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Introduction to Blockchain Applications in Finance - Training Week

I. Introduction to Blockchain applications in finance (ASE) II. Scientific Writing (BFH)

Introduction to Blockchain Applications in Finance - Training Week
Introduction to Blockchain Applications in Finance - Training Week

Time & Location

03 Nov 2025, 09:00 – 07 Nov 2025, 17:00

Bucharest University of Economic Studies, Piața Romană 6, Room Robert Schuman, 010374 Bucharest, Romania

About the event

The training week provides a comprehensive introduction to Blockchain Applications in Finance, covering both the fundamentals of blockchain technology and its practical applications in financial markets.

Participants will explore a broad set of topics, including cryptocurrencies, decentralized finance (DeFi), tokenization of assets, NFTs, and central bank digital currencies (CBDCs). Risk assessment methods such as spectral risk measures, stochastic dominance, regime-switching models, and Financial Risk Meter (FRM) will also be presented, alongside advanced lectures on AI and blockchain integration.

The program combines academic lectures, industry case studies, panel discussions, and hands-on project sessions. By the end of the week, participants will:

  • Understand blockchain fundamentals and financial applications.

  • Analyze risks and systemic implications of blockchain-based assets.

  • Explore opportunities and limitations of DeFi, NFTs, and digital assets.

  • Engage with reproducible research through Quantlet and Quantinar.

  • Begin developing doctoral projects to be presented at final sessions.

Modules and Credits

This training week is part of the MSCA Digital Finance Doctoral Program:

  • 3–6 November: Foundation modules on blockchain, digital assets, and risk modeling (4 ECTS)

  • 7 November: Research writing and publishing tutorials (1 ECTS)

  • 30 January 2026: Final online group presentations

Prior Knowledge

Participants should have:

  • Basic understanding of finance, asset pricing, and risk measures

  • Programming experience in Python or R (data analysis, visualization, econometrics)

  • Knowledge of statistics, linear algebra, and time-series analysis

Time Schedule

Speakers

  • Alla Petukhina Hochschule für Technik und Wirtschaft Berlin (HTW Berlin), Germany

  • Barbara Bedowska Sojka University of Poznan, Poland

  • Christina Sichtmann Bern University of Applied Sciences, Switzerland

  • Daniel Traian Pele IDA Institute Digital Assets, Bucharest University of Economic Studies; Institute for Economic Forecasting, Romanian Academy, Romania;

  • Frederik Bernard University of Twente, Netherlands

  • Joerg Osterrieder Coordinator MSCA Digital Finance

  • Julian Winkel Royalton Partners, Luxembourg

  • Lennart John Baals University of Twente, Netherlands

  • Liana Stanca Babes-Bolyai University, Cluj-Napoca, Romania

  • Matúš Horváth Masaryk University Brno, Czech

  • Patricia Marcella Evite University of Naples Federico II, Italy

  • Petre Caraiani Institute for Economic Forecasting, Romanian Academy; Bucharest University of Economic Studies, Romania

  • Radu Lupu IDA Institute Digital Assets, Bucharest University of Economic Studies; Institute for Economic Forecasting, Romanian Academy, Romania;

  • Rui Ren University of Augsburg, Germany

  • Weining Wang School of Economics, University of Bristol, UK; IDA Institute Digital Assets, Bucharest University of Economic Studies, Romania

  • Will Ruddick Grassroots Economics Foundation, Kenya

  • Wolfgang Karl Härdle IDA Institute Digital Assets, Bucharest University of Economic Studies, Romania

  • Xiaorui Zuo Technische Universität Berlin, Germany


Accommodation Options and Transport Information


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Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Horizon Europe: Marie Skłodowska-Curie Actions. Neither the European Union nor the granting authority can be held responsible for them. This project has received funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101119635

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