[Uwgarp-commits] r92 - in pkg/GARPFRM: R sandbox vignettes

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Fri Feb 21 22:01:09 CET 2014 

Author: tfillebeen
Date: 2014-02-21 22:01:08 +0100 (Fri, 21 Feb 2014)
New Revision: 92

Added:
   pkg/GARPFRM/vignettes/Correlation_Volatility_TF.pdf
   pkg/GARPFRM/vignettes/Correlation_Volatility_TF.tex
Modified:
   pkg/GARPFRM/R/capm.R
   pkg/GARPFRM/sandbox/test_EWMA_GARCH.R
   pkg/GARPFRM/vignettes/CAPM_TF.Rnw
   pkg/GARPFRM/vignettes/CAPM_TF.pdf
Log:
Vignette update CAPM, draft of Correlation_Volatility vignette added

Modified: pkg/GARPFRM/R/capm.R
===================================================================
--- pkg/GARPFRM/R/capm.R	2014-02-19 07:27:38 UTC (rev 91)
+++ pkg/GARPFRM/R/capm.R	2014-02-21 21:01:08 UTC (rev 92)
@@ -125,8 +125,12 @@
 getStatistics.capm_uv <- function(object){
   if(!inherits(object, "capm_uv")) stop("object must be of class capm_uv")
   tmp_sm <- summary.lm(object)
-  # gets t-value, and p-value of model
+  # Gets t-value, and p-value of model
   result = coef(tmp_sm)[,c(1:4)]
+  tstat = (result[2,1] - 1 )/result[2,2]
+  # Two sided t-test
+  pvalue= (2*(1 - pt(abs(tstat),df=nrow(object$x_data)-2)))
+  result[2,3:4] = cbind(tstat, pvalue)
   rownames(result) = cbind(c(paste("alpha.", colnames(object$y_data))),c(paste("beta. ", colnames(object$y_data))))
   return(result)
 }
@@ -146,6 +150,10 @@
     n = i*2 +1
   }
   rownames(tmp_sm) <- c(holder)
+  tstat = (tmp_sm[seq(2,nrow(tmp_sm),2),1] - 1 )/tmp_sm[seq(2,nrow(tmp_sm),2),2]
+  #' Two sided t-test
+  pvalue = (2*(1 - pt(abs(tstat),df=nrow(object$x_data)-2)))
+  tmp_sm[seq(2,nrow(tmp_sm),2),3:4] = cbind(tstat,pvalue)
   return(tmp_sm)
 }
 
@@ -154,13 +162,13 @@
 plot.capm_uv <- function(object){
   xlab <- colnames(object$x_data)
   ylab <- colnames(object$y_data)
-  plot(x=coredata(object$x_data), y=(object$y_data), xlab=xlab, ylab=ylab, main="CAPM Plot")
+  plot(x=coredata(object$x_data), y=(object$y_data), xlab=xlab, ylab=ylab, main="CAPM")
   abline(object)
   abline(h=0,v=0,lty=3)
   alpha = coef(summary(object))[1,1]
-  a_tstat = coef(summary(object))[1,3]
+  a_tstat = coef(summary(object))[1,2]
   beta = coef(summary(object))[2,1]
-  b_tstat = coef(summary(object))[2,3]
+  b_tstat = coef(summary(object))[2,2]
   legend("topleft", legend=c(paste("alpha =", round(alpha,dig=2),"(", round(a_tstat,dig=2),")"),
                              paste("beta =", round(beta,dig=2),"(", round(b_tstat,dig=2),")")), cex=.8, bty="n")
   
