Day 1: Thursday 16th May
08.30 – 09.00
Registration and Welcome Coffee
09.00 – 10.30: Recent Developments in Mathematical Climate Finance (Extended Talk followed by Q&A)
- Carbon equivalence principle for asset pricing
- Minimization of carbon costs
- Carbon-linked financial products
- CO2eVA: how to adjust derivatives valuation for carbon price risk
- Probabilistic transition-risk pricing methods for financial instruments
Andrea Macrina:
Professor of Mathematics, University College London (UCL)
Andrea Macrina:
Andrea Macrina: Professor of Mathematics, University College London (UCL)
Andrea Macrina is Professor of Mathematics and the Director of the Financial Mathematics MSc Programme in the Department of Mathematics, University College London. His current research programme includes projects in climate finance, the development of quantile processes with applications in insurance and finance, and stochastic interpolation. Dr Macrina is Adjunct Professor at the University of Cape Town in the African Institute of Financial Markets and Risk Management where in 2014 he co-founded the Financial Mathematics Team Challenge (FMTC). Andrea is a recipient of the Fields Research Fellowship awarded by The Fields Institute for Research in Mathematical Sciences. He holds a PhD in Mathematics from King’s College, University of London, and an MSc in Physics from the University of Bern, Switzerland. Personal website: https://amacrina.wixsite.com/macrina
Chris Cormack:
Co-founder and Managing Director, Quant Foundry
Chris Cormack:
Chris Cormack: Co-founder and Managing Director, Quant Foundry
Dr Chris Cormack is co-founder and Managing Director of Quant Foundry an innovative company that has designed and built novel risk methodologies to assist in the measurement of climate risks. He is also active in areas of artificial intelligence research with specific applications in agriculture and finance. Chris’s primary interest is focused on designing deep quantitative climate risk methodologies, both as a professional engagement and through academic engagements to improve the capability and understanding of climate risk measurement and mitigation. He has been actively involved in recent research activities as an associate business Fellow within the UK’s Centre for Greening the Financial System (CGFI) where he is involved in developing ideas in transition risk management. Climate risk model frameworks published in 2020 have been influential in shaping climate risk methodologies in the regulatory space. He is involved with the Climate Financial Risk Forum and publishing research on climate risk methodologies with policy makers in the Bank of England.
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: Embedding Climate Risk & The Brave New Normal for Quants
Rutang Thanawalla:
Visiting Fellow, London Institute of Banking & Finance & Strategic Adviser, Climate Tech
Rutang Thanawalla:
Rutang Thanawalla: PhD, Visiting Fellow, London Institute of Banking & Finance & Strategic Adviser, Climate Tech
11.45 – 12.30: Corporate Sustainability Affecting Average Returns
The impact of Environmental Social and Governance (ESG) factors on the performance of listed stocks is still controversial in the literature. We aim to identify groups of companies according to their ESG temporal dynamics to evaluate whether their fluctuations impact the market risk factors. We assess a potential relationship between financial and non-financial risks within the Fama and French framework (Fama and French, 2015) according to the ESG score and its components. We discriminate companies which increase their ESG compliance using a hierarchical time-series clustering approach and computing the similarity between two-time series by Euclidean distance and Dynamic Time Warping (DTW). The latter is useful when the time series have different shifts and speeds. We compare four hierarchical methods with different linkage criteria (average, complete, Ward.D, Ward.D2). The optimal number of clusters is selected based on four cluster validity indices (Silhouette, Calinski-Harabasz, Clustering Order Preservation, and Dunn). The data utilized in this paper refer to the S&P500 and come from the Refinitiv database (Refinitiv, 2023). The cluster validity indices indicate two clusters as the best choice. However, while the Euclidian distance with the average method is the best combination for the ESG score and its components E and S, the DTW distance with the ward.D method is the best for the G score. Overall, we find different effects on the market risk, companies who are classified as “committed” toward sustainability show a different sensitivity to the market excess return factor respect to the less committed companies. The other risk factors seem to have little impact on the committed or uncommitted companies.
