Chanaka Edirisinghe

Acting Dean, Lally School of Management; Kay and Jackson Tai '72 Chaired Professor, Quantitative Finance | Rensselaer Polytechnic Institute

Troy, NY, UNITED STATES

Specializes in financial portfolio optimization theory, models, and applications

Areas of Expertise (5)

Large-Scale Optimization

Risk Analytics

Financial Engineering

Financial Derivatives

Portfolio optimization

Biography

Professor Chanaka Edirisinghe’s research interests include financial portfolio theory, models, and applications. Specific areas of his investigations include portfolio optimization, arbitrage-free pricing of options and derivatives, risk management, contracting and coordination, and large-scale quadratic optimization algorithms. Emerald Management Reviews awarded the Citation of Excellence for his research in 2009, recognizing his work on firm-fundamental strength analysis using Data Envelopment Analysis as one of the top 50 articles in management research. His research has been published in leading operations research and finance journals, including Management Science, Operations Research, Mathematical Programming, Mathematics of Operations Research, Journal of Financial and Quantitative Analysis, Journal of Banking and Finance, Quantitative Finance, and European Journal of Operational Research.

Education (3)

University of British Columbia: Ph.D., Management Science

Asian Institute of Technology: M.E., Industrial Engineering and Management

University of Peradeniya, Sri Lanka: B.Sc., Mechanical Engineering

Links (1)

Faculty Profile Page

Articles (7)

Indefinite multi-constrained separable quadratic optimization: Large-scale efficient solution

European Journal of Operational Research

Chanaka Edirisinghe, Jaehwan Jeong

2019 Multi-constrained indefinite separable quadratic optimization occurs in many practical applications. However, it is an NP-hard problem and its solution even for problems of moderate size is computationally tedious. Extending our previous work on singly constrained problems, we develop the necessary theory and computational procedures for problems with multiple linear constraints, by employing iterative constraint aggregation, known as surrogation.

Tight bounds on indefinite separable singly-constrained quadratic programs in linear-time

Mathematical Programming

Chanaka Edirisinghe, Jaehwan Jeong

2017 Non-convex knapsack separable quadratic optimization with compact box constraints is an NP-hard problem. We present tight lower and upper bounding procedures that are computationally-efficient as the problem size grows. The lower bound is based on Lagrangian relaxation, and it is computed in linear-time. When the bound is not an exact global solution, a worst-case bound-quality measure is developed. Moreover, the lower bounding (LB) solution is improved to construct a feasible solution, leading to an upper bound (UB) on the given problem.

Lower bounding inventory allocations for risk pooling in two-echelon supply chains

International Journal of Production Economics

Chanaka Edirisinghe, Derek Atkins

2017 This paper addresses the effect of risk-pooling when a single supplier, or a depot, distributes a single commodity to multiple retailers in a two-echelon framework. The demands at the retailers are random and may possibly be correlated. Rather than shipping the full order of inventory from the warehouse at once to the retailer, shipments in two periods within an inventory cycle in a two-echelon format (under observation of real-time demand) yields improved system performance, both in terms of cost and inventory distribution in the system.

An Efficient Global Algorithm for a Class of Indefinite Separable Quadratic Programs

Mathematical Programming

Chanaka Edirisinghe, Jaehwan Jeong

2016 We present a global algorithm for indefinite knapsack separable quadratic programs with bound constraints. The upper bounds on variables with nonconvex terms are assumed to be infinite in the algorithmic development. By characterizing optimal solutions of the problem, we enumerate a subset of KKT points to determine a global optimum. The enumeration is made efficient by developing a theory for shrinking and partitioning the search domain of KKT multipliers. The global algorithmic procedure is developed based on interval and point testing techniques that have roots in solving strictly convex problems.

Risk assessment based on the analysis of the impact of contagion flow

Journal of Banking & Finance

Chanaka Edirisinghe, Aparna Gupta, Wendy Roth

2015 This paper presents a new framework to model and calibrate the process of firm value evolution when an unanticipated exogenous event impacting one firm can contagiously affect other firms. The nature of propagation of such contagion is determined by the underlying connections between firms, which can adversely affect the tail risks of firm value, hence the securities issued by the firm. This paper combines the insights gained from the existing firm-value models and historical events into a structural model for flow of contagion among firms using a network-based approach...

Entropy-based optimization of nonlinear separable discrete decision models

Management Science

Yuji Nakagawa, Ross JW James, César Rego, Chanaka Edirisinghe

2013 This paper develops a new way to help solve difficult linear and nonlinear discrete-optimization decision models more efficiently by introducing a problem-difficulty metric that uses the concept of entropy from information theory. Our entropy metric is employed to devise rules for problem partitioning within an implicit enumeration method, where branching is accomplished based on the subproblem complexity. The only requirement for applying our metric is the availability of (upper) bounds on branching subproblems, which are often computed within most implicit enumeration methods such as branch-and-bound (or cutting-plane-based) methods...

Index-tracking optimal portfolio selection

Quantitative Finance Letters

NCP Edirisinghe

2013 This paper considers a portfolio selection problem with multiple risky assets where the portfolio is managed to track a benchmark market barometer, such as the S&P 500 index. A tracking optimization model is formulated and the tracking-efficient (TE) portfolios are shown to inherit interesting properties compared with Markowitz mean-variance (MV) optimal portfolios. In comparison to an MV-portfolio, both the beta and the variance of a TE-portfolio are higher by fixed amounts that are independent of the expected portfolio return. These differences increase with index variance, are convex quadratic in the asset betas, and depend on the asset means and covariance matrix...