General financial equilibrium with policy interventions: a variational inequality approach

In this paper a model of general financial equilibrium with policy interventions is introduced, which yields the optimal composition of assets and liabilities in each sector's portfolio, as well as the market prices for each instrument. The policy interventions considered are taxes and price ceilings. The variational inequality formulation of the equilibrium conditions is derived and then utilized to establish existence and uniqueness properties of the solution pattern. An algorithm is proposed for the computation of the problem. Finally, the algorithm is applied to some special utility functions as numerical examples.     

Multivalued general equilibrium problems

In this paper, we use the auxiliary principle technique to suggest some new classes of iterative algorithms for solving multivalued equilibrium problems. The convergence of the proposed methods either requires partially relaxed strongly monotonicity or pseudomonotonicity. As special cases, we obtain a number of known and new results for solving various classes of equilibrium and variational inequality problems. Since multivalued equilibrium problems include equilibrium, variational inequality and complementarity problems as specials cases, our results continue to hold for these problems.     

Computing efficient financial strategies: An extended compromise programming approach

This paper proposes a mathematical model to plan the financial strategy of a large company. The model links the philosophy of new behavioural economics with the multiple criteria decision making paradigm. Within this theoretical approach, the proposed model is supported by more realistic behavioral hypotheses. After formulating the initial multi-objective programming model, it has, due to its underlying computational difficulties, to be transformed into an easily computable extended compromise programming model. The functional and empirical potential of the model is illustrated with the help of a case study concerning a “stock market quoted” Spanish company operating in the energy sector. This paper shows how such an approach can open up new prospects for research linking economic problems with applied mathematics.     

The financial equilibrium problem with implicit budget constraints

This paper presents the time-dependent, multi-agent and multi-activity financial equilibrium problem when budget constraints are implicitly defined. Specifically, we assume that total wealth is elastic with respect to the optimal investment. Such a problem is formulated as an infinite dimensional quasi-variational inequality for which an existence result is given.     

A general descent framework for the monotone variational inequality problem

We present a framework for descent algorithms that solve the monotone variational inequality problem VIP _v which consists in finding a solutionv ^* _v satisfyings(v ^*)^T(v–v ^*)0, for allv _v. This unified framework includes, as special cases, some well known iterative methods and equivalent optimization formulations. A descent method is developed for an equivalent general optimization formulation and a proof of its convergence is given. Based on this unified logarithmic framework, we show that a variant of the descent method where each subproblem is only solved approximately is globally convergent under certain conditions.This research was supported in part by individual operating grants from NSERC.     

Variational inequalities for international general financial equilibrium modeling and computation

In this paper, a variational inequality approach for modeling competitive general international financial equilibrium is presented, within which general utility functions and taxes, transaction costs, and price policy interventions are explicitly incorporated. The paper examines taxes that depend both on the origin and on the type of investing sector, and price policy interventions that allow the monetary authorities to set upper and lower prices for all instruments and currencies. The optimal composition of assets and liabilities for each sector of each country, as well as the prices of the instruments and the exchange rates, in terms of a basic currency are obtained. We present both qualitative properties of the equilibrium pattern, and propose an algorithm for the computation of the pattern along with convergence results. Finally, we study the special case where the utility function is quadratic and we apply the proposed algorithm in order to compute the equilibrium pattern for a series of numerical examples.     

Computational aspects of minimizing conditional value-at-risk

We consider optimization problems for minimizing conditional value-at-risk (CVaR) from a computational point of view, with an emphasis on financial applications. As a general solution approach, we suggest to reformulate these CVaR optimization problems as two-stage recourse problems of stochastic programming. Specializing the L-shaped method leads to a new algorithm for minimizing conditional value-at-risk. We implemented the algorithm as the solver CVaRMin. For illustrating the performance of this algorithm, we present some comparative computational results with two kinds of test problems. Firstly, we consider portfolio optimization problems with 5 random variables. Such problems involving conditional value at risk play an important role in financial risk management. Therefore, besides testing the performance of the proposed algorithm, we also present computational results of interest in finance. Secondly, with the explicit aim of testing algorithm performance, we also present comparative computational results with randomly generated test problems involving 50 random variables. In all our tests, the experimental solver, based on the new approach, outperformed by at least one order of magnitude all general-purpose solvers, with an accuracy of solution being in the same range as that with the LP solvers. János Mayer: Financial support by the national center of competence in research "Financial Valuation and Risk Management" is gratefully acknowledged. The national centers in research are managed by the Swiss National Science Foundation on behalf of the federal authorities.     