@@ -215,10 +223,11 @@
 hypTest.capm_uv <- function(object,CI = 0.05){
   if(!inherits(object, "capm_uv")) stop("object must be of class capm_uv")
   tmp_sm = getStatistics(object)
-  tmp_A = tmp_sm[1,3] < CI
-  tstat = (tmp_sm[2,2] - 1 )/tmp_sm[2,3]
+  tmp_A = tmp_sm[1,4] < CI
+  # tstat = (tmp_sm[2,1] - 1 )/tmp_sm[2,2]
   #' Two sided t-test
-  tmp_B = (2*(1 - pt(abs(tstat),df=nrow(object$x_data)-1))) < CI
+  # tmp_B = (2*(1 - pt(abs(tstat),df=nrow(object$x_data)-2))) < CI
+  tmp_B = tmp_sm[2,4] < CI
   result = list(alpha = tmp_A, beta = tmp_B)
   return(result)
 }
@@ -229,9 +238,10 @@
   if(!inherits(object, "capm_mlm")) stop("object must be of class capm_mlm")
   tmp_sm = getStatistics(object)
   tmp_A = tmp_sm[seq(1,nrow(tmp_sm),2),4] < CI
-  tstat = (tmp_sm[seq(2,nrow(tmp_sm),2),1] - 1 )/tmp_sm[seq(2,nrow(tmp_sm),2),2]
+  # tstat = (tmp_sm[seq(2,nrow(tmp_sm),2),1] - 1 )/tmp_sm[seq(2,nrow(tmp_sm),2),2]
   #' Two sided t-test
-  tmp_B = (2*(1 - pt(abs(tstat),df=nrow(object$x_data)-2))) < CI
+  # tmp_B = (2*(1 - pt(abs(tstat),df=nrow(object$x_data)-2))) < CI
+  tmp_B = tmp_sm[seq(2,nrow(tmp_sm),2),4] < CI
   result = list(alpha = tmp_A, beta = tmp_B)  
   return(result)
 }
\ No newline at end of file

Modified: pkg/GARPFRM/sandbox/test_EWMA_GARCH.R
===================================================================
--- pkg/GARPFRM/sandbox/test_EWMA_GARCH.R	2014-02-19 07:27:38 UTC (rev 91)
+++ pkg/GARPFRM/sandbox/test_EWMA_GARCH.R	2014-02-21 21:01:08 UTC (rev 92)
@@ -6,20 +6,18 @@
 R <- largecap.ts[, 1:4]
 options(digits=4)
 
-
 # Remember: log-returns for GARCH analysis
-temp_1 = R[,1] 
-temp_2 = R[,3]
+asset1 = R[,1] 
+asset2 = R[,3]
    
 # Create combined data series
-temp = merge(temp_1,temp_2)
+cAssets = cbind(asset1,asset2)
 
 # Scatterplot of returns
-plot(coredata(temp_1), coredata(temp_2), xlab=colnames(temp_1), ylab=colnames(temp_2), 
-     main ="Scatterplot of Returns")
+plot(coredata(asset1), coredata(asset2), xlab=colnames(asset1), ylab=colnames(asset2), main ="Scatterplot of Returns")
 abline(h=0,v=0,lty=3)
 
-# Compute rolling cor
+# Compute rolling cor to illustrate the later smoothing effect of EWMA
 cor.fun = function(x){
   cor(x)[1,2]
 }
@@ -28,51 +26,47 @@
   cov(x)[1,2]
 }
 
-roll.cov = rollapply(as.zoo(temp), FUN=cov.fun, width=20,
-                     by.column=FALSE, align="right")
-roll.cor = rollapply(as.zoo(temp), FUN=cor.fun, width=20,
-                     by.column=FALSE, align="right")
+rollCov = rollapply(cAssets, FUN=cov.fun, width=10, by.column=FALSE, align="right")
+rollCor = rollapply(cAssets, FUN=cor.fun, width=10, by.column=FALSE, align="right")
 par(mfrow=c(2,1))
 # First Rolling Cov
-plot(roll.cov, main="20-Day Rolling Cov",
-     ylab="covariance", lwd=3, col="blue")
+plot(na.omit(rollCov), main="20-Day Rolling Cov", ylab="covariance")
 grid()
-abline(h=cov(temp)[1,2], lwd=3, col="red")
+abline(h=cov(cAssets)[1,2], lwd=3, col="red")
 
