The Laboratory for Financial and Risk Analytics is focused on developing stochastic models, statistical methods and machine learning algorithms for the quantitative analysis of financial markets. We seek to advance and contribute to state-of-the-art knowledge by developing cutting edge solutions motivated by problems from the financial industry and real world applications. The research interests of the laboratory can be subdivided into three distinct areas: high dimensional financial time series, high frequency trading models and advances in financial technology.

High dimensional financial time series

With the globalization of financial markets and the availability of an increasing number of financial assets, the dimensionality of financial time series becomes dangerously close to their length. Within this research topic we focus on statistical and computational methods for estimation of high dimensional dependence structures in interconnected financial systems.

High frequency trading models

Advances in technology allow for complex algorithms and models to be used for intraday trading activities at high frequencies. We seek to model the specific characteristics of price formation and investor behavior, and develop cutting-edge models in order to improve the understanding of intraday trading activity and market microstructure.

Advances in financial technology

The distributed ledger technology and the associated blockchain-based financial assets present the forefront of technological advances in finance. However, their adoption is hindered by the poor awareness of the associated risks. By developing applicable models we aim to improve the understanding of investment risks for these technological advancements.


Conferences & Talks

  • S. Begušić, V. Keranović, Z. Kostanjčar, and B. Jeren “On the predictive power of statistical factor modelsInternational Conference on Quantitative Finance – Forecasting Financial Markets, Venice, Italy, Jun. 2019

  • L. Mrčela, A. Merćep, K. Ljubičić, M. Birov, and Z. Kostanjčar, “Deep self-normalizing network for credit risk assessmentRobust Techniques in Quantitative Finance, University of Oxford, UK, Sep. 2018
  • M. Puljiz, S. Begušić, and Z. Kostanjčar, “Market Microstructure and Order Book Dynamics in Cryptocurrency Exchanges" CryptoValley Conference on Blockchain Technology, Zug, Switzerland, Jun. 2018
  • L. Mrčela, A. Merćep, S. Begušić, and Z. Kostanjčar, “Portfolio Optimization Using Preference Relation Based on Statistical Arbitrage" International Conference on Smart Systems and Technologies, Osijek, Croatia, Oct. 2017, doi: 10.1109/SST.2017.8188688
  • S. Begušić, Z. Kostanjčar, and B. Podobnik, “Predictive Power of Complexity Theory in Financial Markets" 1st International Scientific Conference “Agenda 2030: Economics in a changing world", Umag, Croatia, Aug. 2017
  • S. Begušić, Z. Kostanjčar, “Information Flow Networks of Financial Time Series" 8th Conference on Complex Networks, CompleNet 2017, Dubrovnik, Croatia, Mar. 2017
  • S. Begušić, Z. Kostanjčar, H. Eugene Stanley, and B. Podobnik, “A Network-Based Approach to Modeling Market Bubbles and Crashes" 7th International Conference on Information Technologies and Information Society, Novo mesto, Slovenia, Nov. 2015
  • S. Begušić, Z. Kostanjčar, H. Eugene Stanley, and B. Podobnik, “Does bargaining dynamics inherently cause market bubbles and crashes?7th General Advanced Mathematical Methods in Finance and Swissquote Conference, Lausanne, Switzerland, Sep. 2015
  • Z. Kostanjčar, Ž. Juretić, and B. Jeren, “Modelling the Relationship Between Developed Equity Markets and Emerging Equity Markets" IEEE Computational Intelligence for Financial Engineering and Economics, London, UK, Mar. 2014.
  • Z. Kostanjčar, and B. Jeren, “Relationship between bid-ask spreads and fluctuations in market prices" Econophysics Colloquium 2010, Taipei, Taiwan, Nov. 2010