1 code implementation • 19 Jun 2023 • 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-stage calibration of Bayer et al. (2018) and Liu et al. (2019).
no code implementations • 12 Jul 2021 • Giacomo Bormetti, Fulvio Corsi
We propose an observation-driven time-varying SVAR model where, in agreement with the Lucas Critique, structural shocks drive both the evolution of the macro variables and the dynamics of the VAR parameters.
1 code implementation • 1 Sep 2020 • Fabio Baschetti, Giacomo Bormetti, Silvia Romagnoli, Pietro Rossi
We provide several results which were missing in the early derivation: i) a rigorous proof of the convergence of the SINC formula to the correct option price when the support grows and the number of Fourier frequencies increases; ii) ready to implement formulas for put, Cash-or-Nothing, and Asset-or-Nothing options; iii) a systematic comparison with the COS formula for several log-price models; iv) a numerical challenge against alternative Fast Fourier specifications, such as Carr and Madan (1999) and Lewis (2000); v) an extensive pricing exercise under the rough Heston model of Jaisson and Rosenbaum (2015); vi) formulas to evaluate numerically the moments of a truncated density.
no code implementations • 17 Jun 2020 • Pietro Rossi, Flavio Cocco, Giacomo Bormetti
Neural networks with many outputs can interpolate every single assets in the portfolio generated by a single Monte Carlo simulation.
no code implementations • 3 Oct 2019 • Danilo Vassallo, Giacomo Bormetti, Fabrizio Lillo
We propose a novel approach to sentiment data filtering for a portfolio of assets.
