By Kevin Dowd

This ebook presents an advent to worth in danger (VaR) and anticipated tail loss (ETL) estimation and is a student-oriented model of Measuring industry Risk (John Wiley & Sons 2002).

An advent to industry danger Measurement comprises insurance of:

  • Parametric and non-parametric possibility estimation

  • Simulation

  • Numerical equipment

  • Liquidity dangers

  • Risk Decomposition and Budgeting

  • Backtesting

  • Stress checking out

  • Model hazard
Divided into elements, half one discusses many of the possibility size thoughts, when half offers a toolkit of the most instruments required to appreciate industry probability size. A CD is packaged with the publication, containing a MATLAB folder of possibility dimension capabilities, as well as a few examples in Excel/VBA.

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Sample text

8 These parameters imply that the VaR will be exceeded in only about one 10-day period in every four years. This should lead to a very low probability of failure, because the capital requirement itself is at least three times the VaR. Our estimated failure probability will then depend on what we assume about the P/L distribution. 25% per year, and less if we have a multiplier of greater than 3. This means that our institutions should be pretty safe — unless, like Barings or LTCM, they have a poor risk measurement model.

3 See Ingersoll (1987, p. 139) or Yoshiba and Yamai (2001, p. 8). 5) y is some small change in yield. , Fabozzi (2000, p. 66)). ) and, as such, they are useful, though limited, tools in the risk measurer’s armoury. , changes in bond price) to probabilities. If we are to use duration measures for risk measurement purposes in this sense, our best option is to derive the standard deviation of holding-period return and then feed that into a normal risk framework. 8) If we want a risk measure, the easiest step is to assume that bond prices are approximately normal and we can then work out the probabilities of specified gains or losses, and so forth.

Note: Produced using the ‘normalvarplot2D cl’ function. 8 VaR and holding period. Note: Produced using the ‘normalvarplot2D hp’ function. how the same VaR varies as we change the confidence level and keep other parameters constant. In this particular case, the VaR not only rises with the confidence level, but also rises at an increasing rate — a point that risk managers might care to note. We should also remember that the VaR is contingent on the choice of holding period as well, and so we should consider how the VaR varies with the holding period.

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