An Introduction to Value-at-Risk - download pdf or read online

By Moorad Choudhry

ISBN-10: 0470017570

ISBN-13: 9780470017579

ISBN-10: 0470033770

ISBN-13: 9780470033777

The value-at-risk dimension method is a widely-used instrument in monetary marketplace possibility administration. The fourth version of Professor Moorad Choudhry's benchmark reference textual content An creation to Value-at-Risk deals an obtainable and reader-friendly examine the idea that of VaR and its assorted estimation equipment, and is aimed particularly at newbies to the marketplace or these unexpected with sleek danger administration practices. the writer capitalises on his event within the monetary markets to offer this concise but in-depth insurance of VaR, set within the context of chance administration as a complete.

Topics coated comprise:

  • Defining value-at-risk
  • Variance-covariance method
  • Monte Carlo simulation
  • Portfolio VaR
  • Credit danger and credits VaR

themes are illustrated with Bloomberg displays, labored examples, routines and case reports. comparable concerns akin to statistics, volatility and correlation also are brought as priceless historical past for college students and practitioners. this can be crucial analyzing for all those that require an creation to monetary marketplace danger administration and value-at-risk.

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Additional info for An Introduction to Value-at-Risk

Example text

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. 014 813 282 Mean Standard deviation Excel formula ¼AVERAGE(C7:C16) ¼AVERAGE(D7:D16) ¼STDEV(C7:C16) ¼STDEV(D7:D16) VOLATILITY AND CORRELATION 21 The volatility demonstrated above is historical volatility; it is based on past performance.

To calculate the VaR for a single security, we would calculate the standard deviation of its price returns. This can be done using historical data, but more commonly using the implied volatility contained in exchange-traded option prices. We would then select a confidence interval and apply this to the standard deviation, which would be our VaR measure. This is considered in more detail later. There are three main methods for calculating VaR. As with all statistical models, they depend on certain assumptions.

The volatility matrix contains the standard deviations along the diagonal, and 0s are entered in all the other cells. So, if the portfolio we were calculating has 50 assets in it, we would require a 50 Â 50 matrix and enter the standard deviations for each asset along the diagonal line. All the other cells would have a 0 in them. Similarly, for the weighting matrix this is always one row, and all the weights are entered along the row. To take the example just given the result would be a 1 Â 50 weighting matrix.

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An Introduction to Value-at-Risk by Moorad Choudhry

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