FundamentalFactor.Cov: Covariance Matrix Estimation by Fundamental Factor Model In FinCovRegularization: Covariance Matrix Estimation and Regularization for Finance

Description

Estimate covariance matrix by fitting a fundamental factor model using OLS or WLS regression

Usage

 `1` ```FundamentalFactor.Cov(assets, exposure, method = "WLS") ```

Arguments

 `assets` a N*p matrix of asset returns, N indicates sample size and p indicates the dimension of asset returns `exposure` a p*q matrix of exposure indicator for the fundamental factor model, p corresponds to the dimension of asset returns, q indicates the number of fundamental industries `method` a character, indicating regression method: "OLS" or "WLS"

Value

an estimated p*p covariance matrix

Examples

 ```1 2 3 4 5 6 7 8``` ```data(m.excess.c10sp9003) assets <- m.excess.c10sp9003[,1:10] Indicator <- matrix(0,10,3) dimnames(Indicator) <- list(colnames(assets),c("Drug","Auto","Oil")) Indicator[c("ABT","LLY","MRK","PFE"),"Drug"] <- 1 Indicator[c("F","GM"),"Auto"] <- 1 Indicator[c("BP","CVX","RD","XOM"),"Oil"] <- 1 FundamentalFactor.Cov(assets,exposure=Indicator,method="WLS") ```

FinCovRegularization documentation built on May 29, 2017, 11:47 a.m.