NOT FOR QUOTATION
WITHOUT PERMISSION
OF THE AUTHOR
A BANK ASSET AND LIABILITY MANAGEMENT MODEL
M.I. Kusy*
W.T.
Ziemba**
December 1983
CP-83-59
*
Concordia University, Montreal,
Quebec, Canada.
**International Institute for Applied Systems
Analysis, Laxenburg, Austria.
and
University of British Columbia, Vanouver,
B.C., Canada
CoZZdborative Papers
report work which has not been
performed solely at the International Institute for
Applied Systems Analysis and which has received only
limited review. Views or opinions expressed herein
do not necessarily represent those of the Institute,
its National Member Organizations, or other organi-
zations supporting the work.
INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS
A-2361 Laxenburg, Austria
PREFACE
The area of asset
managemeht is rich in potential applications
of stochastic programming techniques. This article develops
a multiperiod stochastic
programming model for bank asset and
liability management, it shows that the results are far superior
to those of a deterministic version of such a model. The algorithm
used to solve the stochastic problem is part of the soft ware
packages for stochastic optimization problems under development
by the Adaptation and Optimization Task at IIASA.
Roger Wets
ABSTRACT
The uncertainty of a bank's cash flows, cost of funds and return on invest-
ments due
to
inherent factors and variable economic conditions has emphasized
the need for greater efficiency in the management of asset and liabilities.
A
primary goal
is
to determine
an
optimal tradeoff between risk, return, and
liquidity. In this paper
we
develop a multiperiod stochastic linear programming
model
(Am)
that includes the essential institutional, legal, financial, and
bank related
policy considerations, along with their uncertain aspects, yet
is
computationally tractable for realistic sized problems.
A
version of the model
was developed for the Vancouver City Savings Credit Union for a five year plan-
ning period. The results indicate that
ALN
is
theoretically and operationally
superior
to
a corresponding deterministic linear prgramming model and the effort
required for the implementation of
ALN
and the computational costs are compar-
able
to
those of the deterministic model. Wreover, the qualitative and quant-
itative characteristics of the solutions are sensitive
to
the stochastic
elements of the model such
as
the asymmetry of the cash flow distributions.
ALN
was also compared with the stochastic decision tree (SDT) model developed by
Bradley and Crane.
ALN
is
more computationally tractable on realistic sized
problems than
SDT and simulation results indicate that
AM
generates superior
policies.
Without implicating them
we
would like
to
thank
J.
Birge,
W.
~Shler,
G.
Gassmann,
J.G.
Kallberg, C.E. Sarndal, and
R.W.
White for helpful discussions
and
Messrs.
Bently and Hook of
the
Vancouver City Savings Credit Union for
providing data used in this study. This research was supported by the
International Institute for Applied Sys
terns
Analysis, Austria, the Canada
Council, and the Natural Sciences and Engineering Research Council of Canada.
