![]() In Exhibit 14.3, we find that our recommended standard coverage test’s non-rejection interval for q = 0.95 and α + 1 = 125 is. It can also be obtained by visual inspection of Exhibit 14.1. The last column indicates the specific quantile of loss for each P&L result, again, as determined by the value-at-risk measure. The exceedance column has a value of 1 if the portfolio realized a loss exceeding the 0.95 quantile of loss, as determined by the value-at-risk measure. Value-at-risk (VaR) and P&L values in the second and third columns are expressed in millions of euros. 14.6 Example: Backtesting a One-Day 95% EUR Value-at-Risk MeasureĮxhibit 14.8: Backtesting data for a one-day 95% EUR value-at-risk measure compiled over 125 trading days.14.5 Backtesting With Independence Tests.14.4 Backtesting With Distribution Tests.11.6 Shortcomings of Historical Simulation.11.5 Flawed Arguments for Historical Simulation.11.3 Calculating Value-at-Risk With Historical Simulation.11.2 Generating Realizations Directly From Historical Market Data.10.4 Monte Carlo Transformation Procedures.10.3 Quadratic Transformation Procedures.7.4 Unconditional Leptokurtosis and Conditional Heteroskedasticity.5.7 Breaking the Curse of Dimensionality.5.6 Implementing Pseudorandom Number Generators.5.5 Testing Pseudorandom Number Generators.4.8 White Noise, Moving-Average and Autoregressive Processes.3.17 Quantiles of Quadratic Polynomials of Joint-Normal Random Vectors.3.13 Quadratic Polynomials of Joint-Normal Random Vectors.3.8 Bernoulli and Binomial Distributions.3.5 Linear Polynomials of Random Vectors.2.3 Gradient & Gradient-Hessian Approx.1.6 Other Applications of Value-at-Risk. ![]()
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