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Swiss National Science Foundation Research Project

Our Team

Cooperation between the American University of Sharjah, UAE and Bern Business School, Switzerland

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Joerg Osterrieder

Principal Investigator

Bern Business School, Switzerland

University of Twente, Netherlands

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Yuanyuan Zhang

Team member

University of Manchester, UK

 

 

 

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Yiting Liu

Team member

Bern Business School, Switzerland

University of Twente,

Netherlands

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Stephen Chan

Co-PI

 American University of Sharjah, UAE

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Branka Hadji Misheva

Team member

Bern Business School, Switzerland

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Lennart John Baals

Team member

Bern Business School, Switzerland

University of Twente,

Netherlands

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Jeffrey Chu

Team member

Renmin University of China, China

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Codruta Mare

Team member

Babes-Bolyai University, Romania

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Gabin Taibi

Team member

Bern Business School, Switzerland

University of Twente,

Netherlands

Description of the Research Project

Background: Blockchain networks are increasingly being implemented into healthcare, supply chain, and retail systems, through smart contracts, smart devices, smart identity management. Although the use of this technology brings with it benefits, it can also still cause problems. A particular problem is derived from the immutability property, which means that fraudulent transactions or transfers of information cannot be reversed.

Rationale: Blockchains can be attacked via a deluge of requests or transactions within a short time span, resulting in the loss of connectivity to the blockchain for users and businesses, or even financial institutions. Therefore, the rapid detection of anomalies from such activities is critical in order to prevent damage from occurring, or correct any damage as soon as possible to reduce the severity of its impact.
 
Overall objectives: This project will study the problem of anomaly and fraud detection from the perspective of blockchain-based networks. Anomaly and fraud detection in blockchain-based networks is more complex due to their unique properties such as decentralisation, global reach, anonymity, etc., which make them different from traditional networks.
 
Specific aims: To further the understanding of the sources and behaviours of anomalies and fraud in blockchain-based networks, and develop new improved methods for both static and dynamic anomaly detection that can be used alongside blockchain-based systems for real-time fraud detection.
 
Methods: Developing and implementing static anomaly detection methods via a hybrid approach and  developing dynamic anomaly detection methods using extreme value theory.
 
Expected results: This research work will be able to contribute to improving the security relating to blockchain-based networks by providing more accurate and efficient methods for detecting anomalies and fraud and reducing the impact of losses resulting from these anomalies.
 
Impact for the field: The project will be particularly beneficial alongside real world blockchain-based networks to allow for the fast detection of anomalous or fraudulent data, preventing damage or allowing for damage to be corrected as soon as possible.  For cryptocurrency networks, this will reduce the impact of market manipulation, fraud, and more widely on global financial markets, currencies, and trade. In addition, the project will be of interest to a broad range of cryptocurrency and blockchain stakeholders including (but not limited to) academics, financial institutions,policymakers, regulators, and cybercrime agencies.

Output and Results

This project has boosted collaboration both within the scientific communication and wider public. A few indicators to show the impact of this project are listed below.

3

Scientific Publications

2

Knowledge Transfer Events

2

Third-Party Funds

2

Datasets

2

Public Communications

2

Follow-up projects

4

Academic Events

2

Collaborations

Scientific Publications

01 Metaverse non-fungible tokens

Osterrieder, J., Chan, S., Zhang, Y., & Chu, J. (2024). Metaverse non-fungible tokens. Financial Innovation. Manuscript under review

