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

By Kevin Dowd

This publication offers an advent to price in danger (VaR) and anticipated tail loss (ETL) estimation and is a student-oriented model of Measuring industry Risk (John Wiley & Sons 2002).

An creation to industry chance Measurement contains assurance of:

  • Parametric and non-parametric danger estimation

  • Simulation

  • Numerical tools

  • Liquidity hazards

  • Risk Decomposition and Budgeting

  • Backtesting

  • Stress trying out

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

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2) Since VaR tells us the maximum amount we are likely to lose, we can use it to determine capital allocation. We can use it to determine capital requirements at the level of the firm, but also right down the line, to the level of the individual investment decision: the riskier the activity, the higher the VaR and the greater the capital requirement. 17 (4) We can use VaR information to assess the risks of different investment opportunities before decisions are made. 19 (6) VaR information can be used to provide new remuneration rules for traders, managers and other employees that take account of the risks they take, and so discourage the excessive risk-taking that occurs when employees are rewarded on the basis of profits alone, without any reference to the risks they took to get those profits.

1997); Danielsson (2001); Holton (1997, 2002); Hoppe (1998); Linsmeier and Pearson (1996); Moosa and Knight (2001); Schachter (1997); Taleb (1997a,b). 2 Measures of Financial Risk This chapter deals with alternative measures of financial risk. To elaborate, suppose we are working to a daily holding or horizon period. At the end of day t − 1, we observe that the value of our portfolio is Pt−1 . However, looking forward, the value of our portfolio at the end of tomorrow, Pt , is uncertain. Ignoring any intra-day returns or intra-day interest, if Pt turns out to exceed Pt−1 , we will make a profit equal to the difference, Pt − Pt−1 ; and if Pt turns out to be less than Pt−1 , we will make a loss equal to Pt−1 − Pt .

4, which shows how a symmetric fat-tailed distribution — in this case, a Student t-distribution with five degrees of freedom — compares to a normal one. Because the area under the pdf curve must always be 1, the distribution with the fatter tails also has less probability mass in the centre. Tail-fatness — kurtosis in excess of 3 — means that we are more likely to gain a lot or lose a lot, and the gains or losses will tend to be larger, relative to normality. , zero-skew) distribution with a kurtosis of 3.

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