parmautility-methods: Utility Based Optimization
In parma: Portfolio Allocation and Risk Management Applications

Description Usage Arguments Details Value Author(s) References

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

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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)))

`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.

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.

A parmaPort object containing details of the PARMA optimized portfolio.

Alexios Ghalanos

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

parma documentation built on May 2, 2019, 5:53 p.m.