Quality Seal Emagister EMAGISTER CUM LAUDE

Managing Model Risk for Quants, Traders and Validators

London Financial Studies
En London (Inglaterra), New York (Estados Unidos)

4001-5000

Información importante

  • Short course
  • En 2 sedes
  • Duración:
    3 Days
  • Cuándo:
    A elegir
Descripción

Understand the most advanced approaches to detect and control critical risks related to the use of quantitative models for pricing, hedging and risk management.

This course updates principles of model validation in accordance with most recent evolutions in research and accounting & regulatory standards, covering a range of models commonly used for different asset classes.

From SABR and BGM in Interest Rates to local volatility, stochastic volatility and jump dynamics used in Equity and FX, moving through copulas, structural and reduced form models used in credit, simple and advanced models are explained with a focus on the management of their hidden risks.

Practical prescriptions for comparing and choosing alternative models are given, including stress testing, scenario analysis, reverse engineering of models from counterparty quotes and consensus platforms, and monitoring of model evolution to minimise model losses.

The course also covers liquidity and discounting for different derivatives, statistical arbitrage with quantitative models, efficient modelling of correlation for various assets, an update of credit and counterparty risk models based on the analysis of the latest events, and the modelling and management of basis risk. Other points that are crucial for the validation of models and their practical applications are also analysed: hedging analysis, P&L analysis, calibration stability, and monitoring of the accuracy of approximations.

Información importante
¿Qué objetivos tiene esta formación?

¿Esta formación es para mí?

Managers and Directors in Validation and Financial engineering
Quants in Front Office and Risk management
Derivatives Traders
Regulators
Structurers
Exotic products managers (pricing strategy development)
Portfolio managers

Requisitos: The foundations of derivatives pricing. Matlab will be used but prior knowledge is not essential.

Instalaciones y fechas

Dónde se imparte y en qué fechas

Inicio Ubicación
A elegir
London
34 Curlew Street, se12nd, London, Inglaterra
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A elegir
New York
New York, Estados Unidos
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¿Qué aprendes en este curso?

Arbitrage
Risk
Monitoring
Calibration
Risk Management
Interest Rates
Market
Options
Credit
Testing
Engineering
IT risk
Equity
Derivatives
Market Liquidity
Methodology
Correlation
Design
Counterparty Risk
SABR
Hedging Analysis
Extrapolations
Mathematical errors
Bond

Temario

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
Correlation
  • 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
Calibration
  • 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

Approximations
  • 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
Extrapolations
  • 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
Arbitrage
  • 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