About

Tim S.T. Leung is a Boeing Endowed Professor and the Director of the Computational Finance and Risk Management Program at the University of Washington's Department of Applied Mathematics. His research focuses on computational finance, risk management, and related mathematical modeling. As a faculty member, he contributes to the development of advanced quantitative methods and their applications in financial mathematics and risk analysis.

Research topics

• Artificial Intelligence
• Computer Science
• Data science
• Computer Security
• Machine Learning
• Algorithm
• World Wide Web
• Knowledge management
• Finance
• Business

Selected publications

• Short-Rate-Dependent Volatility Models

  Open MIND · 2026-01-31

  preprint1st authorCorresponding

  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

• Short-Rate-Dependent Volatility Models

  ArXiv.org · 2026-01-31

  articleOpen access1st authorCorresponding

  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

• Pricing energy spread options with variance gamma-driven Ornstein-Uhlenbeck dynamics

  ArXiv.org · 2025-07-15

  preprintOpen access1st authorCorresponding

  We consider the pricing of energy spread options for spot prices following an exponential Ornstein-Uhlenbeck process driven by a sum of independent multivariate variance gamma processes, which gives rise to mean-reverting, infinite activity price dynamics. Within this class of driving processes, the Esscher transform is used to obtain an equivalent martingale measure with a focus on the weak variance alpha-gamma process. By deriving an analytical formula for the cumulant generating function of the innovation term, we obtain a pricing formula for forwards and apply the FFT method of Hurd and Zhou to price spread options. Lastly, we demonstrate how the model should be both estimated on energy prices under the real world measure and calibrated on forward or call prices, and provide numerical results for the pricing of spread options.

  PublisherOA PDFDOI

• A flexible regime-switching framework for foreign exchange dynamics

  Studies in Economics and Finance · 2025-03-20

  articleSenior author

  Purpose This paper proposes a flexible regime-switching framework to model the dynamics of foreign exchange (FX) rates. Design/methodology/approach In their approach, the FX rate dynamics may switch between two different models. Specifically, this paper consider the regime-switching Ornstein-Uhlenbeck (RSOU), regime-switching Brownian Motion (RSBM) and regime-switching Brownian Motion – Ornstein-Uhlenbeck (RSBMOU) models, where the model parameters are modulated by a hidden Markov chain over time. This paper apply an Expectation–Maximization (EM) algorithm along with filtering and smoothing techniques. Findings Using over two decades of historical FX data, this paper show the effectiveness of our approach and illustrate how the regime varies over time through economic cycles and major financial crises. Originality/value This paper consider a RSOU model and a RSBMOU model to study the behavior of FX rates. To calibrate model parameters, this paper apply the EM algorithm along with time series filtering and smoothing techniques. To their knowledge, parameter estimation via the EM algorithm has not been applied to both models. This paper proceed to compare the outcomes obtained from both models and this paper show that both models are good fits for FX rate data.

  PublisherDOI

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

  International Journal of Financial Engineering · 2025-09-19

  article1st authorCorresponding

  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

• Threshold overnight comovement analysis of intraday and overnight returns

  Investment Analysts Journal · 2025-09-07

  articleSenior author
  PublisherDOI

• A Coupled Optimal Stopping Approach to Pairs Trading over a Finite Horizon

  Computational Economics · 2025-11-04

  articleSenior author
  PublisherDOI

• Stochastic Control Approach to Futures Trading

  WORLD SCIENTIFIC eBooks · 2024-10-17

  book1st authorCorresponding

  Futures play an integral role in the financial markets. Tens of millions of contracts are traded on futures exchanges around the globe every day. In recent years, futures have been incorporated into a wide array of financial securities and have become the driving force behind their price dynamics. Managed futures portfolios and commodity trading advisors (CTAs), with hundreds of billions under management, are major parts of the hedge fund industry.

  PublisherDOI

• Optimal positioning in derivative securities in incomplete markets

  Frontiers of Mathematical Finance · 2024-01-01 · 2 citations

  articleOpen access1st authorCorresponding

  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

• Multiscale Financial Data Analytics and Machine Learning

  WORLD SCIENTIFIC eBooks · 2024-06-29

  book1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Brian Ward

  19 shared

• Marco Santoli

  City University of Seattle

  19 shared

• Bahman Angoshtari

  16 shared

• Matthew Lorig

  15 shared

• Kazutoshi Yamazaki

  15 shared

• Yang Zhou

  14 shared

• Theodore Zhao

  University of Washington Applied Physics Laboratory

  14 shared

• Hongzhong Zhang

  China Coal Technology and Engineering Group Corp (China)

  13 shared

Education

• Ph.D., Operations Research & Financial Engineering

  Princeton University

• M.S., Industrial Engineering & Operations Research

  Columbia University

• B.S., Applied Mathematics & Statistics

  Johns Hopkins University

Awards & honors

• Charlotte Procter Honorific Fellowship
• Emerald Literati Network Award (2016)