The laboratory is designed to support research and promote partnerships between academia and the financial industry. Our team members manage and collaborate on research and development projects funded by various agencies and our partners from the financial industry.

Deep Reinforcement Learning Algorithms for Risk Management

Funded by the Croatian Science Foundation

The goal of this project is to develop a novel class of risk-sensitive reinforcement learning algorithms in dynamic environments with applications in financial risk management. The objectives also include the implementation of state space representation models that extract information from time series data by exploiting latent factors and design of portfolio optimization algorithms based on proposed methods.

Deep Learning for Credit Risk Assessment

Funded by PBZ and the Intesa Sanpaolo Innovation Centre

Within the scope of the project deep learning models will be studied and novel deep learning methods developed specifically for the application of credit risk assessment. The goal is to build competences in applications of deep learning models for credit risk assessment for retail and SME customers, and transfer the structured knowledge to the banking industry.


Algorithms for Systemic Risk Measurement

Funded by the Croatian Science Foundation

The goal of the project is to develop statistical methods for modelling and measuring systemic risk in complex systems such as financial markets. By researching the methods of mathematical and computational modeling, valuable insights can be obtained. For instance, the emergence of power-law and two-phase behavior in the financial market fluctuations, or the basic mechanisms that underlie systemic risk and the stability of the complex financial systems.

Intrinsic Value Estimator for Emerging Capital Market Indices

Funded by the Croatian Agency for SMEs, innovations and Investments (HAMAG-BICRO)

The main goal of this project is to test the innovative concept of an estimator of the intrinsic values of emerging capital markets. By applying advanced valuation methods based on fundamental firm-level and external macro data, we are able to obtain accurate estimates for the otherwise elusive intrinsic values of emerging market indices.

Financial Market Risk Modelling

Funded by OTP Invest

Within the scope of the project, quantitative methods and multivariate statistical models for financial market risk are studied. The aim of the project is to develop efficient methods for modelling and measurement of risks associated with the cross-sectional spillover and dependencies in financial markets.