The Capital Asset Pricing Model would not be used to estimate the probable maximum loss that may be incurred at the end of the year. The capital asset pricing model (CAPM) is used to estimate the investors’ expected rate of return on a security or a portfolio of securities. The CAPM uses the security or portfolio’s risk and the market rate of return to calculate the investors’ required return. The theory behind the CAPM is that investors will price investments so that the expected return on a security or a portfolio will be equal to the risk-free rate plus a risk premium proportional to the risk, or beta, for that investment.
Covariance would not be used to estimate the probable maximum loss that may be incurred at the end of the year. Covariance is a statistical measure of the amount by which two securities’ returns move together.
Arbitrage Pricing Theory would not be used to estimate the probable maximum loss that may be incurred at the end of the year. Arbitrage Pricing Theory (APT) is based on the idea that in a competitive financial market, arbitrage will assure equilibrium pricing according to risk and return. Arbitrage is defined as simultaneously purchasing and selling the same asset in different markets where its price is different, in order to profit from the unequal prices. Arbitrage Pricing Theory looks at a number of common risk factors to determine the correct price for a security, with the goal of then using that information to identify securities that are underpriced and can be purchased and immediately resold for a higher price. Arbitrage Pricing Theory does not state what these factors are. They can be anything that affects stock price.
Value-at-Risk can be used to estimate the probable maximum loss that may be incurred at the end of the year. Value-at-Risk (VaR) measures the potential loss in value of a risky asset or event over a defined period for a given confidence interval. It is based on the assumption that the possible outcome of the event is represented by a normal distribution (bell curve). With a normal distribution, we know that 95% of the results will lie within 1.96 standard deviations of the mean, and that 99% of the results will lie within 2.57 standard deviations of the mean. Using this information, we can predict what the range of results will be with a measured level of confidence.
|
