famamacb20171101101843.html - Factor Models Fama-MacBeth...

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Factor Models Fama-MacBeth regressions One factor model Test the CAPM Several factors model Fama-MacBeth regressions One factor model Here we introduce only one factor: market excess return. 1)Estimate over the sample 1962:1 2014:6 the CAPM for the 25 FF portfolios and plot the Securities Market Line # 1962:1 2014:6 returns <- as.matrix(subset(dataff, select = PR11:PR55, date >= 1962 & date <=2014 + 5/12 + 0.01)) rf <- as.matrix(subset(dataff, select = RF, date >= 1962 & date <=2014 + 5/12 + 0.01)) ex_returns <- returns - rep(rf, ncol(returns)) factors <- as.matrix(subset(dataff, select = exret_mkt, date >= 1962 & date <=2014 + 5/12 + 0.01)) # the first step X <- cbind(matrix(1, nrow(ex_returns), 1), factors) Y <- ex_returns beta <- solve(t(X) %*% X) %*% t(X) %*% Y # same as lm(returns ~ factors) #plotting the security market line mean_exret <- apply(X = ex_returns, 2, mean) beta1=beta[2,] df1=data.frame(mean_exret,beta1) plot(df1$beta1,df1$mean_exret, main="Security Market line", xlab="betas",ylab="mean exc ret") abline(reg = lm(df1$mean_exret ~ df1$beta1), col = "red")
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# second step: aggregating data, estimating + Heteroskedasticity robust vcov matrix
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