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Another approach to model risk is the worst-case, or minmax approach, advocated in decision theory by Gilboa and Schmeidler. [22] In this approach one considers a range of models and minimizes the loss encountered in the worst-case scenario. This approach to model risk has been developed by Cont (2006). [23]
Financial risk modeling is the use of formal mathematical and econometric techniques to measure, monitor and control the market risk, credit risk, and operational risk on a firm's balance sheet, on a bank's accounting ledger of tradeable financial assets, or of a fund manager's portfolio value; see Financial risk management.
This model provides a rapid update of market variance which is incorporated into the update of F, resulting in a more dynamic model of risk. In particular it accounts for the convergence of asset returns and consequent loss of diversification that occurs in portfolios during periods of market turbulence.
Model risk quantifies the consequences of using the wrong models in risk measurement, pricing, or portfolio selection. The main element of a statistical model in finance is a risk factor distribution. Recent papers treat the factor distribution as unknown random variable and measuring risk of model misspecification.
Risk premium is the product of the market price of risk and the quantity of risk, and the risk is the standard deviation of the portfolio. The CML equation is : R P = I RF + (R M – I RF)σ P /σ M. where, R P = expected return of portfolio I RF = risk-free rate of interest R M = return on the market portfolio σ M = standard deviation of the ...
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The input into a typical cat modeling software package is information on the exposures being analyzed that are vulnerable to catastrophe risk. The exposure data can be categorized into three basic groups: Information on the site locations, referred to as geocoding data (street address, postal code, county/CRESTA zone, etc.)
The methods (or approaches) increase in sophistication and risk sensitivity with AMA being the most advanced of the three. Under AMA the banks are allowed to develop their own empirical model to quantify required capital for operational risk. Banks can use this approach only subject to approval from their local regulators.