Proximal level bundle methods for convex nondifferentiable optimization,saddle-point problems and variational inequalities

We study proximal level methods for convex optimization that use projections onto successive approximations of level sets of the objective corresponding to estimates of the optimal value. We show that they enjoy almost optimal efficiency estimates. We give extensions for solving convex constrained problems, convex-concave saddle-point problems and variational inequalities with monotone operators. We present several variants, establish their efficiency estimates, and discuss possible implementations. In particular, our methods require bounded storage in contrast to the original level methods of Lemaréchal, Nemirovskii and Nesterov.This research was supported by the Polish Academy of Sciences.Supported by a grant from the French Ministry of Research and Technology.     

Massively parallel computation of large-scale spatial price equilibrium models with discriminatory ad valorem tariffs

In this paper, we develop a perfectly competitive spatial equilibrium model in price and quantity variables in the presence of discriminatory ad valorem tariffs, a widely used trade policy instrument. We derive the equilibrium conditions and formulate them as a variational inequality problem. An algorithm is then proposed for the computation of the equilibrium pattern and convergence results established. The algorithm resolves the problem into very simple subproblems, each of which can be solved simultaneously and in closed form. Finally, the algorithm is implemented on the massively parallel Thinking Machines CM-2 and CM-5 architectures, known as the Connection Machines, and numerical results presented.     

SDP diagonalizations and perspective cuts for a class of nonseparable MIQP

We present a new approach, requiring the solution of a SemiDefinite Program, for decomposing the Hessian of a nonseparable mixed-integer quadratic problem to permit using perspective cuts to improve its continuous relaxation bound. The new method favorably compares with a previously proposed one requiring a minimum eigenvalue computation.     

The computation of a general equilibrium in a public goods economy

This paper presents an algorithm for computing approximations to a certain subset of Pareto optimal allocations in a public goods economy. Consumers are partitioned into a number of exogenous governmental jurisdictions, which provide public goods locally and raise revenue to cover their costs by means of a proportional wealth tax. The Pareto optimal allocations studied are consistent with profit maximization on the part of producers, and utility maximization over private goods bundles subject to after-tax budget constraints by consumers. The computational routine is based on the Scarf algorithm for computing fixed points.The origins of this research date back to the Dartmouth Workshop on Applications to economics of new methods of computing fixed points, held during the summer of 1972 under the direction of H. Scarf. The author wishes to thank the participants in this workshop for many stimulating discussions. Also the provision of computer time by the Computer Research Center of the National Bureau of Economic Research is gratefully acknowledged. FIXPOINT, an interactive computer system developed at the Computer Research Center, was used in performing the numerical computations presented in the paper.     

Exact methods for large-scale multi-period financial planning problems

A relevant financial planning problem is the periodical rebalance of a portfolio of assets such that the portfolio’s total value exhibits certain characteristics. This problem can be modelled using a transition graph G to represent the future state space evolution of the corresponding economy and mathematically formulated as a linear programming problem. We present two different mathematical formulations of the problem. The first considers explicitly the set of the possible scenarios (scenario-based approach), while the second considers implicitly the whole set of scenarios provided by the graph G (graph-based approach). Unfortunately, for both the formulations the size of the corresponding linear programs can be huge even for simple financial problems. However, the graph-based approach seems to be a more powerful model, since it allows to consider a huge number of scenarios in a very compact formulation. The purpose of this paper is to present both heuristic and exact methods for the solution of large-scale multi-period financial planning problems using the graph-based model. In particular, in this paper we propose lower and upper bounds and three exact methods based on column, row and column/row generation, respectively. Since the methods based on column/row generation exploits simultaneously both the primal and the dual structure of the problem we call it Criss-Cross generation method. Computational results are given to prove the effectiveness of the proposed methods.      

Accelerating the convergence of the diagonalization and projection algorithms for finite-dimensional variational inequalities

This paper presents an acceleration step for the linearly convergent diagonalization and projection algorithms for finite-dimensional variational inequalities which is reminiscent of a PARTAN step in nonlinear programming. After establishing the convergence of this technique for both algorithms, several numerical examples are presented to illustrate the sometimes dramatic savings in computation time which this simple acceleration step yields.     