This study undertakes a comprehensive examination of Non-Fungible Tokens (NFTs) within the Metaverse, starting with a review of the Metaverse's evolution, the emergence of NFTs, and the transformative benefits of the Metaverse. It explains the concepts of Web2 and Web3, providing a comparative analysis between historical and contemporary iterations of the Metaverse. This foundational review sets the stage for an in-depth empirical analysis of five notable NFTs: AXS, MANA, ENJ, THETA, and SAND. Utilizing data sourced from CoinMarketCap spanning November 2020 to October 2022, the study employs a multifaceted analytical approach encompassing descriptive statistics, Hill's estimator, detrended fluctuation analysis, volatility clustering, asymmetric volatility clustering, and quantile-on-quantile regression analysis. This rigorous methodology uncovers a set of stylized facts that characterize the unique market behaviors, pricing mechanisms, and investor dynamics within the Metaverse NFT space. The findings not only illuminate the nuanced complexities of the NFT market but also provide critical insights for investors, content creators, and policymakers, highlighting the necessity for innovative strategies and regulatory considerations in navigating the evolving Metaverse ecosystem. To bolster the robustness of our findings, a comparative analysis was conducted using Metaverse indices from Bloomberg (BB) and data provided by Yield Guild Games (YGG). This study not only enriches the academic discourse on digital assets but also lays a groundwork for future research and development in the domain of Metaverse NFTs.  

02 Enhancing security in blockchain networks: Anomalies, frauds, and advanced detection techniques

Osterrieder, J., Chan, S., Chu, J., Zhang, Y., Hadji Misheva, B., & Mare, C. (2024). Enhancing security in blockchain networks: Anomalies, frauds, and advanced detection techniques. Financial Innovation. Manuscript under review.

Blockchain technology, a foundational distributed ledger system, enables secure and transparent multi-party transactions. Despite its advantages, blockchain networks are susceptible to anomalies and frauds, posing significant risks to their integrity and security. This paper offers a detailed examination of blockchain's key definitions and properties, alongside a thorough analysis of the various anomalies and frauds that undermine these networks. It describes an array of detection and prevention strategies, encompassing statistical and machine learning methods, game-theoretic solutions, digital forensics, reputation-based systems, and comprehensive risk assessment techniques. Through case studies, we explore practical applications of anomaly and fraud detection in blockchain networks, extracting valuable insights and implications for both current practice and future research. Moreover, we spotlight emerging trends and challenges within the field, proposing directions for future investigation and technological development. Aimed at both practitioners and researchers, this paper seeks to provide a technical, in-depth overview of anomaly and fraud detection within blockchain networks, marking a significant step forward in the search for enhanced network security and reliability.

03 Stylized facts of Metaverse Non-Fungible Tokens

Chan, S., Chu, J., & Osterrieder, J. (2024). Stylized facts of Metaverse Non-Fungible Tokens. Physica A: Statistical Mechanics and its Applications. Manuscript submitted for publication.

This study undertakes a comprehensive examination of Non-Fungible Tokens (NFTs) within the Metaverse, starting with a review of the Metaverse's evolution, the emergence of NFTs, and the transformative benefits of the Metaverse. It explains the concepts of Web2 and Web3, providing a comparative analysis between historical and contemporary iterations of the Metaverse. This foundational review sets the stage for an in-depth empirical analysis of five notable NFTs: AXS, MANA, ENJ, THETA, and SAND. Utilizing data sourced from CoinMarketCap spanning November 2020 to October 2022, the study employs a multifaceted analytical approach encompassing descriptive statistics, Hill's estimator, detrended fluctuation analysis, volatility clustering, asymmetric volatility clustering, and quantile-on-quantile regression analysis. This rigorous methodology uncovers a set of stylized facts that characterize the unique market behaviors, pricing mechanisms, and investor dynamics within the Metaverse NFT space. The findings not only illuminate the nuanced complexities of the NFT market but also provide critical insights for investors, content creators, and policymakers, highlighting the necessity for innovative strategies and regulatory considerations in navigating the evolving Metaverse ecosystem.

To bolster the robustness of our findings, a comparative analysis was conducted using Metaverse indices from Bloomberg (BB) and data provided by Yield Guild Games (YGG).   

This study not only enriches the academic discourse on digital assets but also lays a groundwork for future research and development in the domain of Metaverse NFTs.

04 GARCH modelling of cryptocurrencies.

Chu, J., Chan, S. and Osterrieder, J., 2017. GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), p.17. [Citations: 340]
 

With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates.

05 A statistical analysis of cryptocurrencies

Chan, S., Chu, J. and Osterrieder, J., 2017. A statistical analysis of cryptocurrencies. Journal of Risk and Financial Management, 10(2), p.12. [Citations: 213]

We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.

06 Lead behaviour in bitcoin markets

Y Chen, P Giudici, B Hadji Misheva, S Trimborn. Lead behaviour in bitcoin markets (2020). Risks 8 (1), 4

We aim to understand the dynamics of Bitcoin blockchain trading volumes and, specifically, how different trading groups, in different geographic areas, interact with each other. To achieve this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of trading volumes, both in time and in space. The extension is based on network models, which improve pure autoregressive models, introducing a contemporaneous contagion component that describes contagion effects between trading volumes. Our empirical findings show that transactions activities in bitcoins is dominated by groups of network participants in Europe and in the United States, consistent with the expectation that market interactions primarily take place in developed economies.

Academic Events

The team has received invitations to numerous international conferences, serving roles as keynote speakers, session chairs, or even organizing the events themselves.

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Seminar-AUS 2024

May 5, 2024. Dr. Osterrieder was invited to talk as the keynote speaker at an inaugural research conference on Mathematics and Related Area at the Department of Mathematics, American University of Sharjah. The talk titled 'Data Science in Finance - Applications' goes deeper into the mathematical concepts and their applications for research initiatives, advancements, and innovations in the field of Digital Finance. 

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AUS-ICMS 2025

February 19 - 22, 2025. AUS Fourth International Conference on Mathematics and Statistics, AUS-ICMS 2025,Member of the International Advisory Board: Joerg Osterrieder talks at a Special Session on Statistics and Data Science for Digital Finance: Joerg Osterrieder, Stephen Chan, Jeffrey Chu, Yuanyuan Zhang

Collaborations

Department of Mathematics, American University of Sharjah, UAE
Prof. Dr. Stephen Chan
- In-depth constructive exchanges on approaches, methods, or results
- Publications
- Exchange of personnel

 

Department of Mathematics, University of Manchester, UK
Dr. Yuanyuan Zhang
- In-depth constructive exchanges on approaches, methods, or results
- Publications

 

Department of Mathematics, Renmin University, China
Prof. Dr. Jeffrey Chu
- In-depth constructive exchanges on approaches, methods, or results
- Publications

 

Department of Statistics, Babeș-Bolyai University, Romania
Prof. Dr. Codruța Mare
- In-depth constructive exchanges on approaches, methods, or results
- Publications
- Exchange of personnel

Department of Business, Bern Business School, Switzerland
Prof. Dr. Branka Hadji Misheva
- In-depth constructive exchanges on approaches, methods, or results
- Publications
- Exchange of personnel

COST Action CA19130 Fintech and Artificial Intelligence in Finance

Action Chair: Joerg Osterrieder

- In-depth constructive exchanges on approaches, methods, or results

- Publications

- Exchange of personnel

MSCA Industrial Doctoral Network on Digital Finance

Coordinator: Joerg Osterrieder

- In-depth constructive exchanges on approaches, methods, or results

- Publications

- Exchange of personnel

Third-Party Funds

The team has applied to and acquired several large national and international research funds, from institutional grants, national funding organisations, the Swiss National Science Foundation annd the Horizon Europe framework programme. 

01

Faculty Research Grant 2024 (FRG24), AUS

American University of Sharjah, UAE

Proposal Number: FRG24-E-S25

Grant Period: 1 June 2024 – 31 May 2025

Title: From Digits to Dollars: The Evolution of Price Impact in Digital Assets

Stephen Chan (PI); Joerg Osterrieder(Co-PI)

Awarded: 25,000 AED

04

MSCA Industrial Doctoral Network on Digital Finance

Horizon Europe

Title: Digital Finance - Reaching New Frontiers

Joerg Osterrieder (Coordinator)

