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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
LongTerm 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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Models are used more significantly when hedging.
Withinmodel hedging: Risk of changes in variables that are assumed to be uncertain by
the model (e.g. price changes).
Outsidemodel 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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 Spring '08
 cobus
 Liquidity, liquidity risk, LongTerm Capital Management, Model building

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