Quant Insights Conference:
Portfolio Management in Quant Finance

12th March 2025
12:00 - 18:00 GMT
Globally Live-Online

Brought to you by
CQF Institute, Fitch Learning, and Wilmott

Days

1 Day

Speakers

7 Talks

Price

Free Tickets

Post Conference

Video

About the Conference

Join us for the highly anticipated return of the Portfolio Management in Quant Finance Conference on March 12, 2025, featuring an exciting lineup of groundbreaking discussions on the latest industry advancements.

Don't miss the opportunity to engage with industry leaders as they explore topics such as drawdown risk mitigation through machine learning, timescale separation in financial data, cryptocurrency jump risk premia, quantitative asset allocation strategies, and the transformative applications of AAD in finance.

Tickets are free, offering you the chance to earn valuable CPD credits and gain exclusive access to video on demand post-conference.

Book your free ticket

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Conference Schedule

12th March 2025

12:00 - 12:30 GMT

Virtual conference doors open - Familiarize yourself with the event platform

12:30 - 12:35 GMT

Welcome Remarks
Dr. Randeep Gug, Managing Director, CQF and CQF Institute

12:35 - 13:10 GMT

Fast Times, Slow Times and Timescale Separation in Financial Timeseries Data
Dr. Jan Rosenzweig, Portfolio Manager, Pine Tree Market Neutral

13:15 - 13:50 GMT

Jump Risk Premia in the Presence of Clustered Jumps
Professor Natalie Packham, Professor of Mathematics and Statistics, Berlin School of Economics and Law

13:55 - 14:30 GMT

Quantitative Asset Allocation at a Swiss Insurer: Insights from Three Years
Claus Huber, Head of Quantitative Modelling & Analytics, Helvetia Insurance Group

14:30 - 15:00 GMT

Break and Networking

15:00 - 15:35 GMT

AAD Applications as a Game Changer for Finance
Adil Reghai, Quantitative Research and Development Lead, ADIA

15:40 - 16:15 GMT

Canonical Portfolios: Optimal Asset and Signal Combinations
Dr. Nick Firoozye, Senior Researcher, TradeLink Holdings LLC

16:15 - 16:35 GMT

Break and Networking

16:35 - 17:10 GMT

Drawdown Mitigation via Identification and Prediction Using Machine Learning
Sebastian Petric, Head of FX, Gold, and Geopolitical Strategy, LGT Private Banking

17:10 - 17:15 GMT

Closing Remarks
Dr. Randeep Gug, Managing Director, CQF Institute

Conference Speakers

Confirmed speakers and panelists. Click on the images below to explore abstracts and biographies.

Dr. Jan Rosenzweig
Portfolio Manager
Pine Tree Market Neutral

Abstract Biography

Fast Times, Slow Times and Timescale Separation in Financial Timeseries Data

Quantitative strategies rely on stationarity of the underlying processes. Parameter drift eventually kills all strategies, how ever brilliant they may be. This talk will explore some methods for unmixing financial timeseries using intrinsic relaxation timescales, recovering any stationary processes, but also allowing us to separate “slow” from “fast” processes. This has various interesting implications for trading and portfolio allocation, which we will briefly discuss.

Dr. Jan Rosenzweig, Portfolio Manager, Pine Tree Market Neutral

Jan has been working in the financial markets for close to 20 years, as a quant, structurer, trader and portfolio manager. He worked at Credit Suisse, Rabobank, HSH Nordbank, IV Capital, Brancherose and Pine Tree. He has a PhD in applied maths from Cambridge and BSc in applied maths and computer science from Zagreb. Jan is based in London.

Professor Natalie Packham
Professor of Mathematics and Statistics
Berlin School of Economics and Law

Abstract Biography

Jump Risk Premia in the Presence of Clustered Jumps

We introduce an option pricing model with clusters of jumps by incorporating a bivariate Hawkes process with exponential decay memory kernel. The Hawkes process characterises the self- and cross-excitement of positive jumps and negative jumps, allowing the model to effectively capture the volatile price dynamics, jump arrival times and implied volatility smiles often observed in cryptocurrencies such as Bitcoin (BTC). We derive jump risk premia for positive and negative jumps, defined as the discrepancies in jump measures between the objective measure and the risk-neutral measure. Our findings reveal that these jump risk premia: (i) provide insights on how the BTC options market reacted to major events, such as the COVID-19 outbreak and the FTX scandal; (ii) possess significant predictive power for delta-hedged option returns; and (iii) are indicators in explaining the volatile cost-of-carry implied from BTC futures prices.

