Model Risks von Edu Pristine

Dozent des Vortrages Model Risks

Edu Pristine

Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading International Training providers for Finance Certifications like FRM®, CFA®, PRM®, Business Analytics, HR Analytics, Financial Modeling, Operational Risk Modeling etc. It was founded by industry professionals who have worked in the area of investment banking and private equity in organizations such as Goldman Sachs, Crisil - A Standard & Poors Company, Standard Chartered and Accenture.

EduPristine has conducted corporate training for various leading corporations and colleges like JP Morgan, Bank of America, Ernst & Young, Accenture, HSBC, IIM C, NUS Singapore etc. EduPristine has conducted more than 500,000 man-hours of quality training in finance.
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... with trying to capture an observed phenomenon using a financial model. If the output of a model is a ...

... heavy tailed, we might mistake a lognormal P/L process for a normal one, and so on, and it is rarely easy to identify the 'correct' process. Missing risk factors: We might ignore factors such as stochastic volatility or fail to consider enough points ...

... not replaced it when a superior model became available. We might run Monte Carlo simulations with a poor random number generator or an insufficient number of trials. Model Implementation Risk: ...

... period sample for modeling. Risk models are prone to calibration problems for volatilities and correlations. Programming problems: Errors arising from poor programming, ...

... 'Normal' so that expression will be distributed as with n - 1 degrees of freedom. This eventually results in a closed loop confidence interval for VaR that takes care of uncertainty about the volatility parameter. Two parameter models: An example of such a ...

... of Monte Carlo process as follows (it can be extended to n assets): Means would be drawn from normal distributions. Variances and covariance from Chi-square distributions ...

... Check results and test the proposed model. Independent Risk Oversight: This unit should encompass risk measurement as well as risk management, should be independent of line execution Areas, and ...

... Identify, evaluate and key assumptions: Users should explicitly set out the key assumptions on which a model is based, evaluate the extent to which the model's results depend on these assumptions, and check them as ...

... extremely revealing, and simple histograms or plots often show up errors that might otherwise be very hard to detect. Re-evaluate models periodically: Models should be recalibrated ...

... the temptation to overlook a model just because it's use has started spinning profits. Hear out concerns of mid-office and back-office in this context and do not over depend on front office traders. Avoid situations of model ...

... Independent Risk Oversight (IRO) unit. Ideally a middle office unit responsible for overall risk management and measurement. The unit head should report directly report to CEO and may even sit on the ...

... numbers generated in the model are biased. ii.Implementation risk can occur when different users input different parameters into the model. iii. An incorrect model application occurs when a well-specified model ...

... other units for it to function properly. Which of the following statements is most likely incorrect? A. The compensation of IRO should be based on the compensation of other units. B. A model should be properly vetted ...

... random number generator is a form of incorrect model application. A model specification error occurs when the ...