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Managing Model Risk for Quants and Traders

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.

All delegates will receive a copy of Dr Morini's book, "Understanding and Managing Model Risk: A Practical Guide for Quants, Traders and Validators" published by Wiley Finance.

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  • Date:
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  • Central London
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Who The Course is For
  • 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
Prior Knowledge

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

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