By Moorad Choudhry
The value-at-risk size technique is a widely-used software in monetary marketplace threat administration. The fourth version of Professor Moorad Choudhry's benchmark reference textual content An advent to Value-at-Risk bargains an available and reader-friendly examine the concept that of VaR and its various estimation tools, and is aimed in particular at beginners to the marketplace or these strange with sleek probability administration practices. the writer capitalises on his adventure within the monetary markets to offer this concise but in-depth insurance of VaR, set within the context of possibility administration as a complete.
Topics coated comprise:
- Defining value-at-risk
- Variance-covariance technique
- Monte Carlo simulation
- Portfolio VaR
- Credit chance and credits VaR
issues are illustrated with Bloomberg monitors, labored examples, routines and case reviews. similar concerns similar to records, volatility and correlation also are brought as invaluable heritage for college kids and practitioners. this can be crucial studying for all those that require an creation to monetary marketplace probability administration and value-at-risk.
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Additional info for An Introduction to Value-at-Risk
However, the standard deviation shows a different picture, and we see that Portfolio B exhibits much greater volatility than Portfolio A. Its future performance is much harder to predict with any reasonable confidence. Portfolio B carries higher risk and so would carry higher VaR. 2 that standard deviation is a measure of the dispersion away from the mean of all the observations. To be comfortable that the statistical measures are as accurate as possible, we need the greatest number of observations.
Proactive management involvement in risk issues; daily overview of risk exposure profile and profit and loss ( p&l) reports; VaR as a common measure of risk exposure, in addition to other measures including ‘jump risk’ to allow for market corrections; defined escalation procedures to deal with rising levels of trading loss, as well as internal ‘stop-loss’ limits; independent daily monitoring of risk utilisation by middle-office risk management function; independent production of daily p&l, and independent review of front-office closing prices on a daily basis; independent validation of market pricing, and pricing and VaR models.
1 The log-normal distribution. unlike the normal curve, because it does not have negatives at the extreme values. Confidence intervals Assume an estimate x of the average of a given statistical population where the true mean of the population is . Suppose that we believe that on average x" is an unbiased estimator of . Although this means that on average x" is accurate, the specific sample that we observe will almost certainly be above or below the true level. Accordingly, if we want to be reasonably confident that our inference is correct, we cannot claim that is precisely equal to the observed x".