Newton's method for linear inequality systems

Cataloged from PDF version of article.We describe a modified Newton type algorithm for the solution of linear inequality systems in the sense of minimizing the l(2) norm of infeasibilities. Finite termination is proved, and numerical results are given. (C) 1998 Elsevier Science B.V

Duality in robust linear regression using Huber's M-estimator

Cataloged from PDF version of article.The robust linear regression problem using Huber's piecewise-quadratic M-estimator function is considered. Without exception, computational algorithms for this problem have been primal in nature. In this note, a dual formulation of this problem is derived using Lagrangean duality. It is shown that the dual problem is a strictly convex separable quadratic minimization problem with linear equality and box constraints. Furthermore, the primal solution (Huber's M-estimate) is obtained as the optimal values of the Lagrange multipliers associated with the dual problem. As a result, Huber's M-estimate can be computed using off-the-shelf optimization software

Lower hedging of American contingent claims with minimal surplus risk in finite-state financial markets by mixed-integer linear programming

Cataloged from PDF version of article.The lower hedging problem with a minimal expected surplus risk criterion in incomplete markets is studied for American claims in finite state financial markets. It is shown that the lower hedging problem with linear expected surplus criterion for American contingent claims in finite state markets gives rise to a non-convex bilinear programming formulation which admits an exact linearization. The resulting mixed-integer linear program can be readily processed by available software. (c) 2011 Elsevier B.V. All rights reserved

Buyer's quantile hedge portfolios in discrete-time trading

Cataloged from PDF version of article.The problem of quantile hedging for American claims is studied from the perspective of the buyer of a contingent claim by minimizing the ‘expected failure ratio’. After a general study of the problem in infinite-state spaces, we pass to finite dimensions and examine the properties of the resulting finite-dimensional optimization problems. In finite-state probability spaces we obtain a bilinear programming formulation that admits an exact linearization using binary exercise variables. Numerical results with S&P 500 index options demonstrate the computational viability of the formulations

A global error bound for quadratic perturbation of linear programs

Cataloged from PDF version of article.We prove a global error bound result on the quadratic perturbation of linear programs. The error bound is stated in terms of function values

Sharpe-ratio pricing and hedging of contingent claims in incomplete markets by convex programming

Cataloged from PDF version of article.We analyze the problem of pricing and hedging contingent claims in a financial market described by a multi-period, discrete-time, finite-state scenario tree using an arbitrage-adjusted Sharpe-ratio criterion. We show that the writer’s and buyer’s pricing problems are formulated as conic convex optimization problems which allow to pass to dual problems over martingale measures and yield tighter pricing intervals compared to the interval induced by the usual no-arbitrage price bounds. An extension allowing proportional transaction costs is also given. Numerical experiments using S&P 500 options are given to demonstrate the practical applicability of the pricing scheme. c 2008 Elsevier Ltd. All rights reserved

Equilibrium in an ambiguity-Averse mean-variance investors market

Cataloged from PDF version of article.In a financial market composed of n risky assets and a riskless asset, where short sales are allowed and mean–variance investors can be ambiguity averse, i.e., diffident about mean return estimates where confidence is represented using ellipsoidal uncertainty sets, we derive a closed form portfolio rule based on a worst case max–min criterion. Then, in a market where all investors are ambiguity-averse mean–variance investors with access to given mean return and variance–covariance estimates, we investigate conditions regarding the existence of an equilibrium price system and give an explicit formula for the equilibrium prices. In addition to the usual equilibrium properties that continue to hold in our case, we show that the diffidence of investors in a homogeneously diffident (with bounded diffidence) mean–variance investors’ market has a deflationary effect on equilibrium prices with respect to a pure mean–variance investors’ market in equilibrium. Deflationary pressure on prices may also occur if one of the investors (in an ambiguity-neutral market) with no initial short position decides to adopt an ambiguity-averse attitude. We also establish a CAPM-like property that reduces to the classical CAPM in case all investors are ambiguity-neutral

Static and dynamic VaR constrained portfolios with application to delegated portfolio management

Cataloged from PDF version of article.We give a closed-form solution to the single-period portfolio selection problem with a Value-at-Risk (VaR) constraint in the presence of a set of risky assets with multivariate normally distributed returns and the risk-less account, without short sales restrictions. The result allows to obtain a very simple, myopic dynamic portfolio policy in the multiple period version of the problem. We also consider mean-variance portfolios under a probabilistic chance (VaR) constraint and give an explicit solution. We use this solution to calculate explicitly the bonus of a portfolio manager to include a VaR constraint in his/her portfolio optimization, which we refer to as the price of a VaR constraint. © 2013 © 2013 Taylor & Francis

Gain-loss based convex risk limits in discrete-time trading

Cataloged from PDF version of article.We present an approach for pricing and hedging in incomplete markets, which encompasses other recently introduced approaches for the same purpose. In a discrete time, finite space probability framework conducive to numerical computation we introduce a gain–loss ratio based restriction controlled by a loss aversion parameter, and characterize portfolio values which can be traded in discrete time to acceptability. The new risk measure specializes to a well-known risk measure (the Carr–Geman– Madan risk measure) for a specific choice of the risk aversion parameter, and to a robust version of the gain–loss measure (the Bernardo–Ledoit proposal) for a specific choice of thresholds. The result implies potentially tighter price bounds for contingent claims than the no-arbitrage price bounds. We illustrate the price bounds through numerical examples from option pricing

A simple duality proof in convex quadratic programming with a quadratic constraint, and some applications

Cataloged from PDF version of article.In this paper a simple derivation of duality is presented for convex quadratic programs with a convex quadratic constraint. This problem arises in a number of applications including trust region subproblems of nonlinear programming, regularized solution of ill-posed least squares problems, and ridge regression problems in statistical analysis. In general, the dual problem is a concave maximization problem with a linear equality constraint. We apply the duality result to: (1) the trust region subproblem, (2) the smoothing of empirical functions, and (3) to piecewise quadratic trust region subproblems arising in nonlinear robust Huber M-estimation problems in statistics. The results are obtained from a straightforward application of Lagrange duality. Ó 2000 Elsevier Science B.V. All rights reserved