Interpretation of Lagrange multipliers in nonlinear pricing problem

We present well-known interpretations of Lagrange multipliers in physical and economic applications, and introduce a new interpretation in nonlinear pricing problem. The multipliers can be interpreted as a network of directed flows between the buyer types. The structure of the digraph and the fact that the multipliers usually have distinctive values can be used in solving the optimization problem more efficiently. We also find that the multipliers satisfy a conservation law for each node in the digraph, and the non-uniqueness of the multipliers are connected to the stability of the solution structure.     

Complexity of some natural problems in automatic structures

We prove that the automatic isomorphism problem for automatic structures, the automatic automorphism problem for an automatic structure, and the automatic embedding problem for automatic structures are $ \overset{\lower0.5em\hbox{$ \overset{\lower0.5em\hbox{ Matematicheski $ \overset{\lower0.5em\hbox{$ \overset{\lower0.5em\hbox{ Zhurnal, Vol. 46, No. 1, pp. 71–78, January–February, 2005.     

On nonlinear ill-posed inverse problems with applications to pricing of defaultable bonds and option pricing

This paper considers the estimation of an unknown function h that can be characterized as a solution to a nonlinear operator equation mapping between two infinite dimensional Hilbert spaces. The nonlinear operator is unknown but can be consistently estimated, and its inverse is discontinuous, rendering the problem ill-posed. We establish the consistency for the class of estimators that are regularized using general lower semicompact penalty functions. We derive the optimal convergence rates of the estimators under the Hilbert scale norms. We apply our results to two important problems in economics and finance: (1) estimating the parameters of the pricing kernel of defaultable bonds; (2) recovering the volatility surface implied by option prices allowing for measurement error in the option prices and numerical error in the computation of the operator. The first anther was supported by US National Science Foundation (Grant No. SES-0631613) and the Cowles Foundation for Research in Economics     

Obstacle problem for nonlinear integro-differential equations arising in option pricing

Abstract  We study the obstacle problem for a class of nonlinear integro-partial differential equations of second order, possibly degenerate, which includes the equation modeling American options in a jump-diffusion market with large investor. The viscosity solutions setting reveals appropriate, because of a monotonicity property with respect to the integral term. The same property allows to approximate the problem by penalization and to obtain the existence and uniqueness of solutions via a comparison principle. We also give uniform estimates of the solutions of the penalized problems which allow to prove further regularity. Keywords: Integro-differential equations, Obstacle problem, Viscosity solutions, American options Mathematics Subject Classification (2000): 45K05, 35K85, 49L25, 91B24     

Complexity and algorithms for nonlinear optimization problems

Nonlinear optimization algorithms are rarely discussed from a complexity point of view. Even the concept of solving nonlinear problems on digital computers is not well defined. The focus here is on a complexity approach for designing and analyzing algorithms for nonlinear optimization problems providing optimal solutions with prespecified accuracy in the solution space. We delineate the complexity status of convex problems over network constraints, dual of flow constraints, dual of multi-commodity, constraints defined by a submodular rank function (a generalized allocation problem), tree networks, diagonal dominant matrices, and nonlinear knapsack problem’s constraint. All these problems, except for the latter in integers, have polynomial time algorithms which may be viewed within a unifying framework of a proximity-scaling technique or a threshold technique. The complexity of many of these algorithms is furthermore best possible in that it matches lower bounds on the complexity of the respective problems. In general nonseparable optimization problems are shown to be considerably more difficult than separable problems. We compare the complexity of continuous versus discrete nonlinear problems and list some major open problems in the area of nonlinear optimization. An earlier version of this paper appeared in 4OR, 3:3, 171–216, 2005.     

Complexity of dense-linear-system solution on a multiprocessor ring

Different algorithms, based on Gaussian elimination, for the solution of dense linear systems of equations are discussed for a multiprocessor ring. The number of processors is assumed not to exceed the problem size. A fairly general model for data transfer is proposed, and the algorithms are analyzed with respect to their requirements of arithmetic as well as communication times.     

Optimal solution of nonlinear equations

We survey recent worst-case complexity results for the solution of nonlinear equations. Notes on worst- and average-case analysis of iterative algorithms and a bibliography of the subject are also included.     

非线性Black-Scholes模型下阶梯期权定价

在非线性Black-Scholes模型下,研究了阶梯期权定价问题.首先利用多尺度方法,将阶梯期权适合的偏微分方程分解成一系列常系数抛物方程;其次通过计算这些常系数抛物型方程的解,给出了修正障碍期权的近似定价公式;最后利用Feymann-Kac公式分析了近似结论的误差估计.     

非线性Black-Scholes模型下Bala期权定价

在非线性Black-Scholes模型下,研究了Bala期权定价问题.首先利用双参数摄动方法,将Bala期权适合的偏微分方程分解成一系列常系数抛物方程.其次通过计算这些常系数抛物型方程的解,给出了Bala期权的近似定价公式.最后利用Green函数分析了近似结论的误差估计.     

