World Business StrategiesServing the Global Financial Community since 2000

Main Conference Day 2: Friday 29th September

08.30 – 09.00: Morning Welcome Coffee

All Streams

09.00 – 10.00: Legends in Quantitative Finance: Helyette Geman

Introduction: Rita Laura D’Ecclesia

Helyette Geman was named ‘Financial Engineer of the Year 2022’ by the International Association for Quantitative Finance, the first woman to be honoured in the prize’s 30-year history.

Keynote: Some Topics around Climate: Critical Metals, Green Bunker Fuels, Ammonia, Fertilizers

Helyette Geman:

Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University

Helyette Geman: PhD, PhD: Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University, Visiting Professor, Inland Norway University of Applied Sciences

Director, Commodity Finance Centre, Birkbeck-University of London

Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University

Hélyette Geman is a Graduate of Ecole Normale Superieure in Mathematics and holds PhDs in Probability and Finance and a Masters’ degree in Theoretical Physics.

She has published more than 100 papers in Quantitative Finance; her book ‘Commodities and Commodity Derivatives’ is the reference in the field.

Hélyette Geman has taught in a number of prestigious Institutions worldwide and consulted for trading entities such as Louis Dreyfus, EDF Trading or Total Gas & Power.

Named ‘Financial Engineer of the Year 2022’ by the International Association for Quantitative Finance.

Generative AI / Large Language Models Stream

10.00 – 10.45: The Synergy of AI and Blockchain with a case study on Wrong-Way Risk

Abstract: We discuss a new kind of compiler that helps with solving generic risk and pricing problems. By abstracting complex mathematics, this innovative compiler enables blockchain storage to encompass not only smart trades but also analytics and market data. Moreover, it integrates with Language Model Libraries (LLMs) to facilitate code generation and offers plain English interfaces for code explanations. Through a case study centered around wrong-way risk for non-banks and capital/collateral optimization strategies, we shed light on the practical implications of this powerful combination of AI, blockchain, and risk analysis.

Claudio Albanese:

Founder, Global Valuation

Claudio Albanese: Founder, Global Valuation

10.45 – 11.15: Morning Break and Networking Opportunities

Generative AI / Large Language Models Stream

11.15 – 12.00: Conditioning in Quantitative Finance

In the talk we consider examples for applying conditioning methods in Quantitative Finance. Especially we consider:

Conditioning for Bermudan Options, Data Imputation or Local Stochastic Volatility together with numerical methods to handle applications.

  • Concepts (conditional expectation and variance, …)
  • Monte Carlo, PDE and other classical techniques
  • Conditional Variational Autoencoders, Bayesian Methods and other ML techniques

Jörg Kienitz:

Quantitative Finance and Machine Learning, Acadiasoft

Jörg Kienitz: Quantitative Finance and Machine Learning (Acadiasoft), Partner (Quaternion), Adjunct Prof (UCT), Assistant Prof (BUW)

Jörg Kienitz is a partner at Quaternion, Acadia’s Quant Services division. He owns the finciraptor.de website – an educational platform for Quantitative Finance and Machine Learning. Jörg consults on the development, implementation, and validation of quantitative models. He is an Assistant Professor at the University of Wuppertal and an Adjunct Associate Professor in AIFMRM at the University of Cape Town. He regularly addresses major conferences, including Quant Minds, RISK or the WBS Quant Conference. Jörg has authored four books, Monte Carlo Frameworks (with Daniel J. Duffy), Financial Modelling (with Daniel Wetterau), and Interest Rate Derivatives Explained I and II (with Peter Caspers). He also co-authored research articles that appeared in leading journals like Quantitative Finance, RISK or Mathematics in Industry.

Generative AI / Large Language Models Stream

12.00 – 12.45: Generative Machine Learning for Multivariate Equity Returns

The use of machine learning to generate synthetic data has grown in popularity significantly in the last few years. The core methodology these models use is to learn the distribution of the underlying data, similar to the classical methods common in finance of fitting statistical models to data. In this presentation, we discuss the efficacy of using modern machine learning methods, specifically conditional importance weighted autoencoders (a variant of variational autoencoders) and conditional normalizing flows, for the task of modeling the returns of equities.

