Download An Introduction to Value-at-Risk by Moorad Choudhry PDF

By Moorad Choudhry

The value-at-risk dimension technique is a widely-used device in monetary industry possibility administration. The 5th version of Professor Moorad Choudhry’s benchmark reference textual content An creation to Value-at-Risk deals an available and reader-friendly examine the idea that of VaR and its diverse estimation tools, and is aimed in particular at rookies to the marketplace or these unexpected with glossy probability administration practices. the writer capitalises on his event within the monetary markets to provide this concise but in-depth insurance of VaR, set within the context of possibility administration as a whole.

Topics coated include:

  • Defining value-at-risk
  • Variance-covariance methodology
  • Portfolio VaR
  • Credit possibility and credits VaR
  • Stressed VaR
  • Critique and VaR in the course of crisis

Topics are illustrated with Bloomberg displays, labored examples and workouts. comparable concerns comparable to data, volatility and correlation also are brought as precious history for college students and practitioners. this can be crucial interpreting for all those that require an creation to monetary industry chance administration and probability size techniques.

Foreword through Carol Alexander, Professor of Finance, college of Sussex.

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Extra info for An Introduction to Value-at-Risk

Example text

A large number of randomly generated simulations are run forward in time using volatility and correlation estimates chosen by the risk manager. , historical distributions and volatility and correlation estimates). This method is more realistic than the previous two models and, therefore, is more likely to estimate VaR more accurately. However, its implementation requires powerful computers and there is also a trade-off in that the time to perform calculations is longer. Validity of the volatility–correlation VaR estimate The level of confidence in the VaR estimation process is selected by the number of standard deviations of variance applied to the probability distribution.

For example, mapping individual stocks into the S&P 500 or fixed interest securities into the swap curve translate into the assumption that individual financial instruments move as the market overall. This is reasonable for diversified portfolios but may fall down for undiversified or illiquid portfolios. 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 also using the implied volatility contained in exchange-traded option prices.

It is measured within a given confidence interval, typically 95% or 99%. The tech- VALUE-AT-RISK 31 nique seeks to measure possible losses from a position or portfolio under ‘normal’ circumstances. The definition of normality is critical to the estimation of VaR and is a statistical concept; its importance varies according to the VaR calculation methodology that is being used. Broadly speaking, the calculation of a VaR estimate follows four steps: 1. Determine the time horizon over which one wishes to estimate a potential loss – this horizon is set by the user.

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