An Introduction to Market Risk Measurement (The Wiley by Kevin Dowd

By Kevin Dowd

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 advent to industry chance Measurement comprises insurance of:

  • Parametric and non-parametric probability estimation

  • Simulation

  • Numerical tools

  • Liquidity dangers

  • Risk Decomposition and Budgeting

  • Backtesting

  • Stress trying out

  • Model chance
Divided into elements, half one discusses many of the hazard dimension ideas, while half presents a toolkit of the most instruments required to appreciate industry hazard size. A CD is packaged with the ebook, containing a MATLAB folder of danger dimension capabilities, as well as a few examples in Excel/VBA.

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

Example text

16–17)). If an investor working on his/her own behalf can easily end up with perverse positions, there is even more scope for mischief where decision-making is decentralised and traders or asset managers are working to VaR-constrained targets or VaR-defined remuneration packages. After all, traders or asset managers will only ‘spike’ their firm if they work to an incentive structure that encourages them to do so. If traders face a VaR-defined risk target, they will often have an incentive to sell out-of-the-money options to increase ‘normal’ profits and hence their bonus; the downside is that the institution takes a bigger hit once in a while, but it is difficult to design systems that force traders to care about these bigger hits: the fact that VaR does not take account of what happens in ‘bad’ states can distort incentives and encourage traders or managers to ‘game’ a VaR target (and/or a VaR-defined remuneration package), and promote their own interests at the expense of the interests of the institutions they are supposed to be serving.

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.

In risk management, we want our risk estimates to be unbiased or biased conservatively. If regulators use non-sub-additive risk measures to set capital requirements, a financial firm might be tempted to break itself up to reduce its regulatory capital requirements, because the sum of the capital requirements of the smaller units would be less than the capital requirement of the firm as a whole. Non-sub-additive risk measures can also tempt agents trading on an organised exchange to break up their accounts, with separate accounts for separate risks, in order to reduce their margin requirements.

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