CoSkewnessMatrix(R, ...)
CoKurtosisMatrix(R, ...)
CoVariance(Ra, Rb)
CoSkewness(Ra, Rb)
CoKurtosis(Ra, Rb)
M3.MM(R, ...)
M4.MM(R, ...)
The co-moments are useful for measuring the marginal contribution of each asset to the portfolio's resulting risk. As such, comoments of asset return distribution should be useful as inputs for portfolio optimization in addition to the covariance matrix. Martellini and Ziemann (2007) point out that the problem of portfolio selection becomes one of selecting tangency points in four dimensions, incorporating expected return, second, third and fourth centered moments of asset returns.
Even outside of the optimization problem, measuring the co-moments should be a useful tool for evaluating whether or not an asset is likely to provide diversification potential to a portfolio, not only in terms of normal risk (i.e. volatility) but also the risk of assymetry (skewness) and extreme events (kurtosis).
Martellini, Lionel, and Volker Ziemann. 2007. Improved Forecasts of Higher-Order Comoments and Implications for Portfolio Selection. EDHEC Risk and Asset Management Research Centre working paper.
Ranaldo, Angelo, and Laurent Favre Sr. 2005. How to Price Hedge Funds: From Two- to Four-Moment CAPM. SSRN eLibrary.
Scott, Robert C., and Philip A. Horvath. 1980. On the Direction of Preference for Moments of Higher Order than the Variance. Journal of Finance 35(4):915-919.
BetaCoSkewness
BetaCoKurtosis
BetaCoMoments
data(managers)
CoVariance(managers[, "HAM2", drop=FALSE], managers[, "SP500 TR", drop=FALSE])
CoSkewness(managers[, "HAM2", drop=FALSE], managers[, "SP500 TR", drop=FALSE])
CoKurtosis(managers[, "HAM2", drop=FALSE], managers[, "SP500 TR", drop=FALSE])
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