Rita Laura D’Ecclesia:
Professor of Quantitative Finance. Sapienza University of Rome, Italy
Rita Laura D’Ecclesia:
Rita Laura D’Ecclesia: Professor of Quantitative Finance. Sapienza University of Rome, Italy
12.30 – 13.30: Lunch
13.30 – 14.15: Navigating the Environmental, Social, and Governance (ESG) landscape: Constructing a robust and reliable scoring engine
Nicole Königstein:
Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG
Nicole Königstein:
Nicole Königstein: Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG
Nicole Königstein is a distinguished Data Scientist and Quantitative Researcher, currently working as Data Science and Technology Lead at impactvise, an ESG analytics company, and as Head of AI and Quantitative Research at Quantmate, an innovative FinTech startup focused on alternative data in predictive modeling. Alongside her roles in these organizations, she serves as an AI consultant across diverse industries, leading workshops and guiding companies from the conceptual stages of AI implementation through to final deployment.
As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at various universities. She is a regular speaker at renowned AI and Data Science conferences, where she conducts workshops and educational sessions. In addition, she is an influential voice in the data science community, regularly reviewing books in her field and offering her insights and critiques. Nicole is also the author of the well-received online course, “Math for Machine Learning.
14.15 – 15.00: Transition Risk for Mortgage Portfolios: From Energy Labels Forecasting to Stress Testing
Svetlana Borovkova:
Climate Risk Quant Research, Bloomberg
Svetlana Borovkova:
Svetlana Borovkova: Climate Risk Quant Research, Bloomberg
Svetlana Borovkova is Climate Risk Quant Researcher at Bloomberg, developing models and tools for Climate Risk and Climate Stress Testing for a wide variety of financial institutions.
Previously she held a position of an associate professor of Quantitative Finance and Risk Management at the Vrije Universiteit Amsterdam and was heading the quantitative modelling team at Probability & Partners.
Svetlana Borovkova has over 60 publications in academic and professional journals as well as books and book contributions. She is a frequent invited and keynote speaker at major international conferences such as QuantMinds and RiskMinds.
15.00 – 15.30: Afternoon Break and Networking Opportunities
15.30 – 16.15: Foundation NLP Models for ESG data extraction
- Learning not to “make stuff up”
- SFT (Supervised Fine Tuning)
- RLHF (Reinforcement Learning with Human Feedback)
Robert Dargavel Smith:
Director of Machine Learning Engineering , Clarity AI
Robert Dargavel Smith:
Robert Dargavel Smith: Director of Machine Learning Engineering , Clarity AI
“Robert Smith is the Director of Machine Learning Engineering at Clarity AI. Previously he was Head of Data Science at IHS Markit (now part of S&P Global). He has worked in capital markets for over 25 years in Banco Santander and ABN Amro, holding various positions from Head of CVA Desk to Global Head of Quantitative Analysis.”
16.15 – 17.00: Integrating Large Language Models for Enhanced ESG and Climate Risk Analysis in Finance
In this session, we’re diving into how Large Language Models (LLMs) can be used in ESG and climate risk analysis for finance. We’ll break down how these AI tools help us sift through huge amounts of data to spot risk patterns and insights that might not be obvious at first glance. We’ll see firsthand the value of LLMs in understanding and managing the risks tied to ESG. Will look into how to leverage these technologies for a more informed decision-making approach in finance.
David Pacheco Aznar:
Founding Partner & Chief of AI Research and Development, Raven Risk AI
David Pacheco Aznar:
David Pacheco Aznar: Founding Partner & Chief of AI Research and Development, Raven Risk AI
17.00 – 17.45: Panel: The Impact of Large Language Models and Generative AI on ESG Sustainability Goals
- How sustainable is AI and LLM? What can be done to make it more sustainable and help companies, like Quant funds, meet their ESG goals?
