Research-Topics

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 markets at this granularity.

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 the conditional expected return; and volatility models (GARCH and stochastic volatility) that capture time-varying conditional variance, including the well-documented phenomenon of volatility clustering.

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.

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.