LECTURE_9_NOTES_Updated

LECTURE_9_NOTES_Updated - LECTURE 9 NOTES MODEL RISK AND...

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1 LECTURE 9 NOTES MODEL RISK AND LIQUIDITY RISK --Model risk definition --Models for different kinds of products (Nonlinear, actively traded, structured) --Problems in model building --Liquidity risk definition --Long-Term Capital Management (LTCM) Introduction Model Risk: Risk related to the models that a financial institution uses to value different products. --Models are mostly necessary when pricing products that are relatively illiquid. --Since, when there’s an active market for a product, prices can be observed in the market. --There are two types of model risk: a) The model will give the wrong price at the time a product is traded. This can result in a price too high or too low. b) The other risk concerns hedging. If a wrong model is used, the hedges that are set up will be also wrong. Models for different kinds of products (Nonlinear, actively traded, structured) --Linear products are those whose payoff function is linear. Pricing linear products is straightforward (e.g. use PV calculation). --We do not really need a model for actively traded products (e.g. use interpolation). --Nonlinear products are those whose payoff changes with time and space. --Products that are tailored to the needs of clients are referred to as structured products. A FI must rely on a model to determine the price it charges the client for these products. Both pricing and hedging can be incorrect for these products.
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2 --Models are used more significantly when hedging. --Within-model hedging: Risk of changes in variables that are assumed to be uncertain by the model (e.g. price changes). --Outside-model hedging: Risk of changes in variables that are assumed to be constant (deterministic) by the model (e.g. volatility). Problems in Model Building --Models of finance describe the behavior of market variables. This behavior depends on the actions of human beings. Therefore, models are only approximations. (See Behavioral Finance literature) (Daniel Kahneman, 2002, Nobel Prize Winner) --The parameters of the models in finance are not constant. The process of choosing model parameters is known as calibration. (Robert Engle, 2003, Nobel Prize Winner) --Overfitting: Building a model completely reflecting the data. This makes the model not flexible and general. --Overparameterization: Building a model that has lots of lags, variables, and nonlinear
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This note was uploaded on 10/16/2011 for the course FIN 353 taught by Professor Cobus during the Spring '08 term at S.F. State.

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LECTURE_9_NOTES_Updated - LECTURE 9 NOTES MODEL RISK AND...

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