- AI and in particular LLM are becoming more ingrained in everyday society – is there sufficient Data Center capacity in Europe to accommodate this and Quant fund growth in the years to come
- Can adopting a hybrid IT strategy, for example by using a combination of On Premise and Cloud (public and private), reduce costs and CO2 emissions?
- Greenwashing is becoming a relevant issue for listed companies, How companies can apply safeguards and mitigants to address potential greenwashing risks?
- AI and LLM allow to capture non linearities in relationships between ESG features and structural features of the firms. How can this help companies to comply with the regulator requirements?
- Can AI and LLM help companies to integrate sustainability risks in the decision making process and organizational requirements
- What do you see as the greatest risks of using generative AI in the context of ESG / Sustainability?
Svetlana Borovkova:
Climate Risk Quant Research, Bloomberg
Svetlana Borovkova:
Svetlana Borovkova: Climate Risk Quant Research, Bloomberg
Svetlana Borovkova is Climate Risk Quant Researcher at Bloomberg, developing models and tools for Climate Risk and Climate Stress Testing for a wide variety of financial institutions.
Previously she held a position of an associate professor of Quantitative Finance and Risk Management at the Vrije Universiteit Amsterdam and was heading the quantitative modelling team at Probability & Partners.
Svetlana Borovkova has over 60 publications in academic and professional journals as well as books and book contributions. She is a frequent invited and keynote speaker at major international conferences such as QuantMinds and RiskMinds.
Robert Dargavel Smith:
Director of Machine Learning Engineering , Clarity AI
Robert Dargavel Smith:
Robert Dargavel Smith: Director of Machine Learning Engineering , Clarity AI
“Robert Smith is the Director of Machine Learning Engineering at Clarity AI. Previously he was Head of Data Science at IHS Markit (now part of S&P Global). He has worked in capital markets for over 25 years in Banco Santander and ABN Amro, holding various positions from Head of CVA Desk to Global Head of Quantitative Analysis.”
Andrea Macrina:
Professor of Mathematics, University College London (UCL)
Andrea Macrina:
Andrea Macrina: Professor of Mathematics, University College London (UCL)
Andrea Macrina is Professor of Mathematics and the Director of the Financial Mathematics MSc Programme in the Department of Mathematics, University College London. His current research programme includes projects in climate finance, the development of quantile processes with applications in insurance and finance, and stochastic interpolation. Dr Macrina is Adjunct Professor at the University of Cape Town in the African Institute of Financial Markets and Risk Management where in 2014 he co-founded the Financial Mathematics Team Challenge (FMTC). Andrea is a recipient of the Fields Research Fellowship awarded by The Fields Institute for Research in Mathematical Sciences. He holds a PhD in Mathematics from King’s College, University of London, and an MSc in Physics from the University of Bern, Switzerland. Personal website: https://amacrina.wixsite.com/macrina
David Pacheco Aznar:
Founding Partner & Chief of AI Research and Development, Raven Risk AI
David Pacheco Aznar:
David Pacheco Aznar: Founding Partner & Chief of AI Research and Development, Raven Risk AI
Nicole Königstein:
Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG
Nicole Königstein:
Nicole Königstein: Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG
Nicole Königstein is a distinguished Data Scientist and Quantitative Researcher, currently working as Data Science and Technology Lead at impactvise, an ESG analytics company, and as Head of AI and Quantitative Research at Quantmate, an innovative FinTech startup focused on alternative data in predictive modeling. Alongside her roles in these organizations, she serves as an AI consultant across diverse industries, leading workshops and guiding companies from the conceptual stages of AI implementation through to final deployment.
As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at various universities. She is a regular speaker at renowned AI and Data Science conferences, where she conducts workshops and educational sessions. In addition, she is an influential voice in the data science community, regularly reviewing books in her field and offering her insights and critiques. Nicole is also the author of the well-received online course, “Math for Machine Learning.
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