Joint work with Francis Liu and Artur Sepp

Professor Natalie Packham, Professor of Mathematics and Statistics, Berlin School of Economics and Law

Natalie Packham is Professor of Mathematics and Statistics at Berlin School of Economics and Law and Principal Researcher within the International Research Training Group “High Dimensional Nonstationary Time Series” (IRTG 1792) at Humboldt University Berlin. Natalie has several years of industry experience as a front office software engineer at an investment bank, and is frequently involved in industry-related research and consulting projects. Her research expertise includes Mathematical Finance, Financial Risk Management and Computational Finance, and her academic work has been published in Mathematical Finance, Finance & Stochastics, Quantitative Finance, Journal of Applied Probability and many other academic journals. She is associate editor of “Quantitative Finance”, “Methodology and Computing in Applied Probability” and “Digital Finance” and co-chair of the GARP Research Fellowship Advisory Board. Natalie holds an M.Sc. in Computer Science from the University of Bonn, a Master’s degree in Banking & Finance from Frankfurt School, and a Ph.D. in Quantitative Finance from Frankfurt School.

Dr. Nick Firoozye
Senior Researcher
Tradelink Worldwide Ltd

Abstract Biography

Canonical Portfolios: Optimal Asset and Signal Combinations

I will present a novel framework for analyzing the optimal asset and signal combination problem. The approach builds on the dynamic portfolio selection problem introduced by Brandt and Santa-Clara (2006) among others. We first reformulate their original investment problem into a tractable one that allows us to derive a closed-form expression for the optimal portfolio policy that is scalable to large cross-sectional financial applications. We then recast the problem of selecting a portfolio of correlated assets and signals into selecting a set of uncorrelated managed portfolios via Canonical Correlation Analysis (Hotelling, 1936). The new investment environment, where we consider the most forecastable portfolio and the second-most forecastable orthogonal portfolio, etc. This decomposition offers unique economic insights into the joint correlation structure of our optimal portfolio policy. We also operationalize our theoretical framework to bridge the gap between theory and practice, showcasing the improved performance of our proposed method over natural competing benchmarks.

Dr. Nick Firoozye, Senior Researcher, Tradelink Worldwide Ltd

Dr. Nick Firoozye has over 20 years of experience in finance, in both buy and sell-side firms, including Lehman, Goldman, Deutsche Bank, Nomura, Sanford Bernstein, and Citadel, in research, structuring, and trading. He started finance in MBS/ABS, then EM Research, Credit, Asset Allocation, Rates Macro, RV Strategy and Trading, Distressed Debt Trading, and Vol Strategy, before moving into fully Systematic Trading in 2013. He is currently a Senior Researcher at Tradelink Worldwide Ltd, a proprietary trading firm. He is an Honorary Professor in Computer Science at University College London, focusing on Online Learning, RL, ML, and Statistics in Financial Trading. Nick has had 6 PhD students, four completed, with several working in Algo Trading roles. He co-authored a book, Managing Uncertainty, Mitigating Risk, about the role of uncertainty in finance after the Eurozone Crisis. Nick taught Algorithmic Trading Strategies, a PhD reading course in 2016 and has adapted it for an online course, creating an MSc course which he taught since 2018 to over 600 students. He is currently co-authoring a book on Algorithmic Trading. Nick got his PhD at Courant Institute, NYU, did postdocs at Univ Minn, Heriot-Watt, Bonn, NYU, and was an Asst Prof at University of Illinois, before leaving for Wall Street.

Claus Huber
Head of Quantitative Modelling & Analytics
Helvetia Insurance Group

Abstract Biography

Quantitative Asset Allocation at a Swiss Insurer: Insights from Three Years

Three years ago, Helvetia Insurances' Asset Management decided to build its own tool for Strategic Asset Allocation called Sally, a bespoke implementation of the model suggested by Black/Litterman (1992). During the three years that we have worked with Sally in a production environment, we have addressed quite a few use cases and encountered, and solved, several problems — for example, how to build risk factor models for illiquid asset classes, such as real estate. This talk discusses a few of those issues and our solutions, for example, how to define and measure key performance indicators, how to denoise the covariance matrix, or how we can use the implied equilibrium returns to find indicative expected returns for asset classes with sparse data.

Claus Huber, Head of Quantitative Modelling & Analytics, Helvetia Insurance Group

Claus is the Head of Quantitative Modelling & Analytics at Helvetia Insurances in Basel / Switzerland, where his team develops digital tools for Strategic and Tactical Asset Allocation, risk budgeting, overlay management, manager selection, visual representation of complex data structures, and a few more. In previous roles he developed new investment products for Quantitative Multi Asset Funds and, as Head of Digital Transformation, drove the development of new tools and data products that allow smart data usage and sharing, and advised clients on risk management and quantitative investment solutions. Claus has extensive experience as entrepreneur, risk manager, credit strategist, hedge fund analyst, and government bond trader and has worked for hedge funds, banks, and insurance companies.

Adil Reghai
Quantitative Research and Development Lead
ADIA

Abstract Biography

AAD Applications as a Game Changer for Finance

AAD (Algorithmic Adjoint Differentiation) is a powerful yet simple tool for AI and financial applications. This talk will explore the fundamentals of this technology and highlight its direct and indirect applications, which are truly groundbreaking.

