By Moorad Choudhry
The value-at-risk size technique is a widely-used device in monetary marketplace danger administration. The 5th version of Professor Moorad Choudhry’s benchmark reference textual content An advent to Value-at-Risk bargains an available and reader-friendly examine the idea that of VaR and its varied estimation tools, and is aimed in particular at novices to the marketplace or these unexpected with sleek possibility administration practices. the writer capitalises on his event within the monetary markets to offer this concise but in-depth assurance of VaR, set within the context of possibility administration as a whole.
Topics lined include:
- Defining value-at-risk
- Variance-covariance methodology
- Portfolio VaR
- Credit probability and credits VaR
- Stressed VaR
- Critique and VaR in the course of crisis
Topics are illustrated with Bloomberg monitors, labored examples and routines. comparable matters corresponding to facts, volatility and correlation also are brought as priceless heritage for college kids and practitioners. this is often crucial examining for all those that require an advent to monetary industry possibility administration and chance size techniques.
Foreword by means of Carol Alexander, Professor of Finance, college of Sussex.
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Additional info for An Introduction to Value-at-Risk
Are the term structure of that currency’s interest rate (either the zero-coupon curve or the par yield curve) and the exchange rate. 2. Revalue the portfolio using alternative prices based on changed market factors and calculate the change in the portfolio value that would result. 3. Revaluing the portfolio using a number of alternative prices gives a distribution of changes in value. Given this, a portfolio VaR can be speciﬁed in terms of conﬁdence levels. 4. The risk manager can calculate the maximum the ﬁrm can lose over a speciﬁed time horizon at a speciﬁed probability level.
Two methods for unequal weighting are the generalised autoregressive conditional heteroscedasticity (GARCH) models and exponentially weighted moving averages. GARCH models are ﬁne-tuned to each risk factor time series, while exponentially weighted averages can be computed with little more complication than simple historic volatility. Both methods rely on the assumption that future volatilities can be predicted from historic price movements. Historical simulation method The historical simulation method for calculating VaR is the simplest and avoids some of the pitfalls of the correlation method.
We also assume that our observations are normally distributed. 2. 96 standard deviations from the mean. 96 standard deviations. 96 standard deviations. This would be referred to as a ‘twosided’ (or ‘two-tailed’) conﬁdence interval. It gives the probability of a move upwards or downwards by the random variable outside the limits we are expecting. In the ﬁnancial markets, we do not however expect negative prices, so that values below 0 are not really our concern. ). 65) standard deviations from the mean.