Preconference Day: Summits & Workshops - GMT (Greenwich Mean Time, GMTZ)
Preconference Day: Summits & Workshops - GMT (Greenwich Mean Time, GMTZ)
Perspectives from both fund managers and fund investors.
How is the post-MiFID II era shaping up the landscape?
How to manage large tick data sets in trading.
- Revant Nayar - Chief Investment Officer, Dilaton Technologies LLC Family Office
Perspectives on allocation strategy.
Historically, factors such as value, growth, or momentum have proven effective in generating alpha. The session constructs eight sustainable/transition factors like green minus brown emissions, strong minus weak Climate VaRs, low minus high temperature targets, long minus short biodiversity. It evaluates their financial and sustainable performance alongside their style exposure to traditional factors. The key conclusion is that traditional factors can be regarded as equivalent to sustainable and transition factors, exhibiting relatively orthogonal style exposures, thereby presenting a compelling opportunity for factor integration.
- Heiko Bailer - ESG Investments & Research, LBBW Asset Management
- Jose Canals-Cerda - Senior Special Advisor, Supervision, Regulation and Credit, Federal Reserve Bank of Philadelphia
How has the use of AI in quantitative solutions developed in the past year? How are businesses using different solutions (LLMs vs GenAI) across the industry? What are the key standouts?
In line with regulatory compliance and reputational risk, what are the ethical considerations of LLMs in finance and to what extent is this a factor in quantitative finance?
From the EU’s AI act onwards, what are the regulatory considerations that individuals must take.
This paper introduces a new quantitative toolkit for (reverse) stress testing of the Banking Book -- as required by EBA IRRBB regulations and the Basel Framework. Our toolkit combines classic yield curve modelling and valuation tools with classical Machine Learning clustering techniques to systematically identify blind spots in a bank’s balance sheet. I illustrate the model’s use by applying it to realistic balance sheets of two hypothetical banks and draw implications for risk management and policy making.
- Eric Schaanning - Group Head of Market and Valuation Risk Management, Nordea
How can this be applied in investment signal generation?
The benefits of a Chat GPT-like system being used.
The latest techniques and uses of machine learning in XVA and credit risk.
- News data sourcing and processing.
- Prompt-based investment signals.
- LLM-based factor reduction to identify successful investment themes.
- Comparison to standard investment factors.
- Daniel Mayenberger - Head of Quants Risk as a Service Platform - Digital Products, J.P. Morgan
Maximise potential of pricing methodologies.
Examining the cross over between LLMs and volatility.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
An overview of deep neural networks, activation functions, loss functions and training algorithms. Practical implementation in Pytorch.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Unsupervised hedging and pricing of derivatives, high-frequency asset return prediction.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Deep learning volatility and calibration of (rough) stochastic volatility models.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Market generators and generative AI.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- Shape of the volatility surface
- Scaling of implied volatility smiles
- Monofractal scaling of realized variance
- Estimation of H
- Realized variance forecasting
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- The forward variance curve
- Change of measure
- The rough Bergomi model
- The rough Heston model
- The quadratic rough Heston model
- Financial meaning of parameters
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- Affine forward intensity models
- Affine forward volatility models
- Diamonds and the exponentiation theorem
- The leverage swap
- Moment computations
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- Rational approximation of rough Heston
- The HQE scheme
- Parameter sensitivities
- Smile fitting
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY