AI & Digital Assets (AIDA) Workshop

ECFI, in partnership with its Co-Director Tiejun Ma (School of Informatics), is proud to host the AI & Digital Assets (AIDA) Workshop together with Wolfgang Karl Härdle, Cathy Yi-Hsuan Chen, and Weining Wang from the IDA Institute for Digital Assets in Bucharest.

This focused workshop on “AIDA = AI & Digital Assets” is dedicated to advancing the rigorous statistical and theoretical understanding of the nascent field of digital assets (DA). This workshop aims to convene leading academics to address critical open questions and deepen our analytical frameworks for these rapidly evolving analytical instruments for finance and beyond.

Digital assets, by their very nature, present unique challenges and opportunities for quantitative analysis due to their inherent granularity, decentralised structures, and novel mechanisms. This workshop will explore key directions and foster discussions on the following pivotal areas:

• Microstructure and Granularity of Digital Assets: How do we formally characterise and model the discrete, often indivisible, units of digital assets? What are the implications of this granularity for market microstructure, price formation, liquidity, and order book dynamics, especially in the absence of traditional market-making structures?
• Statistical Modeling of Digital Asset Returns and Volatility: What novel statistical models are required to capture the heavy-tailed distributions, jumps, and regime-switching behaviours observed in digital asset prices? How can we effectively model and forecast their often extreme volatility, considering the unique informational and transactional characteristics of blockchain data?
• Network Effects and Interdependencies: How do the underlying distributed ledger technologies and network effects influence the statistical properties of digital assets? What methodologies can be employed to quantify and model the complex interdependencies between different digital assets, protocols, and the broader financial ecosystem?
• Risk Management and Portfolio Optimisation in Digital Asset Markets: Given the distinct statistical properties and operational risks associated with digital assets, what robust statistical frameworks are necessary for effective risk measurement, stress testing, and portfolio optimisation? How can traditional risk models be adapted or new ones developed to account for the unique characteristics of this asset class?
• Causal Inference and Mechanism Design in Decentralised Finance (DeFi): How can statistical inference be applied to understand the causal impact of various protocols, governance mechanisms, and economic incentives within the DeFi landscape? What statistical tools are appropriate for analysing and designing efficient and robust mechanisms in decentralised environments?

This workshop seeks contributions that push the boundaries of current statistical and theoretical methodologies. We invite researchers to engage in discussions that will lay the groundwork for a more profound and statistically grounded understanding of this transformative asset class.

All participants are invited to submit to the Special Issue of the Journal Financial Econometrics.

Registration fee

• £50 for external attendees (lunch included)
• Free for University of Edinburgh staff and students (lunch included). Please contact Jodie for the internal passcode: Jodie.Cameron@ed.ac.uk

Spaces are limited, please register early to secure your place.

Programme

Day 2 will be devoted to a one-day compact quantitative finance class for PhD students.

Proposed Modules as below:

Students will employ the Q2 eco system, i.e. follow the vertical integration of code and knowledge platforms via Quantinar and Quantlet

Relevant websites

• Institute for Digital Assets
• Quantinar
• Blockchain Research Center
• Quantlet

Partners