Conference Stream
08.15 – 09.00
Registration and Welcome Coffee
09.00 – 09.45: Large Language Models and how they can be applied in finance
Svetlana Borovkova:
Head of Quantitative Modelling, Probability & Partners. Associate Prof, Vrije Universiteit Amsterdam
09.45 – 10.30: Deep Hedging
Abstract:
Traditional risk management is based on the Greeks provided by classical valuation models. These models typically have simplified dynamics and assume perfect hedge-ability.
As a result, decisions on when and how to hedge are based on traders’ intuition, experience, and view on market dynamics.
With Deep Hedging , we go beyond Greek-based hedging and take a new approach to exotics risk management.
Deep Hedging formulates the hedging problem as a reinforcement learning problem and shifts towards the machine learning paradigm.
We solve for the optimal hedging in incomplete markets using a periodic policy search.
The model-based policy search approximates the hedging actions, the policy, using deep neural networks.
Amira Akkari:
Executive Director, JP Morgan
11.00 – 11.45: Financial Times Series Prediction in the Age of Transformers
Nicole Königstein:
Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG
11.45 – 12.30: PANEL: Talent Attraction & Retention:
Recruiting/Retaining talent
- What are QR Financial Services currently doing and what should they be doing to attract more female talent?
- What strategies are financial companies using to retain talent? Is there anything else that could be done?
Career progression
- At more senior levels the number of women is even lower than at entry level which means that the female population retention rate is low or/and women are not being promoted. Discuss
- Are quantitative positions too specialised which prevents women (and men) to move horizontally to different (and possibly more senior) roles?
- Hybrid working model-does it benefit for retaining/advancing women careers?
- Mentoring vs sponsorship programmes that could specifically help Diversity & Inclusion.
- Limitations for progression in challenging times when there is less opportunities available
- Is there difference in diversity approach/success between US vs Euro banks?
- Company diversity targets-good idea?
- How to overcome unconscious bias
Moderator:
Svetlana Borovkova:
Head of Quantitative Modelling, Probability & Partners. Associate Prof, Vrije Universiteit Amsterdam
Nicole Königstein:
Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG
Wafaa Schiefler:
Executive Director – Commodities Quantitative Researcher, JP Morgan Chase Speaker
Katarina Hunt:
Managing Director, Goldman Sachs
Imane Loumrhari:
VP, QA XVA – Counterparty Risk Trading, Barclays
13.30 – 14.15: Understanding the true level of PD cyclicality across rating systems
Sian Inch:
Managing Director, QA Credit Risk, Barclays
14.15 – 15.00: Interconnected realities: Markets and geopolitics
Helen Flannery:
Managing Director, Goldman Sachs
Mira Zorkot:
Managing Director, Goldman Sachs
15.00 – 15.45: Neural Networks for Beta Estimation – A Novel Approach to Pairs Trading
Traditional approaches to estimating beta in finance often involve rigid assumptions and fail to adequately capture beta dynamics, limiting their effectiveness in use cases like hedging. To address these limitations, we have developed a model using neural networks that improves the performance of beta estimation.
This model was evaluated through extensive testing on both synthetic datasets and real market data to allow for a detailed exploration. Our findings indicate that this neural network model significantly outperforms traditional approaches, particularly in scenarios requiring dynamic responsiveness.
Furthermore, we explore how this model can be applied to pairs trading, and its potential implications for strategy development.
This study not only validates the effectiveness of neural networks in beta estimation, but also underscores its potential to enhance hedging and trading strategies.
Cindy Liu:
Quantitative Researcher, Bloomberg
16.15 – 17.00: Time changes, Fourier transforms and the joint calibration to the S&P500/VIX smiles
Abstract
We develop a model based on time changed Lévy processes and study its ability of reproducing the joint S&P500/VIX implied volatility smiles and the VIX futures prices – a problem known in the literature as the `joint calibration problem’.
The model admits semi-analytical characteristic functions for the key quantities, and therefore efficient Fourier based pricing schemes can be deployed.
We focus on a specification of the proposed general setting which uses purely discontinuous processes.
Results from the application to market data show satisfactory performances in solving the joint calibration problem, and therefore demonstrate that the class of affine processes can provide a workable fit.
Joint work with Ernst Eberlein and Gregory Rayee
Laura Ballotta:
Prof. of Mathematical Finance, Bayes Business School (formerly Cass)
17.00 – 17.45: Pathwise Methods and Generative Models for Pricing and Trading
Abstract: The deep hedging framework as well as related deep trading setups have opened new horizons for solving hedging problems under a large variety of models and market conditions. In this setting, generative models and pathwise methods rooted in rough paths have proven to be a powerful tool from several perspectives. At the same time, any model – a traditional stochastic model or a market generator – is at best an approximation of market reality, prone to model-misspecification and estimation errors. In a data-driven setting, especially if sample sizes are limited by constraints, the latter issue becomes even more prevalent, which we demonstrate in examples. This raises the question, how to furnish a modelling setup (for deriving a strategy) with tools that can address the risk of the discrepancy between model and market reality, ideally in a way that is automatically built in the setting. A combination of classical and new tools yields insights into this matter.
Blanka Horvath:
Associate Professor in Mathematical and Computational Finance, University of Oxford
17.45 – 18.30: PANEL: Career Progression:
- Do you think that being a woman is a significant factor in slowing down career progression in QR Financial Services? Is it still hard to make it to the top positions, if so why and what can be done to change the situation? If applicable, discuss about gender diversity issues (discuss numbers, policies, how to address it)
- Discuss female role models in finance and significant achievements
- Maternity leave
- Has Shared Parental Leave (SPL) helped equality in this area?
- How important are the following:
- Promotions/Career opportunities
- Pay gap elimination
- Agile/Flexible working
- Getting the feedback you need (even if you don’t really want it)
- Supporting each other
- Discuss the current career return to work strategies available
- Have you benefited from any such schemes?
- Discuss the Importance and value of mentorship and sponsorship
- What mentoring programs are available for juniors if any?
Moderator:
Laura Ballotta:
Prof. of Mathematical Finance, Bayes Business School (formerly Cass)
Mirela Predescu:
Head of RISK Quant Academy, BNP Paribas
Amira Akkari:
Executive Director, JP Morgan
Maria Paraskevopoulou:
VP, QA Climate Risk, Barclays
Mira Zorkot:
Managing Director, Goldman Sachs