By Kevin Dowd

ISBN-10: 0470847484

ISBN-13: 9780470847480

This booklet offers an creation to worth in danger (VaR) and anticipated tail loss (ETL) estimation and is a student-oriented model of *Measuring marketplace Risk* (John Wiley & Sons 2002).

*An creation to industry threat Measurement* contains insurance of:

- Parametric and non-parametric possibility estimation
- Simulation
- Numerical equipment
- Liquidity hazards
- Risk Decomposition and Budgeting
- Backtesting
- Stress trying out
- Model hazard

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**Additional resources for An Introduction to Market Risk Measurement (The Wiley Finance Series)**

**Example text**

A single VaR ﬁgure can also give a misleading impression of relative riskiness: we might have two positions with equal VaRs at some given conﬁdence level and holding period, and yet one position might involve much heavier tail losses than the other. The VaR measure taken on its own would incorrectly suggest that both positions were equally risky. Fortunately, 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 were also estimated at very high conﬁdence levels.

For example, how likely is it that proﬁt (or loss) will be greater than, or less than, a certain amount? The others are questions about quantiles. We specify the probability, and then ask about the associated amount. For example, what is the maximum likely proﬁt (or loss) at a particular level of probability? 2. 645. 645. 645. 645. 645. 645. 645 can then be described as the value at risk (or VaR) of our portfolio at the 95% level of conﬁdence, and we will have more to say about this presently. The assumption that P/L is normally distributed is attractive for three reasons.

1) where X is deﬁned over −∞ < x < ∞. 1. This pdf tells us that outcomes are more likely to occur close to the mean µ. The spread of the probability mass around the mean depends on the standard deviation σ : the greater the standard deviation, the more dispersed the probability mass. The pdf is also symmetric around the mean: X is as likely to take a particular value x − µ as to take the corresponding negative value −(x − µ). Outcomes well away from the mean are very unlikely, and the pdf tails away on both sides: the left-hand tail corresponds to extremely low realisations of the random variable, and the righthand tail to extremely high realisations of it.

### An Introduction to Market Risk Measurement (The Wiley Finance Series) by Kevin Dowd

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