fmVaRDecomp: Decompose VaR into individual factor contributions

In arorar/FactorAnalytics: Factor Analytics

Description

Compute the factor contributions to Value-at-Risk (VaR) of assets' returns based on Euler's theorem, given the fitted factor model. The partial derivative of VaR wrt factor beta is computed as the expected factor return given fund return is equal to its VaR and approximated by a kernel estimator. VaR is computed either as the sample quantile or as an estimated quantile using the Cornish-Fisher expansion.

Usage

fmVaRDecomp(object, ...)

## S3 method for class 'tsfm'
fmVaRDecomp(object, p = 0.95, method = c("modified",
  "gaussian", "historical", "kernel"), invert = FALSE, ...)

## S3 method for class 'sfm'
fmVaRDecomp(object, p = 0.95, method = c("modified",
  "gaussian", "historical", "kernel"), invert = FALSE, ...)

Arguments

object	fit object of class tsfm, sfm or ffm.
...	other optional arguments passed to VaR.
p	confidence level for calculation. Default is 0.95.
method	method for computing VaR, one of "modified","gaussian", "historical", "kernel". Default is "modified". See details.
invert	logical; whether to invert the VaR measure. Default is FALSE.

Details

The factor model for an asset'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 VaR of the asset's return is given by:

VaR.fm = sum(cVaR_k) = sum(beta.star_k*mVaR_k)

where, summation is across the K factors and the residual, cVaR and mVaR are the component and marginal contributions to VaR respectively. The marginal contribution to VaR is defined as the expectation of F.star, conditional on the loss being equal to VaR.fm. This is approximated as described in Epperlein & Smillie (2006); a triangular smoothing kernel is used here.

Computation of the risk measure is done using VaR. Arguments p, method and invert are passed to this function. Refer to their help file for details and other options.

Value

A list containing

VaR.fm	length-N vector of factor model VaRs of N-asset returns.
n.exceed	length-N vector of number of observations beyond VaR for each asset.
idx.exceed	list of numeric vector of index values of exceedances.
mVaR	N x (K+1) matrix of marginal contributions to VaR.
cVaR	N x (K+1) matrix of component contributions to VaR.
pcVaR	N x (K+1) matrix of percentage component contributions to VaR.

Where, K is the number of factors and N is the number of assets.

Author(s)

Eric Zivot, Sangeetha Srinivasan and Yi-An Chen

References

Hallerback (2003). Decomposing Portfolio Value-at-Risk: A General Analysis. The Journal of Risk, 5(2), 1-18.

Meucci, A. (2007). Risk contributions from generic user-defined factors. RISK-LONDON-RISK MAGAZINE LIMITED-, 20(6), 84.

Yamai, Y., & Yoshiba, T. (2002). Comparative analyses of expected shortfall and value-at-risk: their estimation error, decomposition, and optimization. Monetary and economic studies, 20(1), 87-121.

See Also

fitTsfm, fitSfm, fitFfm for the different factor model fitting functions.

VaR for VaR computation. fmSdDecomp for factor model SD decomposition. fmEsDecomp for factor model ES decomposition.

Examples

# Time Series Factor Model
data(managers)
fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
                     factor.names=colnames(managers[,(7:8)]), data=managers)

VaR.decomp <- fmVaRDecomp(fit.macro)
# get the component contributions
VaR.decomp$cVaR

# Statistical Factor Model
data(StockReturns)
sfm.pca.fit <- fitSfm(r.M, k=2)
VaR.decomp <- fmVaRDecomp(sfm.pca.fit)
VaR.decomp$cVaR

arorar/FactorAnalytics documentation built on May 10, 2019, 1:47 p.m.