By Kevin Dowd(auth.)
Totally revised and restructured, Measuring industry danger, moment Edition contains a new bankruptcy on ideas chance administration, in addition to mammoth new details on parametric threat, non-parametric measurements and liquidity dangers, simpler info to assist with particular calculations, and new examples together with Q&A’s and case studies. Content:
Chapter 1 the increase of worth in danger (pages 1–17):
Chapter 2 Measures of economic danger (pages 19–52):
Chapter three Estimating marketplace hazard Measures: An advent and review (pages 53–81):
Chapter four Non?parametric methods (pages 83–125):
Chapter five Forecasting Volatilities, Covariances and Correlations (pages 127–150):
Chapter 6 Parametric ways (I) (pages 151–187):
Chapter 7 Parametric techniques (II): severe price (pages 189–207):
Chapter eight Monte Carlo Simulation equipment (pages 209–226):
Chapter nine purposes of Stochastic probability size equipment (pages 227–248):
Chapter 10 Estimating recommendations danger Measures (pages 249–264):
Chapter eleven Incremental and part dangers (pages 265–277):
Chapter 12 Mapping Positions to chance components (pages 279–290):
Chapter thirteen rigidity checking out (pages 291–307):
Chapter 14 Estimating Liquidity dangers (pages 309–320):
Chapter 15 Backtesting marketplace probability versions (pages 321–349):
Chapter sixteen version possibility (pages 351–363):
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Additional resources for Measuring Market Risk, Second Edition
They would operate on both organised exchanges and relatively liquid OTC (over-the-counter) markets. The sophistication of the instruments traded will vary enormously, from very simple vanilla instruments at one end to highly sophisticated instruments at the other. They would also operate with advanced market risk measurement systems, and they would typically value their positions (and so determine gains and losses) using mark-to-market valuation methodologies. In addition, these traders would often aim to beat benchmarks, although the type of benchmark used would depend on the market.
Of course, each of the last two ﬁgures only gives a partial view of the relationship between the VaR and the parameters on which it depends: the ﬁrst takes the holding period as given and varies the conﬁdence level, and the second varies the holding period while taking the conﬁdence level as given. To form a more complete picture, we need to see how VaR changes as we allow both parameters to change. 9, based here on a hypothetical assumption that μ = 0 – that enables us to read off the VaR for any given combination of these two parameters.
A VaR-based risk–return analysis can only be justiﬁed under conditions that are empirically usually too restrictive and/or a priori implausible. 10 We can sometimes ameliorate these problems by using more VaR information. For example, the trader who spikes his ﬁrm might be detected if the VaR of his position is estimated at a higher conﬁdence level as well. A (partial) solution to our earlier problems is, therefore, to look at more than one point on the VaR–conﬁdence level curve and not just to look at a single VaR ﬁgure.
Measuring Market Risk, Second Edition by Kevin Dowd(auth.)