We apply our method to learn a 500 dimensional joint distribution for S&P 500 members. We show that this generative model has a broad range of applications in finance, including generating realistic synthetic data, volatility and correlation estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization.

Achintya Gopal:

Machine Learning Quant Researcher, Bloomberg

Achintya Gopal: Machine Learning Quant Researcher, Bloomberg

Achintya Gopal is a Machine Learning Quant Researcher in the Quantitative Research group in the Office of the CTO at Bloomberg, where he works on applying machine learning within the financial domain. Prior to that, he worked on estimating carbon emissions using machine learning, developing new models in normalizing flows, and exploring new methods to evaluate statistical models with model uncertainty. More recently, he has been working on a variety of projects ranging from volatility modeling using neural networks, causal inference for investing, generative models in differential privacy, active learning for NLP, and the interpretability of large language models.

12.45 – 13.45: Lunch

Generative AI / Large Language Models Stream

13.45 – 15.15: Differential Machine Learning: Nailing fast XVA and friends with ML

Extended talk.

Antoine Savine:

Head of Macro Analytics,

Antoine Savine: Head of Macro Analytics,

Antoine Savine has worked for various Investment Banks since 1995, along Bruno Dupire, Leif Andersen and Marek Musiela. He was Global Head of Quantitative Research for Fixed Income, Currency and Credit Derivatives for BNP-Paribas 1999-2009, and currently works in Copenhagen for Danske Bank, where his work with Jesper Andreasen earned the In-House System of the Year 2015 Risk Award. His upcoming publications in Wiley’s Computational Finance series are dedicated to teaching the technologies implemented in those award-winning systems.

Antoine also teaches Volatility Modeling and Numerical Finance in the University of Copenhagen’s Masters of Science in Mathematics-Economics. The curriculum for his Numerical Finance lectures is being published by Wiley under the name “AAD and Parallel Simulations”.

Antoine holds a Masters from the University of Paris (Jussieu) and a PhD from the University of Copenhagen, both in Mathematics.

Brian Norsk Huge:

Head of Quant, Saxo Bank

Brian Norsk Huge: Head of Quant, Saxo Bank

Brian Huge is working as the head of Quant at Saxo Bank. Before joining Saxo Bank Brian worked as a quant for 20 years in Danske Bank. Brian has a Ph.D. in Mathematical Finance from University of Copenhagen. In 2012 he was awarded Quant of the Year for his work on Volatility Interpolation and Random Grids.

15.15 – 15.45: Afternoon Break and Networking Opportunities

All Streams: Closing talk

15.45 – 16.15: How a green data strategy can reduce energy bills and cut CO₂ emissions.

  • Can Hedge Funds using quantitative algorithmic trading be sustainable?
  • Can operating and environmental costs be managed with exponential data growth?
  • Increasingly Hedge funds with quant operations are looking to data centers to locate their high-density compute requirements but can these IT costs be managed? And what is the environmental impact?

A: By Leveraging green data strategies there is a way for Quant funds to optimise energy efficiency and minimise CO₂ footprint

      • How easy is it to (co)locate IT data to green data center locations such as the Nordics?

A: Harnessing the power of green data strategies: A sustainable approach to lower energy bills and CO₂ emission reduction 

 

Warren Barrie:

Global Sales Director, Bulk Data Centers

Warren Barrie: Global Sales Director, Bulk Data Centers

08.30 – 09.00: Morning Welcome Coffee

All Streams

09.00 – 10.00: Legends in Quantitative Finance: Helyette Geman

Introduction: Rita Laura D’Ecclesia

Helyette Geman was named ‘Financial Engineer of the Year 2022’ by the International Association for Quantitative Financethe first woman to be honoured in the prize’s 30-year history.

