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Verification of Financial Models

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EXTENT Conference - October 2011
Test Automation for Trading Systems

Verification of Financial Models: Duplication of Development Efforts?

Alyona Lamash, Head of Risk Management Systems Practice Innovative Trading Systems

Boris Rabinovich, Senior QA Analyst ITS

Published in: Business, Economy & Finance
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Verification of Financial Models

  1. 1. Verification of Financial Models: Duplication of Development Efforts? Alyona Lamash, Boris Rabinovich EXTENT October 2011
  2. 2. Contents <ul><li>Introduction </li></ul><ul><li>Model and Implementation </li></ul><ul><li>Application </li></ul><ul><li>Technical Analysis </li></ul><ul><li>Derivatives Pricing </li></ul><ul><li>Implied Liquidity </li></ul><ul><li>Value at Risk </li></ul><ul><li>Model Risk in Modern Markets </li></ul><ul><li>Summary </li></ul><ul><li>Q&A </li></ul>
  3. 3. 1.Introduction <ul><li>Financial modeling: </li></ul><ul><ul><li>Applying appropriate mathematical models to financial concepts </li></ul></ul><ul><li>Verification of financial models: </li></ul><ul><ul><li>Correctness of model implementation </li></ul></ul><ul><ul><li>Internal consistency of the model </li></ul></ul><ul><ul><li>Its correspondence to real life </li></ul></ul><ul><ul><li>Calibration (fine tuning) </li></ul></ul>
  4. 4. 2.Model and Implementation <ul><li>Verification of Model: </li></ul><ul><li>Selecting assumptions </li></ul><ul><li>The risk to make an assumption </li></ul><ul><li>The impact of the assumption on your model </li></ul><ul><li>Calibrating the model </li></ul>
  5. 5. 2.Model and Implementation (cont.) <ul><li>Verification of Implementation: </li></ul><ul><li>Algorithm </li></ul><ul><li>Hardware capacity </li></ul><ul><li>Market conditions </li></ul>
  6. 6. 3.Application <ul><li>Examples of application: </li></ul><ul><ul><li>Technical analysis </li></ul></ul><ul><ul><li>Derivatives pricing </li></ul></ul><ul><ul><li>Implied liquidity </li></ul></ul><ul><ul><li>Risk measurement (VaR) </li></ul></ul><ul><ul><li>Trading algorithms (robots) </li></ul></ul><ul><ul><li>Accounting </li></ul></ul>
  7. 7. 4.Technical Analysis
  8. 8. 4.Technical Analysis (cont.) <ul><li>Testing of technical analysis applications </li></ul><ul><ul><li>Excel: basic strategies and P&L calculations </li></ul></ul><ul><ul><li>Test on historical data </li></ul></ul><ul><ul><li>Manually include patterns to the data </li></ul></ul><ul><ul><li>Then test complex strategies, trends, etc. </li></ul></ul><ul><ul><li>on artificially created market data </li></ul></ul>
  9. 9. 4.Technical Analysis (cont.) <ul><ul><li>Testing of technical analysis strategies </li></ul></ul><ul><ul><li>Firstly test on historical data (back-testing) </li></ul></ul><ul><ul><li>No full freedom in data manipulation </li></ul></ul><ul><ul><li>Simulate specific market conditions </li></ul></ul><ul><ul><ul><li>(extra-ordinary, but still realistic) </li></ul></ul></ul><ul><ul><li>Take into account: </li></ul></ul><ul><ul><ul><li>Delay after the signal </li></ul></ul></ul><ul><ul><ul><li>Bid-Ask spread </li></ul></ul></ul><ul><ul><ul><li>Market impact </li></ul></ul></ul>
  10. 10. 5.Derivatives Pricing <ul><li>Derivative – financial product depending on another asset (underlying) </li></ul><ul><li>Derivative pricing validation </li></ul><ul><ul><li>Internal consistency: </li></ul></ul><ul><ul><ul><li>Call - Put = Forward (call-put parity) </li></ul></ul></ul><ul><ul><ul><li>American option > European option </li></ul></ul></ul><ul><ul><ul><li>Knock In + Knock Out = Vanilla </li></ul></ul></ul><ul><ul><ul><li>Geometric mean < arithmetical mean </li></ul></ul></ul><ul><ul><li>Dependencies on parameters </li></ul></ul><ul><ul><li>Simple is a particular case of complex </li></ul></ul>
  11. 11. 6.Implied Liquidity <ul><li>Implied order – a combination of existing orders in the market. </li></ul><ul><li>Errors and limitations: rounding, dual liability, etc </li></ul>Bid 2Y Offer 5Y Offer 2Yv5Y
  12. 12. 7.Value at Risk <ul><li>1 day 99% confidence level VaR – </li></ul><ul><ul><li>A loss from a portfolio which you are 99% sure will not be exceeded in one day </li></ul></ul><ul><li>Historical VaR vs Variance/covariance VaR vs Monte-Carlo simulation </li></ul><ul><li>Tail loss </li></ul><ul><li>Stress testing </li></ul><ul><li>VaR </li></ul>
  13. 13. 7.Value at Risk <ul><li>1 day 99% confidence level VaR – </li></ul><ul><ul><li>A loss from a portfolio which you are 99% sure will not be exceeded in one day </li></ul></ul><ul><li>Historical VaR vs Variance/covariance VaR vs Monte-Carlo simulation </li></ul><ul><li>Tail loss </li></ul><ul><li>Stress testing </li></ul><ul><li>VaR </li></ul>
  14. 14. 8.Model Risk in Modern Markets <ul><li>QA (verification) to prevent errors in model and its implementation </li></ul><ul><li>Financial disasters when models failed </li></ul>
  15. 15. 9.Summary <ul><li>Verification gives another point of view on the problem </li></ul><ul><li>Helps to find errors in the algorithm </li></ul><ul><li>Reveals caveats in model and implementation </li></ul><ul><li>Appropriate method should be selected in order not to duplicate efforts but give additional value </li></ul>
  16. 16. 10.Questions & Answers <ul><li>Thank you. </li></ul>

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