10 research outputs found
Time consistent expected mean-variance in multistage stochastic quadratic optimization: a model and a matheuristic
In this paper, we present a multistage time consistent Expected Conditional Risk Measure for minimizing a linear combination of the expected mean and the expected variance, so-called Expected Mean-Variance. The model is formulated as a multistage stochastic mixed-integer quadratic programming problem combining risk-sensitive cost and scenario analysis approaches. The proposed problem is solved by a matheuristic based on the Branch-and-Fix Coordination method. The multistage scenario cluster primal decomposition framework is extended to deal with large-scale quadratic optimization by means of stage-wise reformulation techniques. A specific case study in risk-sensitive production planning is used to illustrate that a remarkable decrease in the expected variance (risk cost) is obtained. A competitive behavior on the part of our methodology in terms of solution quality and computation time is shown when comparing with plain use of CPLEX in 150 benchmark instances, ranging up to 711,845 constraints and 193,000 binary variables.project MTM2015-65317-P (MINECO/FEDER/EU);
BERC 2014-2017;
IT-928-16; and by the University of the Basque Country UPV/EHU;
BCAM Severo Ochoa excellence accreditation Grant SEV-2013-0323;
BERC 2014-201
Distributionally robust optimization through the lens of submodularity
Distributionally robust optimization is used to solve decision making
problems under adversarial uncertainty where the distribution of the
uncertainty is itself ambiguous. In this paper, we identify a class of these
instances that is solvable in polynomial time by viewing it through the lens of
submodularity. We show that the sharpest upper bound on the expectation of the
maximum of affine functions of a random vector is computable in polynomial time
if each random variable is discrete with finite support and upper bounds
(respectively lower bounds) on the expected values of a finite set of
submodular (respectively supermodular) functions of the random vector are
specified. This adds to known polynomial time solvable instances of the
multimarginal optimal transport problem and the generalized moment problem by
bridging ideas from convexity in continuous optimization to submodularity in
discrete optimization. In turn, we show that a class of distributionally robust
optimization problems with discrete random variables is solvable in polynomial
time using the ellipsoid method. When the submodular (respectively
supermodular) functions are structured, the sharp bound is computable by
solving a compact linear program. We illustrate this in two cases. The first is
a multimarginal optimal transport problem with given univariate marginal
distributions and bivariate marginals satisfying specific positive dependence
orders along with an extension to incorporate higher order marginal
information. The second is a discrete moment problem where a set of marginal
moments of the random variables are given along with lower bounds on the cross
moments of pairs of random variables. Numerical experiments show that the
bounds improve by 2 to 8 percent over bounds that use only univariate
information in the first case, and by 8 to 15 percent over bounds that use the
first moment in the second case.Comment: 36 Pages, 6 Figure
Applying stochastic programming models in financial risk management
This research studies two modelling techniques that help seek optimal strategies in
financial risk management. Both are based on the stochastic programming methodology.
The first technique is concerned with market risk management in portfolio
selection problems; the second technique contributes to operational risk management
by optimally allocating workforce from a managerial perspective.
The first model involves multiperiod decisions (portfolio rebalancing) for an asset
and liability management problem and deals with the usual uncertainty of investment
returns and future liabilities. Therefore it is well-suited to a stochastic programming approach.
A stochastic dominance concept is applied to control the risk of underfunding.
A small numerical example and a backtest are provided to demonstrate advantages of
this new model which includes stochastic dominance constraints over the basic model.
Adding stochastic dominance constraints comes with a price: it complicates the
structure of the underlying stochastic program. Indeed, new constraints create a link
between variables associated with different scenarios of the same time stage. This
destroys the usual tree-structure of the constraint matrix in the stochastic program
and prevents the application of standard stochastic programming approaches such as
(nested) Benders decomposition and progressive hedging. A structure-exploiting interior
point method is applied to this problem. Computational results on medium scale
problems with sizes reaching about one million variables demonstrate the efficiency of
the specialised solution technique.
The second model deals with operational risk from human origin. Unlike market
risk that can be handled in a financial manner (e.g. insurances, savings, derivatives),
the treatment of operational risks calls for a “managerial approach”. Consequently,
we propose a new way of dealing with operational risk, which relies on the well known
Aggregate Planning Model. To illustrate this idea, we have adapted this model to the
case of a back office of a bank specialising in the trading of derivative products. Our
contribution corresponds to several improvements applied to stochastic programming
modelling. First, the basic model is transformed into a multistage stochastic program
in order to take into account the randomness associated with the volume of transaction
demand and with the capacity of work provided by qualified and non-qualified
employees over the planning horizon. Second, as advocated by Basel II, we calculate
the probability distribution based on a Bayesian Network to circumvent the difficulty
of obtaining data which characterises uncertainty in operations. Third, we go a step
further by relaxing the traditional assumption in stochastic programming that imposes
a strict independence between the decision variables and the random elements. Comparative
results show that in general these improved stochastic programming models
tend to allocate more human expertise in order to hedge operational risks. The dual
solutions of the stochastic programs are exploited to detect periods and nodes that are
at risk in terms of expertise availability