Keynote: Some Topics around Climate: Critical Metals, Green Bunker Fuels, Ammonia, Fertilizers

Helyette Geman:

Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University

Helyette Geman: PhD, PhD: Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University, Visiting Professor, Inland Norway University of Applied Sciences

Director, Commodity Finance Centre, Birkbeck-University of London

Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University

Hélyette Geman is a Graduate of Ecole Normale Superieure in Mathematics and holds PhDs in Probability and Finance and a Masters’ degree in Theoretical Physics.

She has published more than 100 papers in Quantitative Finance; her book ‘Commodities and Commodity Derivatives’ is the reference in the field.

Hélyette Geman has taught in a number of prestigious Institutions worldwide and consulted for trading entities such as Louis Dreyfus, EDF Trading or Total Gas & Power.

Named ‘Financial Engineer of the Year 2022’ by the International Association for Quantitative Finance.

Volatility, Pricing & Modelling Stream

10.00 – 10.45: Model Risk Quantification based on Relative Entropy

Outline

  • Asset pricing foundations
  • Model risk and entropy
  • Challenger model framework
  • Model risk quantification in practice

Abstract

Model risk can be broadly defined as the possibility of suffering losses due to errors either in the development or use of models. Financial pricing and risk measurement are subject to model risk, and its quantification is a hot topic in both academia and the financial industry. This lecture presents a technique for challenging derivative pricing models that will also help to quantify model risk. The proposed approach is based on the fundamental theorems of asset pricing, which allow a model to be interpreted as a pricing measure, and on the use of the minimum relative entropy technique as a way of changing between measures. The proposed methodology makes the following contributions:

  1. it overcomes many of the limitations of previous approaches that identified models and probability measures.
  2. it defines a statistical divergence for both quantifying the divergence between measures,

and therefore, between models, and determining the set of optimal market instruments in the calibration of models.

III. It can assess the model risk of a target portfolio. Further, it is theoretically possible to determine a model without model risk for such a portfolio.

This framework has been explicitly designed to be easily applied by financial industry practitioners.

Daniel Arrieta:

Senior Model Validation Quant, Santander

Daniel Arrieta: Senior Model Validation Quant, Santander

Daniel Arrieta is Senior Model Validation Quant at Santander and Associate professor at Universidad Complutense de Madrid (UCM). At Santander Daniel validates XVA Front Office models and as researcher he is focused in pricing models and model risk quantification. Daniel holds a PhD in Mathematical Finance from Universidad Complutense de Madrid, a MSc in Advanced Mathematics from U.N.E.D. and a MSc in Quantitative Finance from Escuela de Finanzas Aplicadas.

10.45 – 11.15: Morning Break and Networking Opportunities

Volatility, Pricing & Modelling Stream

11.15 – 12.00: Derivatives with and without ML

  • Implied volatility surface construction
  • Fast option pricing
  • A new Greek for path dependence

Bruno Dupire:

Head of Quantitative Research, Bloomberg

Bruno Dupire: Head of Quantitative Research, Bloomberg

Bruno Dupire is the Global Head of Quantitative Research, CTO Office at Bloomberg, which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008. He runs and organizes the Bloomberg Quant (BBQ) seminar, the largest monthly event of this kind.

Volatility, Pricing & Modelling Stream

12.00 – 12.45: Towards the lower bound for Bermudans

  • An achievable lower bound for a Bermudan is not always the max European
  • Finding a robust (model-free) lower bound for Bermudans is surprisingly difficult
  • It is important for designing “Bermudan discount” adjustment models prevalent in the industry for the last 30+ years
  • We make some progress towards this goal

Vladimir Piterbarg:

MD, Head of Quantitative Analytics and Quantitative Development, NatWest Markets

Vladimir Piterbarg: MD, Head of Quantitative Analytics and Quantitative Development at NatWest Markets

12.45 – 13.45: Lunch

Volatility, Pricing & Modelling Stream

13.45 – 14.30: Stochastic Volatility for Multi-Factor HJM Framework

Abstract: We introduce log-normal stochastic volatility extension of the Factor HJM model for arbitrage free evolution of the forward curve. We assume non-zero correlation between curve drivers and SV driver for modeling positive implied volatility skew across different tenors. We introduce the SV extension of the Nelson-Siegel term-structure model for practical application. We extend the closed-form solution for the valuation of swaptions in a multi-factor framework and show that the proposed model is able to fit observed swaption implied volatilities across different expiries and tenors.

