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parma (version 1.7)

parma-package: The parma package

Description

Portfolio Allocation and Risk Management. Models and Methods for scenario and moment based optimization of portfolios.

Arguments

How to cite this package

Whenever using this package, please cite as

@Manual{Galanos_2014,
 author       = {Alexios Galanos and Bernhard Pfaff},
 title        = {{parma}: Portfolio Allocation and Risk Management Applications.},
 year         = {2014},
 note 	      = {R package version 1.5-1.},}

License

The releases of this package is licensed under GPL version 3.

Author

Alexios Galanos and Bernhard Pfaff

Details

Package:parma
Type:Package
Version:1.5-2
Date:2014-07-09
License:GPL
LazyLoad:yes
Depends:methods
Imports:nloptr, Rglpk, quadprog
Suggests:Rsymphony, truncnorm, timeSeries

The portfolio allocation and risk managament applications (parma) package contains a unique set of methods and models for the optimal allocation of capital in financial portfolios. It uniquely represents certain discontinuous problems using their smooth approximation counterparts and implements fractional based programming for the direct optimization of risk-to-reward ratios. In combination with the rmgarch package, it enables the confident solution to scenario based optimization problems using such risk and deviation measures as Mean Absolute Deviation (MAD), Variance (EV), Minimax, Conditional Value at Risk (CVaR), Conditional Drawdown at Risk (CDaR) and Lower Partial Moments (LPM). In addition, it implements moment based optimization for use with the quadratic EV problem, and a higher moment CARA utility expansion using the coskewness and cokurtosis matrices generated from the GO-GARCH with affine GH or NIG distributions. Benchmark relative optimization (tracking error) is also implemented as are basic mixed integer cardinality constraints. Finally, for non-convex problem formulations such as the upper to lower partial moments function, global optimization methods using a penalty based method are available. The key functions in the package are parmaspec which defines the optimization setup, and parmasolve which solves the problem given a chosen representation and solver. A portfolio frontier function is implemented in parmafrontier, utility optimization in parmautility and a custom translation of the cmaes global optimization solver of Hansen (2006) with full features is implemented in cmaes.

References

Charnes, A. and Cooper, W. 1962, Programming with linear fractional functionals, Naval Research Logistics Quarterly, 9, 181--186.
Dinkelbach, W. 1967, On nonlinear fractional programming, Management Science, 13(7), 492--498.
Fishburn, P.C. 1977, Mean-risk analysis with risk associated with below-target returns, The American Economic Review, 67(2), 116-126.
Galanos, A. 2012, Higher Moment Models for Risk and Portfolio Management, Thesis (submitted) Cass Business School.
Hansen, N. 2006, The CMA Evolution Strategy: A Comparing Review, Towards a New Evolutionary Computation (Studies in Fuzziness and Soft Computing), 192, 75--102.
Holthausen, D. 1981, A risk-return model with risk and return measured as deviations from a target return, The American Economic Review, 71, 182--188.
Konno, H. and Yamazaki, H. 1991, Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market, Management Science, 37(5), 519--531.
Markowitz, H. 1952, Portfolio selection, The Journal of Finance, 7(1), 77--91.
Rockafellar, R.T. and Uryasev, S. and Zabarankin, M., 2006, Generalized deviations in risk analysis, Finance and Stochastics, 10(1), 51--74.
Stoyanov, S.V. and Rachev, S.T. and Fabozzi, F.J. 2007, Optimal financial portfolios, Applied Mathematical Finance, 14(5), 401--436.