Adil Reghai, Quantitative Research and Development Lead, ADIA

Adil Reghai is a renowned expert in quantitative finance and artificial intelligence. With a strong academic background in mathematics and finance, he has held prominent roles in the financial industry, including positions at major banks and financial institutions. Adil is widely recognized for his contributions to the development and application of advanced mathematical techniques, such as Algorithmic Adjoint Differentiation (AAD), in solving complex financial problems. His work bridges the gap between cutting-edge technology and practical financial applications, making him a sought-after speaker and thought leader in the field. Adil is also an author and educator, sharing his expertise through publications, lectures, and workshops, inspiring the next generation of quants and AI practitioners.

Sebastian Petric
Head of FX, Gold, and Geopolitical Strategy
LGT Private Banking

Abstract Biography

Drawdown Mitigation via Identification and Prediction Using Machine Learning

Risk-off periods in financial markets can have considerable impacts on portfolios. This talk focuses on drawdown mitigation in equity index investing using a data-driven machine learning approach. The talk proposes a return-based methodology that identifies periods of heightened risk, and deal with these episodes using clustering, ridge regression, and sequential feature selection. The latter captures varying degrees of severity, providing greater nuance than a simple binary classification. In out-of-sample tests, signals based on modelling help limit exposure during unfavourable market phases and improve risk-adjusted returns relative to a buy-and-hold benchmark. These results underscore the role of systematic, data-driven techniques in mitigating drawdown risk for investors.

Sebastian Petric, Head of FX, Gold, and Geopolitical Strategy, LGT Private Banking

Sebastian Petric began his career in emerging market asset management, which heightened his awareness of the diverse factors influencing returns and risks. This experience guided him towards multi-asset class investing and capital market research, where he developed a passion for data science and machine learning. He carries this out specifically at the University of Liechtenstein. As a future-focused leader and a Fellow at the Oxford Internet Institute, he is equipped with extensive knowledge and expertise in innovative technologies. He strives to make a difference through his roles in advisory and advocacy, and he aims to influence through knowledge sharing via his publications.

Dr. Randeep Gug
Managing Director
CQF and CQF Institute

Biography

Dr. Randeep Gug, Managing Director, CQF and CQF Institute

Dr. Randeep Gug is the Managing Director of the CQF Institute and the Certificate in Quantitative Finance (CQF). Prior to joining Fitch Learning, Randeep worked in a variety of roles. He spent five years working in the Equities division at Salomon Smith Barney and later traded futures and options on the Indian National Stock Exchange (NSE). More recently he has spent time teaching mathematics at all levels. He is a qualified teacher, holds a 1st class honours degree and a PhD for research in semiconductor physics. He is a CQF Alumnus, achieving a distinction on the programme and his current interests are based around improving and promoting the teaching and learning of Quant Finance.

Conference Tickets

Date

Wednesday 12th March 2025

Time

12:00 - 18:00 GMT

Book your free ticket

Conference Organizers

Quant Insights is presented by the CQF Institute, Fitch Learning, & Wilmott

Promoting the highest standard in practical financial engineering, the CQF Institute, part of Fitch Learning, is a global membership organization dedicated to educating and building the quantitative finance community. The CQF Institute is also the awarding body for the Certificate in Quantitative Finance (CQF) the world’s largest professional qualification in quantitative finance.

Part of the Fitch Group, Fitch Learning partners with businesses to help develop the future leaders of the financial services industry. Alongside centers in established financial hubs, Fitch Learning utilizes a best-in-class technology platform to deliver blended learning solutions that maintain the personal element of development.

Wilmott is the leading resource for the quant finance community, comprised a website and discussion forum and Wilmott magazine.

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Quant Insights Conference:Portfolio Management in Quant Finance

About the Conference

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Conference Schedule

Conference Speakers

Fast Times, Slow Times and Timescale Separation in Financial Timeseries Data

Dr. Jan Rosenzweig, Portfolio Manager, Pine Tree Market Neutral

Jump Risk Premia in the Presence of Clustered Jumps

Professor Natalie Packham, Professor of Mathematics and Statistics, Berlin School of Economics and Law

Canonical Portfolios: Optimal Asset and Signal Combinations

Dr. Nick Firoozye, Senior Researcher, Tradelink Worldwide Ltd

Quantitative Asset Allocation at a Swiss Insurer: Insights from Three Years

Claus Huber, Head of Quantitative Modelling & Analytics, Helvetia Insurance Group

AAD Applications as a Game Changer for Finance

Adil Reghai, Quantitative Research and Development Lead, ADIA

Drawdown Mitigation via Identification and Prediction Using Machine Learning

Sebastian Petric, Head of FX, Gold, and Geopolitical Strategy, LGT Private Banking

Dr. Randeep Gug, Managing Director, CQF and CQF Institute

Conference Tickets

Conference Organizers

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Quant Insights Conference:
Portfolio Management in Quant Finance