruiescu

    (“Romanian American” University of Bucharest, Romania)

  • Corina Ioanăş

    (Academy of Economic Studies, Bucharest, Romania)

  • Dragoş Dan Morega

    (Romanian Academy, Bucharest, Romania)

Abstract

Value at risk (VaR) is a summary statistic that quantifies the exposure of an asset or portfolio to market risk. Value at risk is now viewed by many as indispensable ammunition in any serious corporate risk manager’s arsenal. VaR is often used as an approximation of the maximum reasonable loss a company can expect to realize from all its financial exposures. The purpose of any risk measurement system and summary risk statistic is to facilitate risk reporting and control decision. VaR certainly is not the only way a firm can systematically measure its financial risk. But, its appeal is mainly its conceptual simplicity and its consistency across financial products and activities.

Suggested Citation

  • Mihai Aristotel Ungureanu & Mihaela Gruiescu & Corina Ioanăş & Dragoş Dan Morega, 2010. "The Evaluation Of Risk Regarding Insurance. Statistical Methods Of Risk Dissipation," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 10(2), pages 353-362.
    • Handle: RePEc:pet:annals:v:10:y:2010:i:2:p:353-362
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    Citations

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    Cited by:

    1. Emilia Clipici, 2012. "Solvency Ii – The New Eu Solvency Regime On The Insurance Market," Scientific Bulletin - Economic Sciences, University of Pitesti, vol. 11(2), pages 112-119.

    More about this item

    Keywords

    insurance; value at risk; risk management; risk statistic;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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