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FRM®一级风险管理基础备考公式（2）
文章来源：网络
发布时间：2021-12-30 08:43
阅读：561次
本文继续为大家介绍FRM®一级风险管理基础的备考公式，以下是定量分析备考公式（2），一起来看吧。
Multiple Regression
A simple regression is the two-variable regression with one dependent variable, Yi, and one independent variable, Xi. A multiple regression has more than one independent variable.【点击免费下载>>>更多FRM学习相关资料】
Y = α + β1 X1 + β2 X2 +…. + βk Xk + ε
Standard Error of the Regression (SER)
Measures the degree of variability of the actual Y-values relative to the estimated Y-values from a regression equation. The SER gauges the “fit” of the regression line. The smaller the standard error, the better the fit.
Total Sum of Squares
For the dependent variable in a regression model, there is a total sum of squares (TSS) around the sample mean. total sum of squares (TSS) = explained sum of squares (ESS) + residual sum of squares (RSS)
Coefficient of Determination
Represented by R2, it is a measure of the goodness of fit of the regression.
In a simple two-variable regression, the square root of R2 is the correlation coefficient (r) between Xi and Yi. If the relationship is positive, then
Adjusted R-Squared
Adjusted R2 is used to analyze the importance of an added independent variable to a regression.
Regression Assumption Violations
Heteroskedasticity occurs when the variance of the residuals is not the same across all observations in the sample.
Multicollinearity refers to the condition when two or more of the independent variables, or linear combinations of the independent variables, in a multiple regression are highly correlated with each other.
Covariance Stationary
A time series is covariance stationary if its mean, variance, and covariances with lagged and leading values are stable over time. Covariance stationarity is a requirement for using autoregressive (AR) models. Models that lack covariance stationarity are unstable and do not lend themselves to meaningful forecasting.
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