Search Results for author: Francesca Biagini

Found 12 papers, 1 papers with code

Supplement Liquidity based modeling of asset price bubbles via random matching

no code implementations27 Nov 2023 Francesca Biagini, Andrea Mazzon, Thilo Meyer-Brandis, Katharina Oberpriller

This is a supplement to the paper "Liquidity based modeling of asset price bubbles via random matching".

Approximation Rates for Deep Calibration of (Rough) Stochastic Volatility Models

no code implementations26 Sep 2023 Francesca Biagini, Lukas Gonon, Niklas Walter

We derive quantitative error bounds for deep neural networks (DNNs) approximating option prices on a $d$-dimensional risky asset as functions of the underlying model parameters, payoff parameters and initial conditions.

Collective Arbitrage and the Value of Cooperation

no code implementations20 Jun 2023 Francesca Biagini, Alessandro Doldi, Jean-Pierre Fouque, Marco Frittelli, Thilo Meyer-Brandis

We introduce the notions of Collective Arbitrage and of Collective Super-replication in a setting where agents are investing in their markets and are allowed to cooperate through exchanges.

Liquidity based modeling of asset price bubbles via random matching

no code implementations25 Oct 2022 Francesca Biagini, Andrea Mazzon, Thilo Meyer-Brandis, Katharina Oberpriller

In this paper we study the evolution of asset price bubbles driven by contagion effects spreading among investors via a random matching mechanism in a discrete-time version of the liquidity based model of [25].

Detecting asset price bubbles using deep learning

no code implementations4 Oct 2022 Francesca Biagini, Lukas Gonon, Andrea Mazzon, Thilo Meyer-Brandis

In this paper we employ deep learning techniques to detect financial asset bubbles by using observed call option prices.

Reduced-form framework for multiple ordered default times under model uncertainty

no code implementations9 Aug 2021 Francesca Biagini, Andrea Mazzon, Katharina Oberpriller

In this paper we introduce a sublinear conditional operator with respect to a family of possibly nondominated probability measures in presence of multiple ordered default times.

Neural network approximation for superhedging prices

no code implementations29 Jul 2021 Francesca Biagini, Lukas Gonon, Thomas Reitsam

First we prove that the $\alpha$-quantile hedging price converges to the superhedging price at time $0$ for $\alpha$ tending to $1$, and show that the $\alpha$-quantile hedging price can be approximated by a neural network-based price.

A dynamic version of the super-replication theorem under proportional transaction costs

no code implementations6 Jul 2021 Francesca Biagini, Thomas Reitsam

We extend the super-replication theorems of [27] in a dynamic setting, both in the num\'eraire-based as well as in the num\'eraire-free setting.

Asset Price Bubbles in market models with proportional transaction costs

no code implementations22 Nov 2019 Francesca Biagini, Thomas Reitsam

We study asset price bubbles in market models with proportional transaction costs $\lambda\in (0, 1)$ and finite time horizon $T$ in the setting of [49].

Position

A unified approach to xVA with CSA discounting and initial margin

1 code implementation27 May 2019 Francesca Biagini, Alessandro Gnoatto, Immacolata Oliva

In this paper we extend the existing literature on xVA along three directions.

Pricing of Securities

Extended Reduced-Form Framework for Non-Life Insurance

no code implementations21 Feb 2018 Francesca Biagini, Yinglin Zhang

In this paper we propose a general framework for modeling an insurance liability cash flow in continuous time, by generalizing the reduced-form framework for credit risk and life insurance.

The Long-Term Swap Rate and a General Analysis of Long-Term Interest Rates

no code implementations1 Jul 2015 Francesca Biagini, Alessandro Gnoatto, Maximilian Härtel

Furthermore we analyse the relationship between the long-term swap rate, the long-term yield, see Biagini et al. [2018], Biagini and H\"artel [2014], and El Karoui et al. [1997], and the long-term simple rate, considered in Brody and Hughston [2016] as long-term discounting rate.

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