Complexity of solution of linear systems in rings of differential operators

Suppose given a k₁×k₂ system of linear equations over the Weyl algebraA _n = F[X₁,...X₁,D₄,...,D_n] or over the algebra of differential operatorsK _n = F[X₁,...X₁,D₄,...,D_n], where the degree of each coefficient of the system is less than d. It is proved that if the system is solvable overA _n, orK _n, respectively, then it has a solution of degree at most (k, d)²⁰⁽ⁿ⁾.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 192, pp. 47–59, 1991.     

An adjustment scheme for nonlinear pricing problem with two buyers

We examine a contracting problem with asymmetric information in a monopoly pricing setting. Traditionally, the problem is modeled as a one-period Bayesian game, where the incomplete information about the buyers’ preferences is handled with some subjective probability distribution. Here we suggest an iterative online method to solve the problem. We show that, when the buyers behave myopically, the seller can learn the optimal tariff by selling the product repeatedly. In a practical modification of the method, the seller offers linear tariffs and adjusts them until optimality is reached. The adjustment can be seen as gradient adjustment, and it can be done with limited information and so that it benefits both the seller and the buyers. Our method uses special features of the problem and it is easily implementable.     

The solution of nonlinear compartmental models

In this paper, a decomposition scheme is presented as an alternate method for solving nonlinear compartmental models and identification problems associated with biomedical systems. The method is demonstrated by considering some models with Michaelis-Menten elimination kinetics that are of practical significance.     

Numerical solution of nonlinear diffraction problems

An adaptive finite element method is developed for solving Maxwell's equations in a nonlinear periodic structure. The medium or computational domain is truncated by a perfect matched layer (PML) technique. Error estimates are established. Numerical examples are provided, which illustrate the efficiency of the method.     

Convergent series solution of nonlinear equations

The author's decomposition method [1] provides a new, efficient computational procedure for solving large classes of nonlinear (and/or stochastic) equations. These include differential equations containing polynomial, exponential, and trigonometric terms, negative or irrational powers, and product nonlinearities [2]. Also included are partial differential equations [3], delay-differential equations [4], algebraic equations [5], and matrix equations [6] which describe physical systems. Essentially the method provides a systematic computational procedure for equations containing any nonlinear terms of physical significance. The procedure depends on calculation of the author's A_n, a finite set of polynomials [1,13] in terms of which the nonlinearities can be expressed. This paper shows important properties of the A_n which ensure an accurate and computable convergent solution by the author's decomposition method [1]. Since the nonlinearities and/or stochasticity which can be handled are quite general, the results are potentially extremely useful for applications and make a number of common approximations such as linearization, unnecessary.     

Optimal pricing in a duopoly: A noncooperative differential games solution

This paper deals with a differential games model of an oligopoly ofn profit-maximizing firms competing for the same stock of customers. For the sale dynamics, it is assumed that the customers of each firm are driven away gradually by increasing product prices. Since the state variable is absent from the Hamiltonian maximizing conditions as well as from the adjoint equations, open-loop Nash solutions can be obtained. By using phase diagram analysis, for two players the behavior of the optimal pricing strategies can be characterized qualitatively. The main importance of the paper lies in the solution technique, rather than in the economic significance of the proposed model. Under the proposed assumptions, the two-point boundary-value problem resulting from the maximum principle is reduced to a terminal-value problem. It turns out that, for special salvage values of the market shares and if the planning horizon is not too short, nonmonotonic Nash-optimal price trajectories occur.Thanks are due to S. Jørgensen, A. Mehlmann, and R. Willing for helpful suggestions.     

Note on an alternative solution of Kamien-Schwartz's limit pricing problem

The present authors found Kamien-Schwartz's (Ref. 1) theory of limit pricing with infinite planning horizon interesting mainly because of a certain disproportion between the rather simplelooking result, viz., constancy of the optimal price strategy, and its comparatively difficult proof, viz., use of Pontryagin's Maximum Principle (PMP) which in the infinite horizon case is encumbered by the additional difficulty of not leading to a uniquely solvable system of necessary conditions. Quite in consonance with Arrow's opinion referred to in Takayama (Ref. 2, p. 446) that sometimes the infinite horizon formulation is more convenient and revealing than the finite one, it turned out that in this special case the original optimization problem could be replaced by a much simpler one and, therefore, the use of PMP avoided.     

Complexity of projective methods for the solution of ill-posed problems

We consider the problem of finite-dimensional approximation for solutions of equations of the first kind and propose a modification of the projective scheme for solving ill-posed problems. We show that this modification allows one to obtain, for many classes of equations of the first kind, the best possible order of accuracy for the Tikhonov regularization by using an amount of information which is far less than for the standard projective technique.     

Large solution to nonlinear elliptic equation with nonlinear gradient terms

The aim of this paper is to study the qualitative behavior of large solutions to the following problem