This talk is based on joint work with Artur Sepp. 

Parviz Rakhmonov:

Vice President, Quantitative Analyst, Citibank

Parviz Rakhmonov: Vice President, Quantitative Analyst, Citibank

Parviz Rakhmonov is a Quantitative Analyst at Citi, London since 2016. Prior to moving to quantitative finance, Parviz spent two years as a post-doctoral researcher at the Faculty of Mechanics and Mathematics in Lomonosov Moscow State University, focusing on research in Analytic Number Theory, teaching courses in Calculus and Number Theory. He holds a PhD and MSc in Mathematics from the Lomonosov Moscow State University.

Volatility, Pricing & Modelling Stream

14.30 – 15.15: Should we Backtest using Overlapping Samples? Correlation and Power

Nikolai Nowaczyk:

Risk Management Contractor

Nikolai Nowaczyk: Risk Management Contractor

15.15 – 15.45: Afternoon Break and Networking Opportunities

All Streams: Closing talk

15.45 – 16.15: How a green data strategy can reduce energy bills and cut CO₂ emissions.

  • Can Hedge Funds using quantitative algorithmic trading be sustainable?
  • Can operating and environmental costs be managed with exponential data growth?
  • Increasingly Hedge funds with quant operations are looking to data centers to locate their high-density compute requirements but can these IT costs be managed? And what is the environmental impact?

A: By Leveraging green data strategies there is a way for Quant funds to optimise energy efficiency and minimise CO₂ footprint

      • How easy is it to (co)locate IT data to green data center locations such as the Nordics?

A: Harnessing the power of green data strategies: A sustainable approach to lower energy bills and CO₂ emission reduction 

Warren Barrie:

Global Sales Director, Bulk Data Centers

Warren Barrie: Global Sales Director, Bulk Data Centers

08.30 – 09.00: Morning Welcome Coffee

All Streams

09.00 – 10.00: Legends in Quantitative Finance: Helyette Geman

Introduction: Rita Laura D’Ecclesia

Helyette Geman was named ‘Financial Engineer of the Year 2022’ by the International Association for Quantitative Financethe first woman to be honoured in the prize’s 30-year history.

Keynote: Some Topics around Climate: Critical Metals, Green Bunker Fuels, Ammonia, Fertilizers

Helyette Geman:

Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University

Helyette Geman: PhD, PhD: Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University, Visiting Professor, Inland Norway University of Applied Sciences

Director, Commodity Finance Centre, Birkbeck-University of London

Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University

Hélyette Geman is a Graduate of Ecole Normale Superieure in Mathematics and holds PhDs in Probability and Finance and a Masters’ degree in Theoretical Physics.

She has published more than 100 papers in Quantitative Finance; her book ‘Commodities and Commodity Derivatives’ is the reference in the field.

Hélyette Geman has taught in a number of prestigious Institutions worldwide and consulted for trading entities such as Louis Dreyfus, EDF Trading or Total Gas & Power.

Named ‘Financial Engineer of the Year 2022’ by the International Association for Quantitative Finance.

Morning Stream Chair:

Erik Vynckier:

Interim Chief Executive, Foresters Friendly Society

Erik Vynckier: Interim Chief Executive, Foresters Friendly Society

Erik Vynckier is board member of Foresters Friendly Society and chair of the Investment Committee, following a career in investment banking, insurance, asset management and the petrochemical industry. He has been Chief Investment Officer and Chief Executive Officer and frequently consults in investment management, quantitative risk management and derivatives.

