Abstract: A computerized method for generating risk forecasts is provided. A set of securities is selected. A set of risk factors is selected. The risk factor returns a determined. A risk factor covariance matrix and an idiosyncratic variance matrix are constructed. For each risk factor, a factor loading coefficient is determined for each selected security. The risk factor covariance matrix is projected into a future forecast. The idiosyncratic variance matrix is projected into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities.

Abstract: A computerized method for generating risk forecasts is provided. A set of securities is selected. A set of risk factors is selected. The risk factor returns a determined. A risk factor covariance matrix and an idiosyncratic variance matrix are constructed. For each risk factor, a factor loading coefficient is determined for each selected security. The risk factor covariance matrix is projected into a future forecast. The idiosyncratic variance matrix is projected into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities.

Abstract: A computerized method for generating risk forecasts is provided. A set of securities is selected. A set of risk factors is selected. The risk factor returns a determined. A risk factor covariance matrix and an idiosyncratic variance matrix are constructed. For each risk factor, a factor loading coefficient is determined for each selected security. The risk factor covariance matrix is projected into a future forecast. The idiosyncratic variance matrix is projected into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities.

Abstract: A factor risk model based method for generating risk forecasts. In one embodiment, the method includes: selecting a set of securities; selecting a set of risk factors; determining the risk factor returns; constructing a risk factor covariance matrix; constructing an idiosyncratic variance matrix; determining, for each risk factor, a factor loading coefficient for each selected security; projecting the risk factor covariance matrix into a future forecast; and projecting the idiosyncratic variance matrix into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities. In some embodiments, the step of estimating factor loadings includes performing a time series regression to obtain the sensitivity of each stocks' return to variations in the factor's return.

Abstract: A factor risk model based method for generating risk forecasts. In one embodiment, the method includes: selecting a set of securities; selecting a set of risk factors; determining the risk factor returns; constructing a risk factor covariance matrix; constructing an idiosyncratic variance matrix; determining, for each risk factor, a factor loading coefficient for each selected security; projecting the risk factor covariance matrix into a future forecast; and projecting the idiosyncratic variance matrix into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities. In some embodiments, the step of estimating factor loadings includes performing a time series regression to obtain the sensitivity of each stocks' return to variations in the factor's return.