Mauro Cesa
Quant finance editor
Mauro Cesa is quantitative finance editor for Risk.net, based in London. He leads the team responsible for the publication of quantitative research across all brands of the division.
The section of Risk.net he manages, Cutting Edge, publishes peer-reviewed papers on derivatives, asset and risk management, and commodities.
Mauro holds a degree in economics from the University of Trieste and a masters in quant finance from the University of Brescia.
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Articles by Mauro Cesa
Deep hedging strays when volatility gets rough – study
In the most realistic simulations, data-driven approach fared 30% worse than conventional hedging
Podcast: Richard Martin on improving credit migration models
Star quant proposes a new model for predicting changes in bond ratings
Put options power up variable annuities
Insurance quants increase risk-adjusted profits using novel hedging technique
Putting the H in XVAs
Barclays quant proposes methodology for factoring hedging costs into derivatives valuations
Setting boundaries for neural networks
Quants unveil new technique for controlling extrapolation by neural networks
A guiding light for corporates lost in the fog of XVAs
Chris Kenyon proposes a framework for optimising XVAs – from the client perspective
Podcast: Matthias Arnsdorf on a new – and cheaper – KVA
Quant proposes approach anchored by a dealer’s default rate rather than its return on equity
Breaking barriers in options pricing
A new technique for pricing exotic options unifies two classic models
Danske quants discover speedier way to crunch XVAs
Differential machine learning produces results “thousands of times faster and with similar accuracy”
Time to move on from mean-variance diversification
A new diversification measure appears to produce better results than mean-variance optimisation
Podcast: CFM’s Bouchaud on agent-based models and ESG investing
Hedge fund quant, and Risk.net’s new columnist, shares his unique take on markets
Podcast: Dario Villani on managing money with ML
Duality’s CEO discusses key to machine learning success, and the influence of Renaissance’s Jim Simons
Spotting co-movement breakdowns with neural networks
Autoencoders can detect changes in relationship between assets in real time
Podcast: Lipton and de Prado on Covid and trading strategies
Top quants discuss collaboration and their worries about the economic recovery
A tale of two (or three, or four) models
Performance measure based on quality of replicating portfolios outperforms ‘P&L explain’, new paper claims
Podcast: Kaminski and Ronn on negative oil and options pricing
The market is gravitating to the Bachelier model as an alternative to Black 76
Solving the enigma of the volatility smiles
Has the problem of jointly calibrating the volatility smiles of the Vix and S&P 500 been solved?
To model the real world, quants turn to synthetic data
Future financial models will be built using artificially generated data
Three adjustments in calibrating models with neural networks
New research addresses fundamental issues with ANN approximation of pricing models
Podcast: Horvath and Lee on market generator models
Quants explain the application of the latest techniques
Outsmarting counterparty risk with smart contracts
A digital transaction system developed by quants at DZ Bank could slash margin costs for derivatives
No silver bullet for AI explainability
No single approach to interpreting a neural network’s outputs is perfect, so it’s better to use them all
Podcast: Kondratyev and Schwarz on generating data
Market generator models may aid areas of finance where data is limited or sensitive