As we look ahead to 2023, we take a lot of lessons with us from the previous year. The QuantMinds community has shared multiple breakthroughs in volatility modelling, and we are learning more and more about digital and crypto assets every day. Many long-standing challenges are tackled with innovative solutions, so don’t miss out! Here’s our recap of 2022.
Are you ready for QuantMinds International 2022?
Although we should now be looking forward to QuantMinds International 2023, this handy guide is home to many must-reads and must-sees. Under the threat of economic crisis around the world, forecasting inflation will be key, and Saeed Amen shares how it’s done at Turnleaf Analytics. Plus, Sebastian Cassel, Head of Valuation Model Risk, BNP Paribas, shows us how to apply tensor networks to speed up pricing and Carol Alexander, Professor of Finance and lead of the Quantitative Fintech research group in the Business School, University of Sussex, presents the latest on hedging Bitcoin options and modelling implied volatilities. Also, Alexander Lipton, Global Head - Quantitative Research & Development at the Abu Dhabi Investment Authority (ADIA), and Marcos Lopez de Prado, Global Head - Quantitative Research & Development at the Abu Dhabi Investment Authority (ADIA), give us their thoughts on scientific investing and the challenge of applying it in the world of empirical and anecdotal evidence. Flip through here!
Remembering Peter Carr
Last year, the quant finance community lost a key member. Peter Carr was a highly valued supporter and contributor of the QuantMinds community, too, and his loss was certainly felt at QuantMinds International 2022. His work and his mark on the quant finance community, however, lives on. In honour of Carr’s legacy, Sebastien Bossu, Professor of Finance, Worcester Polytechnic Institute, expounded the Carr-Madan spanning formula in connection with related results, and presented recent generalizations obtained under Carr’s broad guidance, with particular focus on multi-asset European options – you can read about it here.
To the future of QuantMinds and beyond
Our mid-year eMagazine contains some exciting food for thought. J.D. Opdyke, Chief Analytics Officer, Sachs Capital Group Asset Management, LLC, examines the methodological challenges of correlation matrices in practice and proposes a way to address 3 key obstacles. Paul Edge and David Seelmann, Corporate Risk Management, EDP, show us what a decentralised, enterprise wide cashflow risk model looks like. Plus, we’ve got updates from Marcos Costa Santos Carreira, PhD Candidate, École Polytechnique, who shares the latest on volatility modelling, and from Jesus Rodriguez, Chief Technology Officer, IntoTheBlock, who explores the quantitative developments in cryptocurrencies and digital assets. Finally, we get a closer inspection of sustainable investments from Dr Svetlana Borovkova, Probability & Partners and Vrije University Amsterdam, and Aymeric Kalife, CEO at iDigital Partners and Adjunct Professor at Paris Dauphine University – PSL, looks at reconciling sustainability, market performance, and customer risk appetite through an ESG risk based optimal asset allocation. Read them all here.
Towards non-equilibrium and non-perturbative finance
“All complex systems in the natural sciences are systems with non-linear dynamics”, Igor Halperin, VP, AI Asset Management, Fidelity Investments, reminds us, and similar to other complex systems, we can observe non-linearity in market dynamics. However, models used by practitioners in quantitative finance are linear models and strategies such as the perturbation theory methods can be applied to account for non-linear effects. Is this approach good enough? Halperin explores the possible solutions.
What drives Bitcoin volatility?
Carol Alexander, Professor of Finance and lead of the Quantitative Fintech research group in the Business School at the University of Sussex, draws on recent events like the attack on the Terra/Luna algorithmic stablecoin and the liquidation of large bitcoin and ether option positions held by Three Arrows Capital on the Deribit exchange in order to find an answer to this question. Read now.
Volatility is (mostly) path-dependent
While Alexander examines what drives Bitcoin volatility, Julien Guyon, professor of applied mathematics, Ecole des Ponts ParisTech, explores what drives volatility at all!
“It has been recently observed by several authors including Zumbach, Chicheportiche, Bouchaud, Blanc, Donier and myself, that financial markets exhibit a clear pattern of path-dependent volatility (PDV)”, Guyon writes. “The implied volatility and future realized volatility depend on the path followed by the asset price in the recent past.”
But how much of volatility is path dependent and how does it depend on past asset returns? Guyon says volatility is “mostly endogenous” and here’s why.
Derivatives on crypto assets in decentralized finance (DeFi) space
Did you miss this presentation from Artur Sepp, Head Systematic Solutions and Portfolio Construction, Sygnum Bank's Asset Management, at QuantMinds Edge: Alpha & Quant investing? Fear not, we’ve got the full session covered:
Find out more about:
- Liquid crypto derivatives in DeFi
- Comparison with traditional finance: pricing by replication vs supply/demand equilibrium
- Examples: perpetual swaps and futures, vanilla and inverse options, perpetual power futures, forward-starting straddles
- Quantitative approach for arbitrage-free valuation and replication on crypto assets
Singular exotic perturbation
In this paper, Adil Reghai, former Head of Quantitative Research Equity & Commodity Markets, Natixis and Florian Monciaud, Quant Researcher - Equity Derivatives, Natixis, introduce a new methodology that combines Singular perturbation analysis and exotic greek computation. They obtain asymptotic formulae for the LSV impact which work extremely well. Tests are performed on the mostly traded Autocalls in the equity derivatives business. Read the paper here.
Market risk: Optimal VaR adaptation & machine learning
Changing volatilities are constant companions in financial markets time series. Nevertheless, regulatory risk models usually rely on some kind of stationarity assumption that leads to problems in these transient environments. Practitioners have long found some ad hoc solutions for this problem, but could machine learning lead to sustainable improvement? Peter Quell, Head of Portfolio Modelling for Market & Credit Risk, DZ Bank, explores the application of ML methods in a regulatory context.
In conversation with Marcos Lopez de Prado and Alexander Lipton
We caught up with not one, but two leading quant researchers, and found out about the most important topics that quants need to address going forward. Watch their interviews here.