portSdDecomp: Decompose portfolio standard deviation into individual factor...

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/portSdDecomp.R

Description

Compute the factor contributions to standard deviation (Sd) of portfolio returns based on Euler's theorem, given the fitted factor model.

Usage

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portSdDecomp(object, ...)

## S3 method for class 'tsfm'
portSdDecomp(object, weights = NULL, factor.cov,
  use = "pairwise.complete.obs", ...)

## S3 method for class 'ffm'
portSdDecomp(object, weights = NULL, factor.cov, ...)

Arguments

object

fit object of class tsfm, or ffm.

...

optional arguments passed to cov.

weights

a vector of weights of the assets in the portfolio. Default is NULL, in which case an equal weights will be used.

factor.cov

optional user specified factor covariance matrix with named columns; defaults to the sample covariance matrix.

use

an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". Default is "pairwise.complete.obs".

Details

The factor model for a portfolio's return at time t has the form

R(t) = beta'f(t) + e(t) = beta.star'f.star(t)

where, beta.star=(beta,sig.e) and f.star(t)=[f(t)',z(t)]'.

By Euler's theorem, the standard deviation of the portfolio's return is given as:

portSd = sum(cSd_k) = sum(beta.star_k*mSd_k)

where, summation is across the K factors and the residual, cSd and mSd are the component and marginal contributions to Sd respectively. Computing portSd and mSd is very straight forward. The formulas are given below and details are in the references. The covariance term is approximated by the sample covariance.

portSd = sqrt(beta.star''cov(F.star)beta.star)
mSd = cov(F.star)beta.star / portSd

Value

A list containing

portSd

factor model Sd of portfolio return.

mSd

length-(K + 1) vector of marginal contributions to Sd.

cSd

length-(K + 1) vector of component contributions to Sd.

pcSd

length-(K + 1) vector of percentage component contributions to Sd.

Where, K is the number of factors.

Author(s)

Douglas Martin, Lingjie Yi

See Also

fitTsfm, fitFfm for the different factor model fitting functions.

portVaRDecomp for portfolio factor model VaR decomposition. portEsDecomp for portfolio factor model ES decomposition.

Examples

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# Time Series Factor Model
data(managers)
fit.macro <- factorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
                     factor.names=colnames(managers[,(7:9)]),
                     rf.name=colnames(managers[,10]), data=managers)
decomp <- portSdDecomp(fit.macro)
# get the factor contributions of risk
decomp$cSd

# random weights 
wts = runif(6)
wts = wts/sum(wts)
names(wts) <- colnames(managers)[1:6]
portSdDecomp(fit.macro, wts)

# Fundamental Factor Model
data("stocks145scores6")
dat = stocks145scores6
dat$DATE = as.yearmon(dat$DATE)
dat = dat[dat$DATE >=as.yearmon("2008-01-01") & 
          dat$DATE <= as.yearmon("2012-12-31"),]

# Load long-only GMV weights for the return data
data("wtsStocks145GmvLo")
wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)  
                                                     
# fit a fundamental factor model
fit.cross <- fitFfm(data = dat, 
              exposure.vars = c("SECTOR","ROE","BP","MOM121","SIZE","VOL121",
              "EP"),date.var = "DATE", ret.var = "RETURN", asset.var = "TICKER", 
              fit.method="WLS", z.score = TRUE)
              
decomp = portSdDecomp(fit.cross) 
# get the factor contributions of risk 
decomp$cSd
portSdDecomp(fit.cross, wtsStocks145GmvLo)               
 

AvinashAcharya/Test_factorAnalytics documentation built on May 5, 2019, 9:22 a.m.