File Name: understanding and managing model risk .zip
A proposed best practice model validation framework for banks. Pieter J. The credit crisis of provoked added concern about the use of models in finance. Measuring and managing model risk has subsequently come under scrutiny from regulators, supervisors, banks and other financial institutions. Regulatory guidance indicates that meticulous monitoring of all phases of model development and implementation is required to mitigate this risk. Considerable resources must be mobilised for this purpose.
Download the full paper - PDF Version. The Pandemic has created an environment of uncertainty to a level not seen by the industry, increasing the risks faced by financial institutions, including the risks associated with the use of models. For many institutions, models play an integral role in conducting day to day operations. Prominent examples of its use include evaluating risks and capital, defining funding requirements, understanding customer behaviors, managing data analytics, and making investment decisions. Models built on data from past recessions with known outcomes are inadequate for modeling pandemic tail risk, which has become reality in the current unknown environment. Regulatory focus on model risk will likely be increased given the important role models play in day to day bank operations across all three lines of defense and the increased inherent risk due to the pandemic. Sound model risk management involves having strong practices at various levels of the organization.
Understanding and Managing Model Risk: A Practical Guide for Quants, Traders and Validators , by Massimo Morini, examines the risks arising from the use of models in calibration, pricing, hedging, and extrapolation in various asset classes. The author starts in Chap. For the comparison of structural versus reduced form models, the author looks at a highly leveraged note on a reference entity. This note must be de-leveraged if a certain trigger occurs and sometimes the face value of the note may not cover the losses. Since in structural models, the default process is to some degree predictable, the estimated gap risk is significantly, but potentially unrealistically, lower for structural models. For equities, the author analyzes local and stochastic volatility models for the pricing of equity barriers options and mentions that both models deliver potentially very different prices. For rates, the author analyzes short-rate and market models for the pricing of Bermudan options.
In finance , model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial securities. Burke regards failure to use a model instead over-relying on expert judgment as a type of model risk. Volatility is the most important input in risk management models and pricing models. Uncertainty on volatility leads to model risk. Derman believes that products whose value depends on a volatility smile are most likely to suffer from model risk. He writes "I would think it's safe to say that there is no area where model risk is more of an issue than in the modeling of the volatility smile. Buraschi and Corielli formalise the concept of 'time inconsistency' with regards to no-arbitrage models that allow for a perfect fit of the term structure of the interest rates.
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