Main Conference Day Two - GMT (Greenwich Mean Time, GMTZ)
Unprecedented data management challenges are taking over quant teams. With a focus on infrastructure, processes, and technologies, how do teams effectively catalogue, clean, normalise, and distribute diverse datasets across organisations?
- Stas Melnikov - Head of Quantitative Research and Risk Data Solutions, SAS
Exclusive guest speaker address
A funny thing that happened on the way to the model
- Matt Parker - Mathematician, award-winning stand-up comedian, YouTuber, and a New York Times best-selling author, Matt Parker
- Mahdi Anvari - Head of Equity Derivatives Quantitative Analysis, Millennium
- Stability and accuracy of fd solution: myths, facts and orthodoxy
- Discrete consistency: backwards, forwards, dupire and monte carlo
- Dividends, stochastic volatility, jumps
- Monte-carlo simulation, likelihood ratio tricks and adjoint differentiation
- Jesper Andreasen - Head of Quantitative Analytics, Verition Fund Management
- Alexander Sokol - Executive Chairman, CompatibL
How containers enable real-time speed
Breaking latency with modular design
- Richard Turner - Managing Director, Currency Management, Mesirow Financial
From edge computing to execution
Portfolio allocation is crucial for investment institutions with diverse market functions and strategies. Allocations can be static, based on long-term expectations, or dynamic, incorporating time-varying stochastic factors. A typical dynamic allocation goal consists of an expectation of a deterministic function of the portfolio wealth at a fixed time-horizon. Usually, the portfolio weights depend on prices, wealth, and a time to maturity.
Our paper explores a novel optimization framework without fixed time horizons, focusing on portfolio returns rather than wealth – an approach particularly relevant to sovereign and pension funds. We adopt a comprehensive stationary approach across utility (regularized running forward-looking returns), model (asset returns driven by stationary market factors like sentiments and VIX), and weights (functions of market drivers without explicit time-dependence).
We derived optimal equations for asset weights with arbitrary utility functions, potentially solvable numerically. Additionally, we developed an efficient convex optimization routine for a special case of concave utilities. Morever, for quadratic utilities, we found out that the optimal weights satisfy a matrix Fredholm equation and develop a highly efficient numerical solution. Finally, we obtained explicit analytics for the Kim-Omberg model solving the Fredholm equation. We have confirmed our findings via numerous numerical experiments: we calibrated the Kim-Omberg model using market sentiment as the stochastic drift factor and holistically analyzed analytical and numerical results.
- Alexandre Antonov - Quanitative Research and Development Lead, ADIA
Join us for an exclusive wellness session in Peninsula East. Rest, relax, recharge, and reset to absorb more in the upcoming sessions.
- Ranbir Toor - Founder, Elevate City
- Andrey Itkin - Adjunct Professor, New York University
Black Scholes, local volatility, and stochastic models
From prototype to production safely
- Youssef Elouerkhaoui - Managing Director, Global Head of Markets Quantitative Analysis, Citigroup
Interpreting models under Basel III
Applying annealing in non-convex spaces
In this talk we shall discuss an approach to forecasting inflation in both DM and EM using machine learning models, contrasting it to more traditional approaches, alongside a discussion of the types of data required. We shall also be discussing how such an approach has performed historically. Later, we discuss ways we can utilise such inflation forecasts within the systematic trading strategies for macro assets such as FX, and how they have performed in a live environment.
- Saeed Amen - Cofounder, Turnleaf Analytics
Extracting alpha from analyst tone
Traditional diversification metrics work well in calm markets but become misleading during market stress. During extreme events, correlations that appear stable at 0.3 can suddenly spike to 0.9, leaving portfolios more concentrated than expected. This session explores why diversification breaks down during stress periods and provides practical techniques for identifying hidden factor concentrations, building more robust diversification measures, and stress-testing portfolio resilience.
- Samar Gad - Director of MBA, Kingston Business School
Volatility clustering meets capital thresholds
- Fabrizio Anfuso - Senior Technical Specialist, Bank of England
Financial Finance objectives are applied to the investment problem. The investment problem (IP) asks for the determination of the amount to be invested in risky assets. Convex cones of random outcomes define the set of arbitrarily acceptable risks for which the IP has no solution. For risky outcomes that are not arbitrarily acceptable, the acceptable risks are defined by a convex set containing the arbitrarily acceptable risks. For such risks the investment problem is solved using Disciplined Saddle Point (DSP) programming. Risks with a negative expectation under test probabilities remain acceptable at a larger scale provided the expected losses are bounded by rebate levels associated with the test probability. The economic values of random outcomes are given by the amount that can be withdrawn while yet maintaining risk acceptability. The IP solution maximizes this economic value. Solutions are developed and implemented into trading strategies in both univariate and multivariate contexts. The methods have also been applied to determine value maximizing investments in the options
- Dilip Madan - Professor of Mathematical Finance, University of Maryland
- Gabriel Tucci - Global Head of Equities Cash Quant Trading, Citi
- Mahdi Anvari - Head of Equity Derivatives Quantitative Analysis, Millennium
Latency, memory, and compilation trade-offs
- Mihail Turlakov - Quant Trader, Independent
The inflationary environment of 2022 reminded investors that a standard 60/40 portfolio of stocks and bonds that produced double-digit losses was not immune to market turbulence. Experienced investors recognize that crisis periods are challenging to predict and their impact on performance can be devastating since most asset classes and investment strategies struggle during crises. A notable exception is the trend-following strategy used by Commodity Trading Advisors, which is often described as "crisis alpha" due to its propensity to perform well during crisis periods. While investors may allocate to CTAs for various reasons, including strong long-term performance and a low correlation to most asset classes and strategies, crisis alpha is a particularly attractive characteristic of CTA investments. Thus, when crises do occur, CTA investors expect to see strong performance and benefit from crisis alpha. What some investors may not have considered is how sensitive the crisis alpha benefits are to manager selection and to the number of managers in a portfolio. This presentation summarizes our recent research, which investigates the impact of manager selection and the number of managers in CTA portfolios on crisis alpha.
- Marat Molyboga - Chief Risk Officer, Director of Research, Efficient Capital Management
Adjusting exposure to volatility shocks
- Richard Turner - Managing Director, Currency Management, Mesirow Financial
1. A new kind around the block in insurance finance.
2. Market overview and opportunities
3. Modeling solvency via equity
- Robert van Kleeck - Managing Director, Head of Credit Portfolio Management, Assenagon Asset Management
- Thousands of pricing models for pricing a derivative
- Model risk, calibration risk, parameters risk, implementation risk, data risk, …
- Conic finance as a way out
- Wim Schoutens - Professor Of Financial Engineering, University of Leuven
- Gregory van Kruijsdijk - PhD Student, KU Leuven