5 papers
Online Learning of Scale Parameters in Score-Driven Filters
Fabrizio Lillo, Giulia Livieri, Gianluca Palmari
A score-driven filter multiplies its scaled log-likelihood score by a scale parameter. We call this coefficient the gain and learn it online. Given the current state and realised s…
Proper-score observation-driven filters: local geometry, estimation, and continuous-time limits
Giulia Livieri, Gianluca Palmari
Observation-driven filters typically use likelihood-score updates, corresponding to the logarithmic scoring rule. We generalise these updates to negative parameter derivatives of d…
Machine-learning regression methods for American-style path-dependent contracts
Matteo Gambara, Giulia Livieri, Andrea Pallavicini
Evaluating financial products with early-termination clauses, in particular those with path-dependent structures, is challenging. This paper focuses on Asian options, look-back opt…
Rough volatility: evidence from option prices
Giulia Livieri, Saad Mouti, Andrea Pallavicini +1
It has been recently shown that spot volatilities can be very well modeled by rough stochastic volatility type dynamics. In such models, the log-volatility follows a fractional Bro…
A backward Monte Carlo approach to exotic option pricing
Giacomo Bormetti, Giorgia Callegaro, Giulia Livieri +1
We propose a novel algorithm which allows to sample paths from an underlying price process in a local volatility model and to achieve a substantial variance reduction when pricing…