28 citations · 95 across the 25 of their papers we have counts for
33 papers
Filtering and Smoothing with Score-Driven Models
Giuseppe Buccheri, Giacomo Bormetti, Fulvio Corsi +1
Score-driven models are, by construction, purely predictive filters. When such models are read as filters for an underlying latent process, rather than as data generating processes…
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…
Pricing Carbon Allowance Options on Futures: Insights from High-Frequency Data
Simone Serafini, Giacomo Bormetti
Leveraging a unique dataset of carbon futures option prices traded on the ICE market from December 2015 until December 2020, we present the results from an unprecedented calibratio…
Why is the estimation of metaorder impact with public market data so challenging?
Manuel Naviglio, Giacomo Bormetti, Francesco Campigli +2
Estimating market impact and transaction costs of large trades (metaorders) is a very important topic in finance. However, using models of price and trade based on public market da…
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-…
Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks
Domenico Di Gangi, Giacomo Bormetti, Fabrizio Lillo
While the vast majority of the literature on models for temporal networks focuses on binary graphs, often one can associate a weight to each link. In such cases the data are better…