AIDA Conference AI and Digital Assets in Energy and Finance

The Bucharest University of Economic Studies, through the IDA — Institute for Digital Assets and the AI4EFin — Artificial Intelligence for Energy Finance Research Group, is proud to host the AIDA Conference — AI & Digital Assets in Energy and Finance.

This conference is dedicated to advancing the rigorous statistical and theoretical understanding of two rapidly evolving fields: digital assets and energy finance. It aims to convene leading academics to address critical open questions and deepen our analytical frameworks for these transformative domains in finance and beyond.

The conference 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 Modelling 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?

AI-Driven Modelling and Forecasting in Energy Finance. How can artificial intelligence and machine learning methods improve the modelling, forecasting, and risk management of energy markets? What novel approaches — including neural forecasting, large language models, and multimodal AI — can capture the complex dynamics of electricity prices, carbon markets, and renewable energy integration? How do digital assets and blockchain-based mechanisms intersect with energy trading, green finance, and the transition to sustainable energy systems?

Electricity Price Forecasting and Market Design. How can advanced machine learning and deep learning architectures improve short-term and long-term electricity price forecasting? What role do neural forecasting models, transformer architectures, and hybrid approaches play in capturing the complex seasonality, spikes, and negative prices characteristic of modern electricity markets?

Renewable Energy Integration and Grid Analytics. How can AI methods address the challenges of integrating intermittent renewable energy sources into power grids? What novel forecasting and optimisation techniques can improve the management of solar, wind, and storage assets, and how do these interact with energy trading strategies?

Blockchain and Digital Assets in Energy Trading. How can blockchain technology and tokenisation transform energy trading, peer-to-peer energy markets, and renewable energy certificate systems? What are the implications of decentralised energy markets for market efficiency, transparency, and the energy transition?

This conference 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 these transformative fields.

All participants are invited to submit to the DFIN Journal, Springer Nature

Registration link: https://forms.gle/TXVGX2SdvzuAB8LR6

Registration is free (lunch + dinner included)

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