Mauro Cesa

1 week ago | risk.net | Mauro Cesa

The generation of synthetic market data is widely seen as one of the most promising applications of sophisticated artificial intelligence models, such as generative adversarial networks (GANs) and autoencoders. A new paper from Jörg Kienitz, director of quantitative methods at consultancy m|rig, suggests these new models still have some way to go to beat the old ways.

1 month ago | centralbanking.com | Mauro Cesa

When the economist and geopolitical strategist Brunello Rosa was finalising drafts of his book Smart Money last year, he noted presciently that critical events might unfold before its publication in paperback. The book, written with co-author Casey Larsen, envisages a new cold war unfolding between the US and China in which “digital de-dollarisation” plays a central role. The paperback is due out in June. In the meantime, the fallout from the Trump administration’s tariff announcements in early

1 month ago | risk.net | Mauro Cesa

When the economist and geopolitical strategist Brunello Rosa was finalising drafts of his book Smart Money last year, he noted presciently that critical events might unfold before its publication in paperback. The book, written with co-author Casey Larsen, envisages a new cold war unfolding between the US and China in which "digital de-dollarisation" plays a central role. The paperback is due out in June.

1 month ago | risk.net | Mauro Cesa

The parameters of popular factor models for interest rates, such as the Heath-Jarrow-Morton model, are known to be mean-reverting. The study of the speed at which the mean reversion happens has been largely neglected, but that information would be helpful for structuring more efficient hedging strategies. More broadly, fresh research on interest rate modelling could be useful for banks and investors as they grapple with volatile rates during a period of macroeconomic uncertainty.

2 months ago | risk.net | Mauro Cesa

A top quant has cast doubt on one of the hottest new practices in investing - the use of synthesised data to train machine learning models and to backtest strategies. In a new working paper, Charles-Albert Lehalle, a professor in applied math at École Polytechnique, together with Adil Rengim Cetingoz, a researcher at Université Paris 1 Panthéon-Sorbonne, shows that synthetic data cannot reduce uncertainty in models - no matter how much of the new data quants might create.