Candidate’s objectives: 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…

Candidate’s objectives: 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…

Candidate’s objectives: 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…

Candidate’s objectives: 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…

Candidate’s objectives: 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…

Candidate’s objectives: 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…

Candidate’s objectives: 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…

Candidate’s objectives: 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…

Candidate’s objectives: 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…

Candidate’s objectives: After completing this reading you should be able to: Calculate and interpret confidence intervals for regression coefficients. Interpret the \(p-value\). Interpret hypothesis tests about regression coefficients. Evaluate the implications of homoskedasticity and heteroskedasticity. Determine the conditions under which…