He co-founded EU initiatives on high performance computing and big data in finance and co-authored “High-Performance Computing in Finance” and “Tercentenary Essays on the Philosophy and Science of Leibniz”.  Erik graduated as MBA at London Business School and as chemical engineer at Universiteit Gent.

ESG & Climate Risk Stream

10.00 – 10.45: ESG Score and the financial statement: an Analysis of European listed companies.

Regulators’ requirements and government policies in terms of sustainability are affecting the investment environment and the challenges and opportunities companies face. The ESG rating and its use in investment choices have become a mantra. We want to investigate the role of ESG ratings in the different European stock indexes. We use data on ESG ratings of all the companies constituent the main European stock indexes and their corporate balance sheet information. Our aim is twofold. First, to study the features of ESG ratings by economic sector in the various countries; second, to identify the main drivers explaining the ESG score for each index. Using a Machine Learning approach, we identify, for each index, three main drivers of the ESG score, which vary by country.

  • ESG Rating
  • Machine Learning
  • Corporate Finance
  • Sustainability.

Rita Laura D’Ecclesia

Professor: Università degli Studi di Roma “La Sapienza”

Rita Laura D’Ecclesia Professor: Università degli Studi di Roma “La Sapienza”

10.45 – 11.15: Morning Break and Networking Opportunities

ESG & Climate Risk Stream

11.15 – 12.00: 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?

Moderator:

Erik Vynckier:

Interim Chief Executive, Foresters Friendly Society

Erik Vynckier: Interim Chief Executive, Foresters Friendly Society

Erik Vynckier is board member of Foresters Friendly Society and chair of the Investment Committee, following a career in investment banking, insurance, asset management and the petrochemical industry. He has been Chief Investment Officer and Chief Executive Officer and frequently consults in investment management, quantitative risk management and derivatives.

He co-founded EU initiatives on high performance computing and big data in finance and co-authored “High-Performance Computing in Finance” and “Tercentenary Essays on the Philosophy and Science of Leibniz”.  Erik graduated as MBA at London Business School and as chemical engineer at Universiteit Gent.

Warren Barrie:

Global Sales Director, Bulk Data Centers

Warren Barrie: Global Sales Director, Bulk Data Centers

Svetlana Borovkova:

Head of Quantitative Modelling, Probability & Partners. Associate Prof, Vrije Universiteit Amsterdam

Svetlana Borovkova: Head of Quantitative Modelling, Probability & Partners and Associate Professor, Vrije Universiteit Amsterdam

Dr Svetlana Borovkova is the partner and Head of Quant Modelling of risk management consulting firm Probability and Partners and an Associate Professor of Quantitative Finance and Risk Management at the  Vrije Universiteit Amsterdam. She is the author of over 60 academic and professional publications and a frequent speaker at conferences such as RiskMinds and QuantMinds. Her work encompasses a wide range of topics, ranging from derivatives pricing and risk modelling to sentiment analysis for quant investing and machine learning in quant finance. Find her work at SSRN and her columns on various finance topics in Financial Investigator.

Rita Laura D’Ecclesia

Professor: Università degli Studi di Roma “La Sapienza”

Rita Laura D’Ecclesia Professor: Università degli Studi di Roma “La Sapienza”

Robert Dargavel Smith:

Lead Data Scientist, Clarity AI

Robert Dargavel Smith: Lead Data Scientist, Clarity AI

“Robert Smith is a Lead Data Scientist 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.”

ESG & Climate Risk Stream

12.00 – 12.45: 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:

Lead Data Scientist, Clarity AI

Robert Dargavel Smith: Lead Data Scientist, Clarity AI

“Robert Smith is a Lead Data Scientist 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.”

12.45 – 13.45: Lunch

Afternoon Stream Chair:

Erik Vynckier:

Interim Chief Executive, Foresters Friendly Society

Erik Vynckier: Interim Chief Executive, Foresters Friendly Society

Erik Vynckier is board member of Foresters Friendly Society and chair of the Investment Committee, following a career in investment banking, insurance, asset management and the petrochemical industry. He has been Chief Investment Officer and Chief Executive Officer and frequently consults in investment management, quantitative risk management and derivatives.

