Daniel P. Palomar

Financial time series — asset returns, volatility, intraday prices — exhibit rich temporal structure that the simple i.i.d. model fails to capture. Econometrics provides the statistical and mathematical framework for modeling returns conditioned on historical observations, with two central families of models: mean models (AR, ARMA, VAR, and state-space models via the Kalman filter) that describe …

High-frequency financial data — order books, trade events, tick-by-tick prices — poses unique modeling challenges: extreme noise, irregular sampling, heavy-tailed return distributions, and volatility clustering at multiple timescales. Deep learning, and in particular large Transformer-based architectures, has emerged as a powerful approach to capturing the complex temporal dynamics of financial m…

Financial markets generate high-dimensional, non-Gaussian, and time-varying data that challenge classical statistical models. Graph-based representations offer a principled way to capture the dependency structure among assets — stocks, cryptocurrencies, FX rates — and to reveal market sectors, risk propagation channels, and crisis dynamics that are invisible to traditional analysis. Our research …

Portfolio optimization — the systematic construction of asset allocations that balance return and risk — is a central problem in quantitative finance. Our research develops rigorous, computationally efficient methods that go well beyond the classical Markowitz mean-variance framework to address the real complexities of financial data. Core directions include risk parity portfolios , where risk co…

Introduces convex optimization theory and its application across FinTech — portfolio optimization, data cleaning, machine learning, and graph learning — with equal emphasis on mathematical foundations and Python/R implementation. [ Syllabus ] Uses the dedicated textbook Portfolio Optimization: Theory and Application (Cambridge University Press, 2025). The Hong Kong University of Science and Techn…

Explores Markowitz portfolio theory and modern extensions — robust optimization, risk parity, sparse index tracking, and alternative risk measures — with strong emphasis on Python programming. [ Syllabus ] Uses the dedicated textbook Portfolio Optimization: Theory and Application (Cambridge University Press, 2025). The Hong Kong University of Science and Technology (HKUST) Spring 2025-26 Prof. Da…

Explores Markowitz portfolio theory and modern extensions in depth — robust methods, risk parity, index tracking, and high-order portfolios — with weekly R programming sessions and a live portfolio game throughout the semester. [ Syllabus ] Uses the dedicated textbook Portfolio Optimization: Theory and Application (Cambridge University Press, 2025). The Hong Kong University of Science and Technol…

Graduate-level convex optimization following a case-study approach. Covers convex theory, duality, algorithms, and nonconvex methods, with applications spanning signal processing, finance, machine learning, and big data drawn from recent top-journal papers. [ Syllabus ] The Hong Kong University of Science and Technology (HKUST) Fall 2025-26 Prof. Daniel P. Palomar

Super proud of Sandeep Kumar for his promotion to Associate Professor at IIT Delhi!! 👏 Sandeep Kumar spent ~3 years (2017-2020) at the Hong Kong University of Science and Technology as a postdoc and I have very fond memories of his time here. We worked together on graph learning research and he collaborated with many other members of my research group (see https://www.danielppalomar.com/publicati…

As we all know, DeepSeek has recently pushed the limits in LLMs through groundbreaking innovations. I’m proud to share that my former PhD student Junxiao Song serves as a Principal Researcher at DeepSeek AI, where he: Proposed the novel reinforcement learning algorithm GRPO (Group Relative Policy Optimization), which has been applied to train nearly all models in the DeepSeek series, e.g., DeepSe…

Yifan Yu has been awarded the Best Student Paper Award (Bronze) at the Asilomar Conference on Signals, Systems, and Computers! 🎉 Our paper, titled “Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach,” introduces efficient methods for extending the Kalman filter to handle non-Gaussian observations—particularly useful in applications such as financial data…

Portfolio Optimization book available online

Syed Awais W. Shah, Daniel P. Palomar, Ian Barr, Leo L. M. Poon, Ahmed Abdul Quadeer, and Matthew R. McKay, “ Seasonal antigenic prediction of influenza A H3N2 using machine learning ,” Nature Communications , vol. 15, no. 3833, 2024.

Amirhossein Javaheri, Arash Amini, Farokh Marvasti, and Daniel P. Palomar, “ Learning Spatio-Temporal Graphical Models From Incomplete Observations ,” IEEE Trans. on Signal Processing , vol. 72, pp. 1361-1374, 2024.

Shengjie Xiu, Xiwen Wang, and Daniel P. Palomar, “ A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization ,” IEEE Trans. on Signal Processing , vol. 71, pp. 2713-2727, July 2023.

R package intradayModel published in CRAN R package intradayModel for design of High-Order Portfolios published in CRAN.

Keynote Talk entitled “Learning Financial Graphs” at the ICAIF'22 Workshop on NLP and Network Analysis in Financial Applications , ACM Conference on AI in Finance, New York, USA.