The Partial Moments package

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Description

The Partial Moments package provides methods, both analytical and sample based, for calculating the partial moments of a distribution or dataset. Additionally, it provides for the calculation of symmetric multivariate couterparts and portfolio optimizer methods based on those.

Details

Package: pmoments
Type: Package
Version: 1.0
Date: 2008-11-11
License: GPL
LazyLoad: yes
Depends: methods

For multivariate datasets, the user should start by calling the pmExpectation, followed by pcmExpectation and finally the pmSolver for portfolio optimization (subject to constraints : see constraints).
There is a relative amount of flexibility as to how the main function pmExpectation is called and with what combination of inputs. The user should go through the detailed examples. Additionally, the function psRisk implements the family of risk measures described in Pedersen and Satchell (1998), which among other interesting measures also subsumes partial moments.

Author(s)

Alexios Ghalanos

References

Stone, B. (1973), A General Class of Three-Parameter Risk Measures, Journal of Finance, 28, 675-685
Fishburn, P.C., Decision and Value Theory, 1964, Wiley, New York
Fishburn, P.C., Mean-Risk Analysis with Risk Associated with Below-Target Returns, 1977, American Economic Review, 67, 116-126
Nawrocki, David N. (1991), Optimal algorithms and lower partial moment: ex post results, Applied Economics, 23 (3), 465-70.
Farinelli, S. and Tibiletti, L. , Sharpe thinking in asset ranking with one-sided measures, European Journal of Operational Research Volume 185, Issue 3, 16 March 2008, Pages 1542-1547.
Pedersen,C.S and Satchell,S.E (1998), An Extended Family of Financial-Risk Measures, The Geneva Papers on Risk and Insurance Theory, 23, 89<96>-117

Examples

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## Not run: 
library(MASS)
spx=SP500/100
lpm = pmExpectation(data=spx, method="boot", threshold=0, moment=seq(0,5,length.out=10), 
		type="lower", standardize=TRUE)
plot(lpm)

## End(Not run)