skites

SKITES
Sharing Knowledge and Intelligence Towards Economic Success
OpCapital Analytics
State of the Art Operational Risk Quantification for Capital Model Regulatory Approval
OpCapital Analytics is a set of tools designed for the quantification of Operational Risk, and economic and regulatory capital
calculations using AMA (Advanced Measurement Approach) built by Skitesi, S.L. (visit www.skites.es). It is currently implemented in
banks and utilities in three continents.
OpCapital Analytics integrates all required
calculations for the operational risk quantification
and economic and regulatory capital determination
process. It starts with data extraction, filtering and
analysis. It then continues with the distribution
fitting of internal and external data, R&CSA and
integration of scenario analyses into the simulation
in order to calculate total loss distribution allowing
for correlations. Various simulations can be
generated to evaluate different insurance policies
and their impact on both regulatory and economic
capital under different solvency standards.
An interesting characteristic of OpCapital Analytics
is its reporting functionalities for all analytical modules
in PDF, HTML or Microsoft Word format with the
analytical results. The productivity is increased as
most analytic modules (data filtering, distribution fitting,
etc.) can be launched simultaneously several times,
permitting direct comparison of different analysis.
OpCapital Analytics´s most differential features are:
 Extreme Value Theory analyses:
- 8 EVT tests to determine threshold and tail
weight: - Hill, Mean Excess Plot, etc.
- Distribution body and tail split.
- Severity distribution shifting.
- Threshold optimization during the
distribution fitting process.
 Combination of parametric and nonparametric distributions within the same
modelling cell.
 Evaluation of the stability of distribution
parameters, GoF and capital estimation.
 Detailed insurance modelling.
 Consideration of all modelling possibilities:
Automatic and simultaneous generation of up
to 30 distributions under 4 fitting methods.
 All conversing distributions are ranked under
12 statistical test and compared in multiple
visual analysis.
 User defined distributions using XML files.
 4 fitting methodologies using XML files,
Probability Weighted Least, Robust Least
Squares and moments approach.
 Integration of qualitative risk evaluations:
- Integration of qualitative scenarios.
- Integration of R&CSA.
- Integration with KRI through regression.
 External data rescaling module: rescale
external data using internal data quantiles as
scaling factors and external data distribution
properties (tail parameter, Kurtosis, etc.)2
methods to integrate external data: Bayesian
and Actuarial method.
 Automatic copula (Gaussian, t-student, etc)
fitting for simulation using multiple MLE.
 Nested copulas and flexible aggregation trees
for correlated Monte Carlo simulation in
multiple levels to decrease the data
requirements for correlation matrices.
 Different capital attribution methodologies:
unexpected loss contribution, expected
shortfall contribution, etc.
 Operational risk backtesting functionalities
including scenario analysis validation using
Multiple Expert Analysis.
 Operational risk stress testing: shifting
distribution parameters, shifting fitting weight
to tail observations and scenario analysis.
 Operational risk appetite setting and
monitoring: capital allocation down to RCSA
granularity and OpRisk limit threshold
calculation for hard and soft limit levels.
 Predictive models for the analysis of
operational risk losses and BEICFs.
OpCapital Analytics is entirely written in MATLAB.
The program is distributed under a share code license
permitting modifications by users.
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SKITES
Sharing Knowledge and Intelligence Towards Economic Success
OpCapital Analytics
State of the Art Operational Risk Quantification for Capital Model Regulatory Approval
Currently, OpCapital Analytics is available in 8 languages
including English, Arabic, Spanish and Japanese. The
program can run on standalone computers or up to 1024
cluster computers on the three most popular platforms
Microsoft Windows, Mac OS X and Linux.
Stability Parameter: representation of tail parameter of fitted
pareto distribution by threshold
OpCapital Analytics can connect via ODBC / JDBC drivers
connections with the most popular databases (i.e., Oracle,
BD2, MS Access, etc.) to extract qualitative and event data.
This makes possible to implement the tool in any
technological environment with minimal implementation effort
(most of the time is drag and play). Additionally, data import
from Microsoft Excel or text files is also allowed.
Tests for Extreme Value Theory Implementation
OpCapital Analytics provides 8 tests for tail and fit analysis
including most popular Extreme Value Theory (EVT) tests.
The list of available analytic tools is the following:
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Stability Parameter
Tail Plot
Mean Excess Plot
DEdH
Hill estimator
HKKP-Hill
GoF by threshold analysis
Analysis of capital estimates stability by threshold
Capital Estimates Stability by Threshold Analysis
DEdH: representation of Sigma and Mu of fitted lognormal
distribution by threshold
GoF by Threshold Analysis
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SKITES
Sharing Knowledge and Intelligence Towards Economic Success
OpCapital Analytics
State of the Art Operational Risk Quantification for Capital Model Regulatory Approval
Fitting distributions
OpCapital Analytics has all the necessary tools for the
modelling of operational losses, using both empirical and
parametric distributions. It is possible to use up to three
different information sources (for example: internal data,
external data, scenario analysis or R&CSA) integrating all
these sources of information into a single distribution. Each of
these distributions can be defined using up to three
segments (for example: low, medium and high losses) and a
segment specific weight for each of the information sources
can also be defined.