Parallel interior-point solver for structured quadratic programs: Application to financial planning problems

Many practical large-scale optimization problems are not only sparse, but also display some form of block-structure such as primal or dual block angular structure. Often these structures are nested: each block of the coarse top level structure is block-structured itself. Problems with these characteristics appear frequently in stochastic programming but also in other areas such as telecommunication network modelling. We present a linear algebra library tailored for problems with such structure that is used inside an interior point solver for convex quadratic programming problems. Due to its object-oriented design it can be used to exploit virtually any nested block structure arising in practical problems, eliminating the need for highly specialised linear algebra modules needing to be written for every type of problem separately. Through a careful implementation we achieve almost automatic parallelisation of the linear algebra. The efficiency of the approach is illustrated on several problems arising in the financial planning, namely in the asset and liability management. The problems are modelled as multistage decision processes and by nature lead to nested block-structured problems. By taking the variance of the random variables into account the problems become non-separable quadratic programs. A reformulation of the problem is proposed which reduces density of matrices involved and by these means significantly simplifies its solution by an interior point method. The object-oriented parallel solver achieves high efficiency by careful exploitation of the block sparsity of these problems. As a result a problem with over 50 million decision variables is solved in just over 2 hours on a parallel computer with 16 processors. The approach is by nature scalable and the parallel implementation achieves nearly perfect speed-ups on a range of problems. Supported by the Engineering and Physical Sciences Research Council of UK, EPSRC grant GR/R99683/01     

Characterizations of linear suboptimality for mathematical programs with equilibrium constraints

The paper is devoted to the study of a new notion of linear suboptimality in constrained mathematical programming. This concept is different from conventional notions of solutions to optimization-related problems, while seems to be natural and significant from the viewpoint of modern variational analysis and applications. In contrast to standard notions, it admits complete characterizations via appropriate constructions of generalized differentiation in nonconvex settings. In this paper we mainly focus on various classes of mathematical programs with equilibrium constraints (MPECs), whose principal role has been well recognized in optimization theory and its applications. Based on robust generalized differential calculus, we derive new results giving pointwise necessary and sufficient conditions for linear suboptimality in general MPECs and its important specifications involving variational and quasivariational inequalities, implicit complementarity problems, etc. Research was partially supported by the National Science Foundation under grant DMS-0304989 and by the Australian Research Council under grant DP-0451168.     

Local analysis of Newton-type methods for variational inequalities and nonlinear programming

This paper presents some new results in the theory of Newton-type methods for variational inequalities, and their application to nonlinear programming. A condition of semistability is shown to ensure the quadratic convergence of Newton's method and the superlinear convergence of some quasi-Newton algorithms, provided the sequence defined by the algorithm exists and converges. A partial extension of these results to nonsmooth functions is given. The second part of the paper considers some particular variational inequalities with unknowns (x, ), generalizing optimality systems. Here only the question of superlinear convergence of {x ^k } is considered. Some necessary or sufficient conditions are given. Applied to some quasi-Newton algorithms they allow us to obtain the superlinear convergence of {x ^k }. Application of the previous results to nonlinear programming allows us to strengthen the known results, the main point being a characterization of the superlinear convergence of {x ^k } assuming a weak second-order condition without strict complementarity.     

Dynamic asset allocation for varied financial markets under regime switching framework

Asset allocation among diverse financial markets is essential for investors especially under situations such as the financial crisis of 2008. Portfolio optimization is the most developed method to examine the optimal decision for asset allocation. We employ the hidden Markov model to identify regimes in varied financial markets; a regime switching model gives multiple distributions and this information can convert the static mean–variance model into an optimization problem under uncertainty, which is the case for unobservable market regimes. We construct a stochastic program to optimize portfolios under the regime switching framework and use scenario generation to mathematically formulate the optimization problem. In addition, we build a simple example for a pension fund and examine the behavior of the optimal solution over time by using a rolling-horizon simulation. We conclude that the regime information helps portfolios avoid risk during left-tail events.     

Using economic and financial information for stock selection

A major inconvenience of the traditional approach in portfolio choice, based upon historical information, is its inability to anticipate sudden changes of price tendencies. Introducing information about future behavior of the assets fundamentals may help to make more appropriate choices. However, the specification and parameterization of a model linking this exogenous information to the asset prices is not straightforward. Classification trees can be used to construct partitions of assets of forecasted similar behavior. We analyze the performance of this approach and apply it to different sectors of the S&P 500.      

Convergence of stationary sequences for variational inequalities with maximal monotone operators

LetT be a maximal monotone operator defined on ^N . In this paper we consider the associated variational inequality 0 T(x ^*) and stationary sequences {x _k ^* for this operator, i.e., satisfyingT(x _k ^* 0. The aim of this paper is to give sufficient conditions ensuring that these sequences converge to the solution setT ^–1(0) especially when they are unbounded. For this we generalize and improve the directionally local boundedness theorem of Rockafellar to maximal monotone operatorsT defined on ^N .     

The perturbed proximal point algorithm and some of its applications

Following the works of R. T. Rockafellar, to search for a zero of a maximal monotone operator, and of B. Lemaire, to solve convex optimization problems, we present a perturbed version of the proximal point algorithm. We apply this new algorithm to convex optimization and to variational inclusions or, more particularly, to variational inequalities.