Portfolio Allocation and Risk Management. Models and Methods for scenario and moment based optimization of portfolios.
Whenever using this package, please cite as
@Manual{Ghalanos_2014,
author = {Alexios Ghalanos and Bernhard Pfaff},
title = {{parma}: Portfolio Allocation and Risk Management Applications.},
year = {2014},
note = {R package version 1.5-1.},}The releases of this package is licensed under GPL version 3.
| 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.
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. Ghalanos, 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.