Risk Warehouse Data Verification by RTTS
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Investment Banking |
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Part of an investment bank’s daily operation is to track the
value of Credit Default Swaps on a daily basis. A Credit
Default Swap (CDS) is a contract between a buyer and a
seller in which the buyer of the CDS agrees to make a series
of payments to the seller that are akin to insurance
premiums against default of a credit instrument (such as a
bond or mortgage backed security). In return, the buyer
receives a payoff from the seller if the credit instrument
goes into default; if the instrument does not go into
default, the seller keeps the payments as profit.. Various
Risk and P&L measures are calculated daily by a risk data
warehouse in order to track the changing values of different
CDSs. Frequent releases of this valuation system are
necessitated because the calculations are changed in
response to changing business and market conditions. The
goal of the software quality effort is to assure that each
new version of the valuation system calculates these
measures the same as the previously deployed production
system. |
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How can a data-warehouse software quality effort compare
hundreds of books with thousands of CDS’s with the
corresponding measures from another data source, and output
the comparisons in a manner suitable for further
investigation of any mismatched measures?
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Implement a high-volume database comparison system to
retrieve risk data from various databases and store the data
in a results database, from which data analysis can be
performed. |
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RTTS designed and
built a custom test
application using Visual
Studio.Net and VB.Net to
set up high-volume
SQL-based data
comparisons between test
systems and production
systems
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Approximately 1000
SQL queries were written
against the combined
systems to compare
measures from the test
systems to the analogous
data in the production
systems.
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Data comparison runs
were performed on an
ever expanding set of
books and then
investigations were
performed on mismatched
CDS’s.
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Several thousand
CDS’s and their
valuation measures could
be compared in less than
10 minutes.
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The results of the
analysis could then be
used to investigate the
reasons there are
differences amongst the
analogous measures.
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The automated framework
decreased the necessary
time to compare CDS’s by
a factor of 100.
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After comparison runs
were completed, manual
investigations as to
differences could be
performed quickly
because the
discrepancies were
pinpointed by the
automated comparisons.
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Testers could run
comparisons and begin
investigations within
minutes, as opposed to
hours.
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