parmautility-methods: Utility Based Optimization

Description

Utility based portfolio optimization using either Taylor series expansion of utility function with moments or scenario based.

Usage

parmautility(U = c("CARA", "Power"), method = c("moment", "scenario"),
		scenario = NULL, M1 = NULL, M2 =  NULL, M3 = NULL, M4 = NULL, RA = 1, 
		budget = 1, LB = rep(0, length(M1)), UB = rep(1, length(M1)))

Value

A parmaPort object containing details of the PARMA optimized portfolio.

Arguments

U: The utility function (only CARA curretly implemented).
method: Whether to use moment or scenario based optimization (only moment currently implemented).
scenario: A n-by-m scenario matrix.
M1: A vector (m) of forecasts.
M2: An m-by-m positive definite covariance matrix.
M3: An m-by-m^2 third co-moment matrix.
M4: An m-by-m^3 fourth co-moment matrix.
RA: Risk Aversion Coefficient for CARA.
budget: The investment constraint.
LB: The lower bounds for the asset weights (positive).
UB: The upper bounds for the asset weights.

Author

Alexios Galanos

Details

The function currently only implements the CARA moment based approach, but will be expanded in the future. The moment approach can take as inputs either M1 and M2 (2-moment approximation), or M1, M2, M3 and M4 (4-moment approximation). Not many models generate M3 and M4, but the “gogarch” model with manig or magh distribution will.

References

Galanos, A. and Rossi, E. and Urga, G. 2012, Independent Factor Autoregressive Conditional Density Model submitted-TBA