OpRisk Data and Governance
After completing this reading, you should be able to: Describe the seven Basel II event risk categories and identify examples of operational risk events in each category. Summarize the process of collecting and reporting internal operational loss data, including the…
Multifactor Models of Risk-Adjusted Asset Returns
After completing this reading, you should be able to: Explain the arbitrage pricing theory (APT), describe its assumptions, and compare the APT to the CAPM. Describe the inputs (including factor betas) to a multifactor model and explain the challenges of using…
Statistical Correlation Models – Application to Finance
There are three popular correlation models that are statistical which we seek to discuss in this chapter. These models are: Spearman rank correlation. Pearson correlation measure. Kendall \(\tau \). Two or more variables usually have a degree of association that…
Modeling and Forecasting Seasonality
[vsw id=”hGBpM3dtrYY” source=”youtube” width=”611″ height=”344″ autoplay=”no”] 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…
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…
Simulation and Bootstrapping
After completing this reading, you should be able to: Describe the basic steps to conduct a Monte Carlo simulation and illustrate how this simulation method is used to approximate moments or other quantities. Describe ways to reduce the Monte Carlo…
Correlations and Copulas
[vsw id=”yFl5kq4JN_E” source=”youtube” width=”611″ height=”344″ autoplay=”no”] 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…
Modeling and Forecasting Trend
[vsw id=”mJcKDkfz4FY” source=”youtube” width=”611″ height=”344″ autoplay=”no”] 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,…
Characterizing Cycles
[vsw id=”eC9QCBH5K0g” source=”youtube” width=”611″ height=”344″ autoplay=”no”] 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…
