Verification of Financial Models

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November 13, 2011

Verification of Financial Models

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

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Exactpro

November 13, 2011
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  1. Verification of Financial Models: Duplication of Development Efforts? Alyona Lamash,

    Boris Rabinovich EXTENT October 2011
  2. Contents 1. Introduction 2. Model and Implementation 3. Application 4.

    Technical Analysis 5. Derivatives Pricing 6. Implied Liquidity 7. Value at Risk 8. Model Risk in Modern Markets 9. Summary 10.Q&A
  3. 1.Introduction Financial modeling: Applying appropriate mathematical models to financial concepts

    Verification of financial models: – Correctness of model implementation – Internal consistency of the model – Its correspondence to real life – Calibration (fine tuning)
  4. 2.Model and Implementation Verification of Model: • Selecting assumptions •

    The risk to make an assumption • The impact of the assumption on your model • Calibrating the model
  5. 2.Model and Implementation (cont.) Verification of Implementation: • Algorithm •

    Hardware capacity • Market conditions
  6. 3.Application Examples of application: – Technical analysis – Derivatives pricing

    – Implied liquidity – Risk measurement (VaR) – Trading algorithms (robots) – Accounting
  7. 4.Technical Analysis

  8. 4.Technical Analysis (cont.) Testing of technical analysis applications – Excel:

    basic strategies and P&L calculations – Test on historical data – Manually include patterns to the data – Then test complex strategies, trends, etc. on artificially created market data
  9. 4.Technical Analysis (cont.) Testing of technical analysis strategies – Firstly

    test on historical data (back-testing) – No full freedom in data manipulation – Simulate specific market conditions (extra-ordinary, but still realistic) – Take into account: • Delay after the signal • Bid-Ask spread • Market impact
  10. 5.Derivatives Pricing • Derivative – financial product depending on another

    asset (underlying) • Derivative pricing validation – Internal consistency: • Call - Put = Forward (call-put parity) • American option > European option • Knock In + Knock Out = Vanilla • Geometric mean < arithmetical mean – Dependencies on parameters – Simple is a particular case of complex
  11. 6.Implied Liquidity Implied order – a combination of existing orders

    in the market. Errors and limitations: rounding, dual liability, etc Bid 2Y Offer 5Y Offer 2Yv5Y
  12. 7.Value at Risk 1 day 99% confidence level VaR –

    A loss from a portfolio which you are 99% sure will not be exceeded in one day • Historical VaR vs Variance/covariance VaR vs Monte-Carlo simulation • Tail loss • Stress testing • VaR
  13. 7.Value at Risk 1 day 99% confidence level VaR –

    A loss from a portfolio which you are 99% sure will not be exceeded in one day • Historical VaR vs Variance/covariance VaR vs Monte-Carlo simulation • Tail loss • Stress testing • VaR
  14. 8.Model Risk in Modern Markets • QA (verification) to prevent

    errors in model and its implementation • Financial disasters when models failed
  15. 9.Summary • Verification gives another point of view on the

    problem • Helps to find errors in the algorithm • Reveals caveats in model and implementation • Appropriate method should be selected in order not to duplicate efforts but give additional value
  16. 10.Questions & Answers Thank you.