paFm: Compute cumulative mean attribution for factor models

In braverock/factorAnalytics: Factor Analytics for Asset Return Data

Compute cumulative mean attribution for factor models

Description

Decompose total returns into returns attributed to factors and specific returns. An object of class "pafm" is generated, with methods for generic functions plot, summary and print.

Usage

paFm(fit, ...)

Arguments

fit	an object of class tsfm, sfm or ffm.
...	other arguments/controls passed to the fit methods.

Details

Total returns can be decomposed into returns attributed to factors and specific returns.
R_t = \sum b_k * f_kt + u_t, t=1...T
b_k is exposure to factor k and f_kt is factor k's return at time t. The return attributed to factor k is b_k * f_kt and specific return is u_t.

Value

The returned object is of class "pafm" containing

cum.ret.attr.f	N X K matrix of cumulative return attributed to factors.
cum.spec.ret	length-N vector of cumulative specific returns.
attr.list	list of time series of attributed returns for every portfolio.

Author(s)

Yi-An Chen and Sangeetha Srinivasan

References

Grinold, R. and Kahn, R. (1999) Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk. McGraw-Hill.

See Also

fitTsfm, fitFfm for the factor model fitting functions.

The pafm methods for generic functions: plot.pafm, print.pafm and summary.pafm.

Examples

data(managers, package = 'PerformanceAnalytics')
fit <- fitTsfm(asset.names=colnames(managers[, (1:6)]),
               factor.names=c("EDHEC LS EQ","SP500 TR"),
               data=managers)
# without benchmark
paFm(fit)

braverock/factorAnalytics documentation built on Dec. 16, 2024, 1:05 p.m.