To evaluate the stability of distribution parameters and capital
estimations, the volatility of capital is compared to the GoF. A
high GoF paired with a high volatility on capital suggests
overfitting. On the other hand, if volatility is low, the fitting
appears to be stable.
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1. Permits to compare the parameter stability of different
distributions and thresholds.
2. At any moment the user may add new distributions or
remove existing ones from the list
3. The parameter stability can be analysed under four fit
methodologies
4. The random fraction of data used in the fitted process can
be modified by the user.
OpCapital Analytics includes more than 30 built-in
parametric distributions including the most commonly used
distributions in operational risk and distribution mixtures. The
user can extend this list of distributions using XML files
without computer programming knowledge and code
modifications. Also distribution XML files can be exchanged
between users due to the fact that it is open standard. It also
provides multiple numerical, P-Value and graphical tests to
determine the best fitting distribution.
5. Parameter statistics for the different distributions are
showed in the bottom window table
6. Three different box tabs can be used to compare results:
- Capital estimate
- Anderson-Darling P-value
- Kolmogorov-Smirnov. P-value
7. Capital estimates statistics are shown at the bottom
window table.
XML file to
introduce new
distributions
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SKITES
Sharing Knowledge and Intelligence Towards Economic Success
OpCapital Analytics
State of the Art Operational Risk Quantification for Capital Model Regulatory Approval
Integration of R&CSA and Scenario Analysis
Monte Carlo simulation
The tool provides functionalities to fit distributions to R&CSA
and scenario analysis that will be integrated later into the
simulation.
Once the empirical data and qualitative scenarios have been
used to model each operational risk cell, the total operational
loss distribution can be calculated using a Monte Carlo
simulation. The simulation allows the consideration of
different insurance policies, solvency standards and levels of
correlation between events, etc. The simulation gives the
economic capital, regulatory capital and the attribution of both
to the different entity business areas or risk types results. The
results can be compared graphically inside OpCapital
Analytics or exported to Microsoft Excel spreadsheets.
OpCapital Analytics can generate correlated simulations after
automatically fitting copulas (Gaussian, T-Student, etc.) using
two different methods:
 Multivariant Maximum Likelihood Estimation (MLE)
for Gaussian and t-Student copulas,
 Kendal-tau, and
 Spearman Rho
Rescaling external data
Rescaling external data can be done by deriving some
scaling factors from internal data (Mean, mode, low
quantiles, etc.) and high moments (kurtosis, etc.) or
distribution parameters (tail parameter, shape parameters,
etc.) from external data. The data rescaling module will find
the distribution that most closely replicates the conditions
established.
Model backtesting and validation.
There is a possibility that the model developed with previous
year’s data doesn´t fit to this year´s data. This problem can
be analyzed using the Backtesting module available in
OpCapital Analytics. With this tool, previous models can be
loaded and tested with different data sets, permitting to
evaluate similarities between empirical losses suffered in
different years, similarity between parametric distributions
used for capital calculations and the real losses experienced
in the following year, etc.
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SKITES
Sharing Knowledge and Intelligence Towards Economic Success
OpCapital Analytics
State of the Art Operational Risk Quantification for Capital Model Regulatory Approval
Stress testing of operational risk
Advantages
OpCapital Analytics provides functionalities to perform stress
testing analysis required by regulators, permitting several
approaches:
OpCapital Analytics permits to automate the full workflow in
operational risk advanced modeling reducing the possibility
of errors, allowing the monitoring of the process and
facilitating the replication of results.
 Directly shifting distribution parameters: for
distribution stored in the modeling archive, their
parameters can be directly shifted and capital
sensitivity is recalculated using the maximum loss
approximation. Generally, parameters should be
shifted as they are related to distribution tangible
and well understood characteristics (frequency,
mean, standard deviation, etc.)
It has very significant advantages versus a bespoke model
which include: audit trail to automatically document modeling,
governance over the modeling options and user profiles,
workflow management, automatic storage of modeling input,
fully integrated data flows between analytical processes,
interfaces with databases and GRC solutions, extensive
reporting (PDF, HTML or Microsoft Word), very flexible
analytics and model structure of business units, loss types,
etc., maintenance,...
Additionally, due to its share code distribution license, it is
possible to audit the calculation process in order to
guarantee the results adequacy and continue the model
development.
The productivity will be increased due to most analytic
modules can be started several times with the same or
different data, making possible to compare directly the result
of different analysis.
 Shifting the weight of the fit to extreme observations
using probability weighted least square approach
for the fit.
Contact Details
For a WebEx demo, information on commercial conditions of
Skites risk management consulting services or software
access, please contact the following people:
Rafael Cavestany
[email protected]
00 34 639 24 36 27
Disclaimer.
The mentioned products or brand names may be trademarks
or registered trademarks of their respective holders.
i SKITES is a society founded by European academics and
 Using scenario analysis, as explained in the
“Integration of R&CSA and Scenario Analysis”
section.
researchers in risk management and analytics focused on
designing and implementing advanced analytics tools with
emphasis in risk management, optimization, decision analysis
and data mining. Current main activities range from OpRisk
management and quantification, reputational risk measurement
via sentiment analysis, cibersecurity, fraud detection, etc.
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