Measuring Market Risk, Second Edition by Kevin Dowd(auth.)

By Kevin Dowd(auth.)

Totally revised and restructured, Measuring marketplace threat, moment Edition comprises a new bankruptcy on recommendations probability administration, in addition to titanic new info on parametric danger, non-parametric measurements and liquidity hazards, simpler details to aid with particular calculations, and new examples together with Q&A’s and case studies. Content:
Chapter 1 the increase of price in danger (pages 1–17):
Chapter 2 Measures of economic chance (pages 19–52):
Chapter three Estimating marketplace danger Measures: An advent and assessment (pages 53–81):
Chapter four Non?parametric ways (pages 83–125):
Chapter five Forecasting Volatilities, Covariances and Correlations (pages 127–150):
Chapter 6 Parametric ways (I) (pages 151–187):
Chapter 7 Parametric methods (II): severe worth (pages 189–207):
Chapter eight Monte Carlo Simulation equipment (pages 209–226):
Chapter nine purposes of Stochastic chance dimension equipment (pages 227–248):
Chapter 10 Estimating recommendations probability Measures (pages 249–264):
Chapter eleven Incremental and part hazards (pages 265–277):
Chapter 12 Mapping Positions to hazard components (pages 279–290):
Chapter thirteen pressure trying out (pages 291–307):
Chapter 14 Estimating Liquidity dangers (pages 309–320):
Chapter 15 Backtesting industry threat types (pages 321–349):
Chapter sixteen version chance (pages 351–363):

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Extra resources for Measuring Market Risk, Second Edition

Sample text

In the case of the normal distribution, the mean and standard deviation can be anything (subject only to the constraint that the standard deviation can never be negative), and the skewness and kurtosis are 0 and 3. However, other distributions can have quite different skewness and/or kurtosis, and therefore have quite different shapes than the normal distribution, and this is true even if they have the same mean and standard deviation. 4 compares a normal distribution with a skewed one (which is in fact a Gumbel distribution).

15 Taleb was also critical of the tendency of some VaR proponents to overstate the usefulness of VaR. He was particularly dismissive of Philippe Jorion’s (1997) claim that VaR might have prevented disasters such as Orange County. Taleb’s response was that these disasters had other causes – especially, excessive leverage. As he put it, a Wall Street clerk would have picked up these excesses with an abacus, and VaR defenders overlook the point that there are simpler and more reliable risk measures than VaR.

7 VaR and confidence level Note: Produced using the ‘normalvarplot2D cl’ function. shows how VaR changes as underlying parameters change, and conveys a great deal of risk information. In this case, which is typical of many, the surface rises with both confidence level and holding period to culminate in a spike – indicating where our portfolio is most vulnerable – as both parameters approach their maximum values. 2 Determination of the VaR Parameters The use of VaR involves two arbitrarily chosen parameters – the confidence level and the holding period – but how do we choose the values of these parameters?

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