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Course Calendar Course Calendar

Managing Model Risk for Quants, Traders and Validators

Day One

Model risk and model validation outlook

  • Managing model risk: value-based approach vs price-based approach. The credit crunch example
  • Suggestions from model risk management in science. The real black swans
  • Measuring model uncertainty. Practical meaning of no-arbitrage pricing and model completeness in liquid and illiquid markets
  • Accounting standards (IAS/IFRS). Implications of fair value on model validation. Practical analysis of level 2 and 3 pricing. A case study on swaps and basis risk
  • Market regulators: BIS, FSA, FED on stress testing, model risk and model validation. What changes after the crisis. Basel new principles
  • From theory to practice: step-by-step building of a practical framework for model validation and the management of model risk

Comparing and using alternative models

  • Model comparison methodology
  • Calibration and realism assessment
  • How to use alternative models to quantify model risk
  • First example: gap risk computation in leveraged notes. Structurals vs reduced-form models and their hidden consequences on gap losses
  • Second example: local vs stochastic volatility models on time-dependent equity derivatives. Smile dynamics
  • Third example: BGM vs short rate models for Interest Rate American-Bermudan. Hidden role of correlations and common misunderstandings 

Stress-testing design and pitfalls

  • Stress testing models and stress testing with models (scenario analysis for portfolios)
  • What to test? Stressing model assumptions and stressing model implementation (approximations, analytics and numerics)
  • First Example: stress-tests using market information. Improving correlation skew modelling for efficient portfolio scenario analysis
  • Second Example: stress scenarios design using historical information. Validating and improving mapping for bespoke credit portfolios
  • Third Example: stress-testing pitfalls. Detect copulas' weaknesses for forward correlations and improve on them

Day Two

Understanding model evolution to prevent model losses

  • Bringing hidden model assumptions to light and monitoring when they break down
  • Example 1: how interest-rate consensus model broke down when the basis spreads exploded. How to find an analytical model that explains the new market. Consequences for term-structure building
  • Example 2: modelling liquidity risk and liquidity charges. Funding liquidity and interactions with credit and discounting. Market liquidity and bid-ask. How changes in market fundamentals can shake the foundations of pricing

Hedging Analysis and P&L Analysis

  • Limits of pricing models when applied to hedging. How models are modified for efficient hedging. Validation of a real hedging strategy
  • Practical example: Local volatility models vs stochastic volatility models in options hedging. The case of SABR and the shadow delta for the swaptions smile
  • What we can get from P&L analysis and the delusions about it. The effect of model recalibration. The real cost of hedging and the charging of hedging costs


  • The risk of wrong correlation assumptions and technical difficulties in modelling interdependencies
  • Three tools to overcome technical difficulties. Examples: FX correlations, interest rate correlation parameterisations, correlation of stochastic volatility with the underlying
  • Controlling model dimensionality and correlation rank
  • Wrong correlation assumptions. 1-correlation risk with example on multifactor models. 0-correlation risk with example on counterparty risk. Correlation vs dependency


  • Assessing calibration stability through comparison with market variability and analysis of the implied evolution of term structure of volatilities
  • The effect of calibration instability on hedging
  • Practical examples in dynamic hedging and pricing of American options
  • Reverse engineering of counterparty quotes, consensus platforms, collateral regulations. Examples

MatLab workshop

Day Three


  • Validating an approximation. The operative steps. Monitoring market features that affect the reliability of approximations. Setting quantitative triggers
  • Validation against Monte Carlo methods. Methodology. Examples from interest rates: the swaption approximation and the convexity adjustments for CMS
  • Validating against analytic methods. SABR approximation vs the SABR model


  • Interpolation and extrapolations. Dangers of extrapolations and how to avoid them
  • Turning extrapolation into interpolation by adding data: example from volatility smile
  • Turning extrapolation into interpolation by changing variable: example from correlation skew


  • The practical meaning of arbitrage trading and statistical arbitrage. Risks of hedge funds and proprietary desks. Models' limitations in detecting arbitrage
  • Analysing and validating arbitrage strategies by revealing their nature of directional trades on market uncertainty
  • Practical example on the Equity/Bond capital-structure arbitrage
  • Cap-swaption arbitrage and how it broke down

Payoff Errors

  • Last but not least: are we sure the payoff is right? Mathematical errors and legal errors
  • Examples of payoff errors widely common in the market: index options and bilateral counterparty risk

A final synthesis with practical MatLab workshops on model risk and model validation


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