 # Second Rolling Cor
-plot(roll.cor, main="20-Day Rolling Cor",
-     ylab="correlation", lwd=3, col="blue")
+plot(na.omit(rollCor), main="20-Day Rolling Cor",ylab="correlation")
 grid()
-abline(h=cor(temp)[1,2], lwd=3, col="red")
+abline(h=cor(cAssets)[1,2], lwd=3, col="red")
 par(mfrow=c(1,1))
 
 # Calculate EWMA cov and cor, applying default lambda - 0.96
-tempEWMACov <- EWMA(temp,lambda=0.94, initialWindow=10, cor=FALSE)
-tempEWMACor <- EWMA(temp,lambda=0.94, initialWindow=10, cor=TRUE)
+cAssetsEWMACov <- EWMA(cAssets,lambda=0.94, initialWindow=30, cor=FALSE)
+cAssetsEWMACor <- EWMA(cAssets,lambda=0.94, initialWindow=30, cor=TRUE)
 
 # Plots
 par(mfrow=c(2,1))
-plot(tempEWMACov,asset1=1,asset2=2)
-plot(tempEWMACor, asset1=1,asset2=2)
+plot(cAssetsEWMACov,asset1=1,asset2=2)
+plot(cAssetsEWMACor, asset1=1,asset2=2)
 par(mfrow=c(1,1))
 
 # Compute EWMA cov and cor for longer half-life of 
 halfLife = log(0.5)/log(0.94) + 5
 lambda = exp(log(0.5)/halfLife)
-covEwma <- EWMA(temp, lambda)
+covEwma <- EWMA(cAssets, lambda)
 
 # Garch11 testing
 data(returns)
-tempReturns = cbind(returns[, "SPY"],returns[,"AAPL"])
+cAssetsReturns = cbind(returns[, "SPY"],returns[,"AAPL"])
 # Dynamic Conditional Cor/Cov
-garch11 <- garch11(tempReturns)
+garch11 <- garch11(cAssetsReturns)
 
 # many extractor functions - see help on DCCfit object
 # coef, likelihood, rshape, rskew, fitted, sigma, residuals, plot, infocriteria, rcor, rcov show, nisurface
 # show dcc fit
 garch11
 
-# Conditional sd of each series
+# Conditional Sigma (vs Realized Absolute Returns)
 plot(garch11, which=2)
 
 # Conditional covar of each series

Modified: pkg/GARPFRM/vignettes/CAPM_TF.Rnw
===================================================================
--- pkg/GARPFRM/vignettes/CAPM_TF.Rnw	2014-02-19 07:27:38 UTC (rev 91)
+++ pkg/GARPFRM/vignettes/CAPM_TF.Rnw	2014-02-21 21:01:08 UTC (rev 92)
@@ -59,67 +59,60 @@
 \section{Fitting CAPM}
 \subsection{Selected Returns Time Series}
 <<ex1>>=
-# 'Load the GARPFRM package and the CAPM dataset.
+# 'Load the GARPFRM package and CRSP dataset for CAPM analysis.
 suppressMessages(library(GARPFRM))
 options(digits=3)
 data(crsp.short)
-data(cons)
 
 stock.df <- largecap.ts[, 1:20]
-cons <- xts(cons[,2], index(largecap.ts))
-colnames(cons)= c("CONS")
-R.market <- largecap.ts[, "market"]
+mrkt <- largecap.ts[, "market"]
 rfr <- largecap.ts[, "t90"]
 
-colnames(stock.df)
+# Plot first four stocks from 
+plot.zoo(stock.df[,1:4], main="First Four Large Cap Returns")
 @
 
