Verification of Financial Models
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Verification of Financial Models

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Verification of Financial Models Verification of Financial Models Presentation Transcript

  • Verification of Financial Models: Duplication of Development Efforts? Alyona Lamash, Boris Rabinovich EXTENT October 2011
  • Contents
    • Introduction
    • Model and Implementation
    • Application
    • Technical Analysis
    • Derivatives Pricing
    • Implied Liquidity
    • Value at Risk
    • Model Risk in Modern Markets
    • Summary
    • Q&A
  • 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)
  • 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
  • 2.Model and Implementation (cont.)
    • Verification of Implementation:
    • Algorithm
    • Hardware capacity
    • Market conditions
  • 3.Application
    • Examples of application:
      • Technical analysis
      • Derivatives pricing
      • Implied liquidity
      • Risk measurement (VaR)
      • Trading algorithms (robots)
      • Accounting
  • 4.Technical Analysis
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 8.Model Risk in Modern Markets
    • QA (verification) to prevent errors in model and its implementation
    • Financial disasters when models failed
  • 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
  • 10.Questions & Answers
    • Thank you.