About

Professor Matthew Lorig obtained a PhD in physics from the University of California at Santa Barbara in 2011 and a B.S. in Physics from the University of Minnesota in 2004. From 2011 to 2014, he worked as a postdoctoral researcher in the Department of Operations Research and Financial Engineering at Princeton University. In 2014, he joined the Department of Applied Mathematics at the University of Washington. His research focuses on solving problems that arise in the financial industry, including derivative pricing, hedging, implied volatility, and portfolio management. He combines tools from stochastic analysis, spectral theory, and perturbation methods for PDEs in his work. Recently, Professor Lorig has been particularly interested in model-free approaches to pricing and hedging path-dependent derivative assets.

Research topics

• Computer Science
• Economics
• Econometrics
• Financial economics
• Microeconomics
• Mathematics
• Finance
• Mathematical economics

Selected publications

• Short-Rate-Dependent Volatility Models

  ArXiv.org · 2026-01-31

  articleOpen accessSenior author

  We price European options in a class of models in which the volatility of the underlying risky asset depends on the short rate of interest. Our study results in an explicit pricing formula that depends on knowledge of a characteristic function. We provide examples of models in which the characteristic function can be computed analytically and, thus, the value of European options is explicit. Numerical implementation to produce the implied volatility is also presented.

  PublisherOA PDF

• Optimal Liquidation of Perpetual Contracts

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Short-Rate-Dependent Volatility Models

  Open MIND · 2026-01-31

  preprintSenior author

  We price European options in a class of models in which the volatility of the underlying risky asset depends on the short rate of interest. Our study results in an explicit pricing formula that depends on knowledge of a characteristic function. We provide examples of models in which the characteristic function can be computed analytically and, thus, the value of European options is explicit. Numerical implementation to produce the implied volatility is also presented.

  DOI

• Optimal Liquidation of Perpetual Contracts

  arXiv (Cornell University) · 2026-01-15

  preprintOpen accessSenior author

  An agent holds a position in a perpetual contract with payoff function $ψ$ and attempts to liquidate the position while managing transaction costs, inventory risk, and funding rate payments. By solving the agent's stochastic control problem we obtain a closed-form expression for the optimal trading strategy when the payoff function is given by $ψ(s) = s$. When the payoff function is non-linear we provide approximations to the optimal strategy which apply when the funding rate parameter is small or when the length of the trading interval is small. We further prove that when $ψ$ is non-linear, the short time approximation can be written in terms of the closed-form trading strategy corresponding to the case of the identity payoff function.

  PublisherDOI

• Optimal Liquidation of Perpetual Contracts

  ArXiv.org · 2026-01-15

  articleOpen accessSenior author

  An agent holds a position in a perpetual contract with payoff function $ψ$ and attempts to liquidate the position while managing transaction costs, inventory risk, and funding rate payments. By solving the agent's stochastic control problem we obtain a closed-form expression for the optimal trading strategy when the payoff function is given by $ψ(s) = s$. When the payoff function is non-linear we provide approximations to the optimal strategy which apply when the funding rate parameter is small or when the length of the trading interval is small. We further prove that when $ψ$ is non-linear, the short time approximation can be written in terms of the closed-form trading strategy corresponding to the case of the identity payoff function.

  PublisherOA PDF

• A Calculus of Variations Approach to Stochastic Control

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Interest rate derivatives in a CTMC setting: Pricing, replication and Ross recovery

  International Journal of Financial Engineering · 2025-09-19

  articleSenior author

  We consider a financial market in which the short rate is modeled by a continuous time Markov chain (CTMC) with a finite state space. In this setting, we show how to price any financial derivative whose payoff is a function of the state of the underlying CTMC at the maturity date. We also show how to replicate such claims by trading only a money market account and zero-coupon bonds. Finally, using an extension of Ross’ Recovery Theorem due to Qin and Linetsky, we deduce the real-world dynamics of the CTMC.

  PublisherDOI

• Short-Rate Derivatives in a Higher-for-Longer Environment

  Asia-Pacific Financial Markets · 2025-12-15

  articleOpen access
  PublisherOA PDFDOI

• A Calculus of Variations Approach to Stochastic Control

  ArXiv.org · 2025-09-01

  preprintOpen access1st authorCorresponding

  We use classical tools from calculus of variations to formally derive necessary conditions for a Markov control to be optimal in a standard finite time horizon stochastic control problem. As an example, we solve the well-known Merton portfolio optimization problem.

  PublisherOA PDFDOI

• Optimal positioning in derivative securities in incomplete markets

  arXiv (Cornell University) · 2024-02-29

  preprintOpen access

  This paper analyzes a problem of optimal static hedging using derivatives in incomplete markets. The investor is assumed to have a risk exposure to two underlying assets. The hedging instruments are vanilla options written on a single underlying asset. The hedging problem is formulated as a utility maximization problem whereby the form of the optimal static hedge is determined. Among our results, a semi-analytical solution for the optimizer is found through variational methods for exponential, power/logarithmic, and quadratic utility. When vanilla options are available for each underlying asset, the optimal solution is related to the fixed points of a Lipschitz map. In the case of exponential utility, there is only one such fixed point, and subsequent iterations of the map converge to it.

  PublisherOA PDFDOI

Frequent coauthors

• Andrea Pascucci

  University of Bologna

  58 shared

• Stefano Pagliarani

  University of Bologna

  54 shared

• Antoine Jacquier

  The Alan Turing Institute

  16 shared

• Tim Leung

  15 shared

• Peter Carr

  New York University

  14 shared

• Jean‐Pierre Fouque

  14 shared

• Natchanon Suaysom

  University of Washington Applied Physics Laboratory

  14 shared

• Roger Lee

  14 shared

Education

• Ph.D., Physics

  University of California at Santa Barbara

  2011

• Other, Operations Research and Financial Engineering

  Princeton University

  2014

Awards & honors

• SIAG-FME Early Career Prize (2016)