Mean-Variance Portfolio Theory
After completing this reading, you should be able to: Explain the mathematics and summary statistics of portfolios. Calculate the risk and return of an asset, given appropriate inputs. Calculate the risk and expected return of a portfolio of many risky…
Foreign Exchange Markets
After completing this reading, you should be able to: Explain and describe the mechanics of spot quotes, forward quotes, and future quotes in the foreign exchange market and distinguish between the bid and ask exchange rates. Calculate bid-ask spread and…
Regression Diagnostics
After completing this reading, you should be able to: Explain how to test whether regression is affected by heteroskedasticity. Describe approaches to using heteroskedastic data. Characterize multicollinearity and its consequences; distinguish between multicollinearity and perfect collinearity. Describe the consequences of…
Capital Structure in Banks
After completing this reading, you should be able to: Evaluate a bank’s economic capital relative to its level of credit risk. Identify and describe important factors used to calculate economic capital for credit risk: the probability of default, exposure, and…
What is ERM?
After completing this reading, you should be able to: Describe enterprise risk management (ERM) and compare and contrast differing definitions of ERM. Compare the benefits and costs of ERM and describe the motivations for a firm to adopt an ERM…
Sample Moments
After completing this reading, you should be able to: Estimate the mean, variance, and standard deviation using sample data. Explain the difference between a population moment and a sample moment. Distinguish between an estimator and an estimate. Describe the bias…
Nonstationary Time Series
After completing this reading, you should be able to: Describe linear and nonlinear time trends. Explain how to use regression analysis to model seasonality. Describe a random walk and a unit root. Explain the challenges of modeling time series containing…
Stationary Time Series
After completing this reading, you should be able to: Describe the requirements for a series to be covariance stationary. Define the autocovariance function and the autocorrelation function. Define white noise; describe independent white noise and normal (Gaussian) white noise. Define…
Multivariate Random Variables
After completing this reading, you should be able to: Explain how a probability matrix can be used to express a probability mass function (PMF). Compute the marginal and conditional distributions of a discrete bivariate random variable. Explain how the expectation…
Random Variables
After completing this reading, you should be able to: Describe and distinguish a probability mass function from a cumulative distribution function and explain the relationship between these two. Understand and apply the concept of a mathematical expectation of a random…
