Correlations and Copulas

After completing this reading you should be able to: Define correlation and covariance and differentiate between correlation and dependence. Calculate covariance using the EWMA and GARCH(1,1) models. Apply the consistency condition to covariance. Describe the procedure of generating samples from…

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Simulation Methods

After completing this reading you should be able to: Describe the basic steps to conduct a Monte Carlo simulation. Describe ways to reduce Monte Carlo sampling error. Explain how to use antithetic variate technique to reduce Monte Carlo sampling error….

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Volatility

After completing this reading you should be able to: Define and distinguish between volatility, variance rate, and implied volatility. Describe the power law. Explain how various weighting schemes can be used in estimating volatility. Apply the exponentially weighted moving average…

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Modeling Cycles: MA, AR, and ARMA Models

After completing this reading you should be able to: Describe the properties of the first-order moving average (MA(1)) process, and distinguish between autoregressive representation and moving average representation. Describe the properties of a general finite-order process of order \(q\) (MA(\(q\)))…

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Charaterizing Cycles

After completing this reading you should be able to: Define covariance stationary, autocovariance function, autocorrelation function, partial autocorrelation function, and autoregression. Describe the requirements for a series to be covariance stationary. Explain the implications of working with models that are…

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Modeling and Forecasting Seasonality

After completing this reading you should be able to: Describe the sources of seasonality and how to deal with it in time series analysis. Explain how to use regression analysis to model seasonality. Explain how to construct an h-step-ahead point…

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Modeling and Forecasting Trend

After completing this reading you should be able to: Describe linear and nonlinear trends. Describe trend models to estimate and forecast trends. Compare and evaluate model selection criteria, including mean squared error (MSE), s2, the Akaike information criterion (AIC), and…

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Hypothesis Tests and Confidence Intervals in Multiple Regression

After completing this reading you should be able to: Construct, apply, and interpret hypothesis tests and confidence intervals for a single coefficient in a multiple regression. Construct, apply, and interpret joint hypothesis tests and confidence intervals for multiple coefficients in…

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Linear Regression with Multiple Regressors

After completing this reading you should be able to: Define and interpret omitted variable bias, and describe the methods for addressing this bias. Distinguish between single and multiple regression. Interpret the slope coefficient in a multiple regression. Describe homoskedasticity and…

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GARP Code of Conduct

After completing this reading, you should be able to: Describe the responsibility of each GARP Member with respect to professional integrity, ethical conduct, conflicts of interest, confidentiality of information, and adherence to generally accepted practices in risk management. Describe the…

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