He co-founded EU initiatives on high performance computing and big data in finance and co-authored “High-Performance Computing in Finance” and “Tercentenary Essays on the Philosophy and Science of Leibniz”.  Erik graduated as MBA at London Business School and as chemical engineer at Universiteit Gent.

ESG & Climate Risk Stream

13.45 – 14.30: What the Pandemic, war in Ukraine and the return of inflation tell you – a contrarian look at ESG

  • Geophysical basis of climate and adverse weather events: what do reinsurers think?
  • The energy mix and other resource intensive industries.
  • Two years after “Peak ESG”, what remains? ESG versus free markets.
  • Is the financial industry the right tool to craft environmental policy?
  • How to invest your portfolio through a greenwash boom and bust?

Erik Vynckier:

Interim Chief Executive, Foresters Friendly Society

Erik Vynckier: Interim Chief Executive, Foresters Friendly Society

Erik Vynckier is board member of Foresters Friendly Society and chair of the Investment Committee, following a career in investment banking, insurance, asset management and the petrochemical industry. He has been Chief Investment Officer and Chief Executive Officer and frequently consults in investment management, quantitative risk management and derivatives.

He co-founded EU initiatives on high performance computing and big data in finance and co-authored “High-Performance Computing in Finance” and “Tercentenary Essays on the Philosophy and Science of Leibniz”.  Erik graduated as MBA at London Business School and as chemical engineer at Universiteit Gent.

ESG & Climate Risk Stream

14.30 – 15.15: Improving Intergenerational Equity of Climate Mitigation Pathways using Non-Linear Discounting, Funding Costs and Stochastic Interest Rates. Analysis and Extensions using Techniques from Mathematical Finance.

We investigate the intergenerational equity in classical integrated assessment models – a class of climate models that combine geophysical quantities and economic quantities: economy drives climate change, and climate change impacts the economy.

  • We briefly introduce the models – using the DICE model as an example.
  • We analyse and then extend the model using techniques from mathematical finance.
  • Our analysis shows that the calibration of these models induces intergenerational inequality: future generations lose while current generations profit.
  • This effect is directly linked to the object function and the role of discounting.
  • We extend the model by introducing stochastic interest rates, funding periods and non-linear discounting.
  • Introducing stochastic interest rates adds another perspective: the intergenerational distribution of risk.
  • It turns out that non-linear discounting enormously improves intergenerational equity.

Christian Fries: 

Head of Model Development, DZ Bank

Christian Fries: Head of Model Development, DZ Bank

Christian Fries is head of model development at DZ Bank’s risk control and Professor for Applied Mathematical Finance at Department of Mathematics, LMU Munich.

His current research interests are hybrid interest rate models, Monte Carlo methods, and valuation under funding and counterparty risk. His papers and lecture notes may be downloaded from http://www.christian-fries.de/finmath

He is the author of “Mathematical Finance: Theory, Modeling, Implementation”, Wiley, 2007 and runs www.finmath.net.

15.15 – 15.45: Afternoon Break and Networking Opportunities

All Streams: Closing talk

15.45 – 16.15: How a green data strategy can reduce energy bills and cut CO₂ emissions.

  • Can Hedge Funds using quantitative algorithmic trading be sustainable?
  • Can operating and environmental costs be managed with exponential data growth?
  • Increasingly Hedge funds with quant operations are looking to data centers to locate their high-density compute requirements but can these IT costs be managed? And what is the environmental impact?

A: By Leveraging green data strategies there is a way for Quant funds to optimise energy efficiency and minimise CO₂ footprint

      • How easy is it to (co)locate IT data to green data center locations such as the Nordics?

A: Harnessing the power of green data strategies: A sustainable approach to lower energy bills and CO₂ emission reduction 

Warren Barrie:

Global Sales Director, Bulk Data Centers

Warren Barrie: Global Sales Director, Bulk Data Centers

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