Confidence interval bands
returns a table of the requested risk measure and its associated confidence
cbTable = confidenceBands(
confidenceBands is used to investigate how the values
of a risk measure and its associated confidence interval converge as the number
of scenarios increases. The
simulate function must be run
confidenceBands is used. For more information on using
creditDefaultCopula object, see
Generate a Table of the Associated Confidence Bands for a Requested Risk Measure for a
Load saved portfolio data.
creditDefaultCopula object with a two-factor model.
cdc = creditDefaultCopula(EAD,PD,LGD,Weights2F,'FactorCorrelation',FactorCorr2F)
cdc = creditDefaultCopula with properties: Portfolio: [100x5 table] FactorCorrelation: [2x2 double] VaRLevel: 0.9500 UseParallel: 0 PortfolioLosses: 
VaRLevel to 99%.
cdc.VaRLevel = 0.99;
simulate function before running
confidenceBands with the
creditDefaultCopula object to generate the
cdc = simulate(cdc,1e5); cbTable = confidenceBands(cdc,'RiskMeasure','Std','ConfidenceIntervalLevel',0.9); cbTable(1:10,:)
ans=10×4 table NumScenarios Lower Std Upper ____________ ______ ______ ______ 1000 23.38 24.237 25.166 2000 23.255 23.859 24.497 3000 23.617 24.117 24.642 4000 23.44 23.871 24.319 5000 23.504 23.891 24.291 6000 23.582 23.935 24.301 7000 23.756 24.086 24.426 8000 23.587 23.893 24.208 9000 23.582 23.871 24.167 10000 23.525 23.799 24.079
comma-separated pairs of
the argument name and
Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
cbTable = confidenceBands(cdc,'RiskMeasure','Std','ConfidenceIntervalLevel',0.9,'NumPoints',50)
RiskMeasure — Risk measure to investigate
(default) | character vector or string with values
Risk measure to investigate, specified as the comma-separated pair
'RiskMeasure' and a character
vector or string. Possible values are:
'EL'— Expected loss, the mean of portfolio losses
'Std'— Standard deviation of the losses
'VaR'— Value at risk at the threshold specified by the
VaRLevelproperty of the
'CVaR'— Conditional VaR at the threshold specified by the
VaRLevelproperty of the
ConfidenceIntervalLevel — Confidence interval level
(default) | numeric between
Confidence interval level, specified as the comma-separated pair
'ConfidenceIntervalLevel' and a
example, if you specify
0.95, a 95% confidence
interval is reported in the output table
NumPoints — Number of scenario samples to report
(default) | nonnegative integer
Number of scenario samples to report, specified as the
comma-separated pair consisting of
and a nonnegative integer. The default is
meaning confidence bands are reported at 100 evenly spaced points of
increasing sample size ranging from 0 to the total number of
NumPoints must be a numeric scalar
1, and is typically much
smaller than total number of scenarios simulated.
confidenceBands can be used to obtain
a qualitative idea of how fast a risk measure and its
confidence interval are converging. Specifying a large value
NumPoints is not recommended and
could cause performance issues with
cbTable — Requested risk measure and associated confidence bands
Requested risk measure and associated confidence bands at each of the
NumPoints scenario sample sizes, returned as a
table containing the following columns:
NumScenarios— Number of scenarios at the sample point
Lower— Lower confidence band
RiskMeasure— Requested risk measure where the column takes its name from whatever risk measure is requested with the optional input
Upper— Upper confidence band
 Crouhy, M., Galai, D., and Mark, R. “A Comparative Analysis of Current Credit Risk Models.” Journal of Banking and Finance. Vol. 24, 2000, pp. 59–117.
 Gordy, M. “A Comparative Anatomy of Credit Risk Models.” Journal of Banking and Finance. Vol. 24, 2000, pp. 119–149.
 Gupton, G., Finger, C., and Bhatia, M. “CreditMetrics – Technical Document.” J. P. Morgan, New York, 1997.
 Jorion, P. Financial Risk Manager Handbook. 6th Edition. Wiley Finance, 2011.
 Löffler, G., and Posch, P. Credit Risk Modeling Using Excel and VBA. Wiley Finance, 2007.
 McNeil, A., Frey, R., and Embrechts, P. Quantitative Risk Management: Concepts, Techniques, and Tools. Princeton University Press, 2005.