This method is from the paper "The Most General Way to Create a Valid
Correlation Matrix for Risk Management and Option Pricing Purposes" by
Rebonato and Jaekel, 1999.
This class encapsulates the top level Black-Litterman functionality in terms
of backing the risk aversion out of the market portfolio, and then on to
calculating the expected return with views.
Given the first optimization run, strips out any path information and draws
the path in series 0 with shapes = CrossCircle so that one can see the path
of the optimizer.
This class is a driver program which reads a file of industry sector returns
from the Fama/French data and generates a sequence of portfolios using 60
month rolling returns.
Dump out the constraints to System.out in the format Constraint : {E |
I} {A | I | X | M} constraint details Each constraint is either equality or
inequality Each constraint is either active, inactive, both = error, or
missing
Provides test data and harness to reproduce the results from the
Fusai and Meucci paper on assessing views for the Black-Litterman
portfolio optimization method.
Given the confidence specified as a decimal (area under the cdf) and the
total width of the region specified in units, the variance is returned
in units squared.
This class implements the method described in Idzorek (2005) of driving the
variance of the views from the confidence level by linearl solution of the
variance to match the expected change in weight of the unconstrained
portfolio.
init() -
Method in class org.akutan.blacklitterman.gui.BLApplet
To start with we will use the He-Litterman data and allow the user to
modify the sampling distribution returns (not the covariances) and also to
specify views.
This class encapsulates an example of solving for the optimal portfolio using
the shrinkage model described in "Honey I Shrunk the Covariance Matrix", by Ledoit
and Wolf, 2003.
Called to compute a probability for the updated returns given that the mehalanobis
distance will be distributed as a Chi squared with n degrees of freedom.
This class reproduces the Monte Carlo test performed in the Peterson & Grier
article on Covariance Misspecification in Asset Allocation from the
July/August 2006 issue of Financial Analysts Journal.
Removes an asset from the solution, and returns a set containing the assets
which are not currently in the solution, and are not the asset just removed
from the solution.
This class implements Return Based Style Analysis as described in the paper
Asset Allocation: Management Style and Performance Measurement
by William F.
This class implements Random Matrix Theory methods for cleaning a
correlation/covariance matrix and returning one which has had the
noise element removed.
This method updates the correlation matrix by removing all the noisy eigenvalues
from the paper "Portfolio Optimization and the Random Matrix Problem", Rosenow, et al,
Europhysics Letters, 59, (4) pp 500-506 (2002).
This method updates the correlation matrix by removing all the noisy eigenvalues
from the paper "Portfolio Optimization and the Random Matrix Problem", Rosenow, et al,
Europhysics Letters, 59, (4) pp 500-506 (2002).
Called to simulate some results, note that we do not have a covariance
matrix for this example so we are basically assuming a correlation of 0
between the various elements.
Class implements a simple Simulated Annealing algorithm from Chang et al,
"Heuristics for Cardinality Constrained Portfolio Optimization", Computers &
Operations Research, 27 (2000), 1271-1302.
Given a list of additional constraints (beyond budget and no shorts) solves
for the list of points along the efficient frontier by fixing the return and
solving for the minimal variance portfolio at each return level.
Solves the problem for the asset weights which generate the return specified
while minimizing the objective function, which here is the variance of the
solution portfolio.
These are the test cases from the paper "The Most General Way to Create a
Valid Correlation Matrix for Risk Management and Option Pricing Purposes" by
Rebonato and Jaekel, 1999.
Implements the Spectral Decomposition method from the paper "The Most General
Way to Create a Valid Correlation Matrix for Risk Management and Option
Pricing Purposes" by Rebonato and Jaekel, 1999.
This class is used to compute the Stochastic Present Value of a
spending plan, and thus the probability that the spending plan
will exhaust resources prior to the end.
SPV() -
Constructor for class org.akutan.faj.spv.SPV
This class implements the method of Stambaugh for adjusting the mean
and covariance of a shorter return series based on that series beta to
one or more longer return series.
Implements the method of Stambaugh to process a set of return series of
different lengths in order to create a robust mean and covariance matrix
for all the series.
This example attempts to replicate the example from Tutuncu and Koenig,
this is the naive example with a box constraint on the mean and covariance
and we assume the worst case for all means and covariances simultaneously.
Subclass of ActiveSetSolver that solves the mean variance portfolio
optimization problem by minimizing the utility for a given risk aversion
parameter.