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

By Kevin Dowd(auth.)

Absolutely revised and restructured, Measuring industry probability, moment Edition contains a new bankruptcy on ideas possibility administration, in addition to tremendous new details on parametric probability, non-parametric measurements and liquidity dangers, more effective details to assist 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 danger (pages 19–52):
Chapter three Estimating marketplace probability Measures: An creation and assessment (pages 53–81):
Chapter four Non?parametric methods (pages 83–125):
Chapter five Forecasting Volatilities, Covariances and Correlations (pages 127–150):
Chapter 6 Parametric techniques (I) (pages 151–187):
Chapter 7 Parametric techniques (II): severe worth (pages 189–207):
Chapter eight Monte Carlo Simulation tools (pages 209–226):
Chapter nine purposes of Stochastic possibility size tools (pages 227–248):
Chapter 10 Estimating concepts chance Measures (pages 249–264):
Chapter eleven Incremental and part dangers (pages 265–277):
Chapter 12 Mapping Positions to hazard components (pages 279–290):
Chapter thirteen rigidity trying out (pages 291–307):
Chapter 14 Estimating Liquidity dangers (pages 309–320):
Chapter 15 Backtesting marketplace possibility versions (pages 321–349):
Chapter sixteen version probability (pages 351–363):

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Sample text

Monotonicity means that a random cash flow or future value Y that is always greater than X should have a lower risk: this makes sense, because it means that less has to be added to Y than to X to make it acceptable, and the amount to be added is the risk measure. Positive homogeneity implies that the risk of a position is proportional to its scale or size, and makes sense if we are dealing with liquid positions in marketable instruments. Translational invariance requires that the addition of a sure amount reduces pari passu the cash needed to make our position acceptable, and is obviously valid when one appreciates that the cash needed is our risk measure.

Recall that any statistical distribution can be described in terms of its moments or moment-based parameters such as mean, standard deviation, skewness and kurtosis. 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.

In this particular case, which is also quite common in practice, 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. As the VaR is also contingent on the holding period, we should consider how the VaR varies with the holding period as well. 8, which plots 95% VaRs based on two alternative μ values against a holding period that varies from 1 day to 100 days. With μ = 0, the VaR rises with the square root of the holding period, but with μ > 0, the VaR rises at a lower rate and would in fact eventually turn down.

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