 
-Summarize the first and last  data values corresponding to the first 5 dates for the first 5 returns.
+Summarize the start and end dates corresponding to the first 4 large cap returns.
 <<ex2>>=
-head(stock.df[,1:5])
-tail(stock.df[,1:5])
-# Count the number of rows
+# Illustrate the type of data being analzyed: start-end dates.
+start(stock.df[,1:4])
+end(stock.df[,1:4])
+# Count the number of rows: sample size.
 nrow(stock.df)
 @
 \subsection{Estimate Excess Returns}
 Estimate excess returns: subtracting off risk-free rate.
-To strip off the dates and just return a plain vector/matrix coredata() can be used.
 <<ex3>>=
-# Excess Returns
+# Excess Returns initialized before utilizing in CAPM
 exReturns <- Return.excess(stock.df, rfr)
 colnames(exReturns)= c(colnames(stock.df))
 @
 
 \subsection{Fitting CAPM Model: Univariate}
-Run CAPM regression for AMAT
+Run CAPM regression for AMAT and estimate CAPM with $\alpha=0$ \& $\beta=1$ for asset.
 <<ex4>>=
 # Univariate CAPM
-uv <- CAPM(exReturns[,1], R.market)
-coef(summary(uv))
+uv <- CAPM(exReturns[,1], mrkt)
+getStatistics(uv)
 
 # Plot data with regression line
 plot(uv)
 @
-\subsection{Fitting CAPM Model: Multiple Linear Model}
-Run CAPM regression for AMAT
+\subsection{CAPM Model: Multiple Asset Analysis}
+Run CAPM regression
 <<ex5>>=
-# MLM CAPM
-mlm <- CAPM(exReturns[,1:3], R.market)
-coef(summary(mlm))
+# MLM CAPM for AMAT, AMGN, and CAT
+mlm <- CAPM(exReturns[,1:3], mrkt)
+getStatistics(mlm)
 
 # Plot data with regression line
 plot(mlm)
 @
 \section{Testing CAPM}
-\subsection{Retrieve CAPM Statistics}
-Estimating CAPM with $\alpha=0$ \& $\beta=1$ for asset.
+\subsection{Retrieve $\alpha$ \& $\beta$ and Estimate Result Significance}
+Retrieve $\alpha$ \& $\beta$ from CAPM object for one or multiple assets and run hypothesis test.
 <<ex6>>=
-getStatistics(uv)
-@
-\subsection{Estimate Significance and Test Beta Results}
-Retrieve tstats from function for assets.
-<<ex7>>=
 # For uv
 getBetas(uv)
 getAlphas(uv)
@@ -133,9 +126,9 @@
 \subsection{Estimate Expected Returns and Plot}
 Plot expected return versus beta.
 Estimate expected returns
-<<ex8>>=
+<<ex7>>=
 # MLM CAPM
-mlm <- CAPM(exReturns[,], R.market)
+mlm <- CAPM(exReturns[,], mrkt)
 
 # Plot expected returns versus betas
 chartSML(mlm)
@@ -144,10 +137,15 @@
 \section{Consumption-Oriented CAPM}
 \subsection{Fitting C-CAPM}
 Run C-CAPM regression for CONS (Consumption).
-<<ex9>>=
-capm.cons = CAPM(cons, R.market)
-summary(capm.cons)
+<<ex8>>=
+# Load FED consumption data: CONS
+data(cons)
+cons <- xts(cons[,2], index(largecap.ts))
+colnames(cons)= c("CONS")
 
+capm.cons = CAPM(cons, mrkt)
+coef(summary(capm.cons))
+
 # Plot data with regression line
 plot(capm.cons)
 @

Modified: pkg/GARPFRM/vignettes/CAPM_TF.pdf
===================================================================
--- pkg/GARPFRM/vignettes/CAPM_TF.pdf	2014-02-19 07:27:38 UTC (rev 91)
+++ pkg/GARPFRM/vignettes/CAPM_TF.pdf	2014-02-21 21:01:08 UTC (rev 92)
@@ -1,126 +1,181 @@
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[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/uwgarp -r 92

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