6 papers
Joint deep calibration of the 4-factor PDV model
Fabio Baschetti, Giacomo Bormetti, Pietro Rossi
Joint calibration to SPX and VIX market data is a delicate task that requires sophisticated modeling and incurs significant computational costs. The latter is especially true when…
Defaultable bond liquidity spread estimation: an option-based approach
Pietro Rossi, Paolo Spezzati, Riccardo Tedeschi
This paper extends an option-theoretic approach to estimate liquidity spreads for corporate bonds. Inspired by Longstaff's equity market framework and subsequent work by Koziol and…
Deep calibration with random grids
Fabio Baschetti, Giacomo Bormetti, Pietro Rossi
We propose a neural network-based approach to calibrating stochastic volatility models, which combines the pioneering grid approach by Horvath et al. (2021) with the pointwise two-…
The SINC way: A fast and accurate approach to Fourier pricing
Fabio Baschetti, Giacomo Bormetti, Silvia Romagnoli +1
The goal of this paper is to investigate the method outlined by one of us (PR) in Cherubini et al. (2009) to compute option prices. We name it the SINC approach. While the COS meth…
Deep learning Profit & Loss
Pietro Rossi, Flavio Cocco, Giacomo Bormetti
Building the future profit and loss (P&L) distribution of a portfolio holding, among other assets, highly non-linear and path-dependent derivatives is a challenging task. We provid…
Portfolio Selection with Probabilistic Utility, Bayesian Statistics and Markov Chain Monte Carlo
P. Rossi, M. Tavoni, F. Cocco +1
We propose a novel portfolio selection approach that manages to ease some of the problems that characterise standard expected utility maximisation. The optimal portfolio is no long…