Awarded: 4.5 Mio EUR

07

Leading House MENA Research Partnership Grants 2023

Leading House MENA

Title: Get Smart: Finance from Switzerland meets Blockchain from UAE: Anomaly and fraud detection in financial blockchain networks

Joerg Osterrieder (PI); Stephen Chan (Co-PI), not approved

Link: 

https://www.hes-so.ch/la-hes-so/international/leading-house-mena

10

China Leading house 2024

Leading House Asia

Title: Graph-Theoretic Analysis for Consumer Credit Risk Assessment in Personal Lending

Joerg Osterrieder (PI); Jeffrey Chu (Co-PI)

Link: 

https://ethz.ch/en/the-eth-zurich/global/eth-global-news-events/2023/11/2023-call-for-applied-research-partnership-grants.html

Awarded: 25, 000 CHF

02

Faculty Research Grant 2023 (FRG23), AUS

Faculty Research Grant 2023 (FRG23), AUS

American University of Sharjah, UAE

Proposal Number: FRG23-C-S68

Grant Period: 1 June 2023 – 31 May 2025

Title: Medium Anomaly and Fraud Detection in Blockchain and Cryptocurrency Networks

Stephen Chan (PI); Joerg Osterrieder(Co-PI)

Awarded: 248,000 AED

05

Leading House MENA Research Partnership Grants 2021

Leading House MENA

Title: Anomaly and fraud detection in blockchain networks

Joerg Osterrieder (PI); Stephen Chan (Co-PI)

Link: 

https://www.hes-so.ch/la-hes-so/international/leading-house-mena

Awarded: 15, 000 CHF

08

Leading House MENA 2023 Consolidation grants

Leading House MENA

Title: A real-time risk rating system for digital assets

Joerg Osterrieder (PI); Stephen Chan (Co-PI), not approved

Link: 

https://www.hes-so.ch/la-hes-so/international/leading-house-mena

03

Centre for Digital Trust and Society’s seed corn funding competition, 2023/24

University of Manchester, UK

Title: Blockchain Forensics: Criminal Analysis using R Shiny

Yuanyuan Zhang (PI); Joerg Osterrieder (Co-PI)

Link of the call: https://www.socialsciences.manchester.ac.uk/dts/research/seedcorn-funding/

Awarded: 8560 GBP

06

Leading House MENA Research Partnership Grants 2022

Leading House MENA

Title: Get Smart: Finance from Switzerland meets Blockchain from UAE: Anomaly and fraud detection in financial blockchain networks

Joerg Osterrieder (PI); Stephen Chan (Co-PI), not approved

Link: 

https://www.hes-so.ch/la-hes-so/international/leading-house-mena

09

China Leading house 2023

Leading House Asia

Title: A Novel Approach to Tracking Digital Asset Market Efficiency

Joerg Osterrieder (PI); Jeffrey Chu (Co-PI), not approved

Link: 

https://ethz.ch/en/the-eth-zurich/global/eth-global-news-events/2023/07/2023-call-for-research-partnership-grants.html

Follow-up projects

MSCA Industrial Doctoral Network on Digital Finance, 2024 - 2027

COST Action CA19130 Fintech and Artificial Intelligence in Finance, 2024

Ongoing cooperation on various publications

  • Towards a European Financial Data Space:
    Collaborative learning across data silos Detecting anomalies and dependence structures in high dimensional, high frequency financial data Predicting financial trends using text mining and NLP Deep Generation of Financial Time Series
  • Artificial Intelligence for Financial Markets
    Strengthening European financial service providers through applicable reinforcement learning Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms Challenges and opportunities for the uptaking of technological development by industry
  • Towards explainable and fair AI-generated decisions
    Audience-dependent explanations Investigating the utility of classical XAI methods in financial time series Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns
  • Driving digital innovations with Blockchain applications
    Industry standard for blockchain Fraud detection in financial networks Risk index for cryptos
  • Sustainability of Digital Finance
    Modelling green credit scores for a network of retail and business clients A recommender system to re-orient investments towards more sustainable technologies and businesses Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period

Scientific Report

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