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…
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…
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…
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…
