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…

More Details
Understanding the Securitization of Subprime Mortgage Credit

After completing this reading, you should be able to: Explain the subprime mortgage credit securitization process in the United States. Identify and describe key frictions in subprime mortgage securitization and assess the relative contribution of each factor to the subprime…

More Details
Simulation and Bootstrapping

After completing this reading, you should be able to: Describe the basic steps to conduct a Monte Carlo simulation. Describe ways to reduce the Monte Carlo sampling error. Explain the use of antithetic and control variates in reducing Monte Carlo…

More Details
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…

More Details
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…

More Details
Characterizing 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…

More Details
X