Advanced moment estimates

PortfolioAnalytics supports the "sample" method as well as three more advanced methods for estimating portfolio moments.

"sample": Basic sample estimate of first four moments.
"boudt": The first four moments are estimated by fitting a statistical factor model based on the work of Boudt et al., 2014.
"black_litterman": The first two moments are estimated using the Black-Litterman framework.
"Meucci": The first two moments are estimated using the Fully Flexible Views framework.

In this exercise, you will estimate the second moment using the "boudt" method. A portfolio specification object named port_spec with a "StdDev" objective has already been created.

This exercise is part of the course

Intermediate Portfolio Analysis in R

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Exercise instructions

Print the portfolio specification object.
Fit a statistical factor model with 3 factors to the asset returns. Assign to a variable named fit
Estimate the portfolio moments using the "boudt" method with 3 factors. Assign to a variable named moments_boudt.
Use extractCovariance() to get the estimated variance-covariance matrix from fit and check if it is equal to the estimate in moments_boudt

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Print the portfolio specification object


# Fit a statistical factor model to the asset returns
fit <- statistical.factor.model(R = ___, k = ___)

# Estimate the portfolio moments using the "boudt" method with 3 factors
moments_boudt <- set.portfolio.moments(R = ___, portfolio = ___, method = ___, k = ___)

# Check if the covariance matrix extracted from the model fit is equal to the estimate in `moments_boudt`
moments_boudt$___ == extractCovariance(___)

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