29 citations · 71 across the 23 of their papers we have counts for
24 papers
Random Gradient-Free Optimization in Infinite Dimensional Spaces
Caio Peixoto, Daniel Csillag, Bernardo F. P. da Costa +1
We propose a new gradient-free method for infinite-dimensional optimization in Hilbert spaces that requires only the computation of directional derivatives. Though functional optim…
Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise
Felipe J. P. Antunes, Yuri F. Saporito, Sebastian Jaimungal
We present a novel numerical method for solving McKean-Vlasov forward-backward stochastic differential equations (MV--FBSDEs) with common noise, combining Picard iterations, elicit…
On the lumpability of tree-valued Markov chains
Rodrigo B. Alves, Yuri F. Saporito, Luiz M. Carvalho
Phylogenetic trees constitute an interesting class of objects for stochastic processes due to the non-standard nature of the space they inhabit. In particular, many statistical app…
Risk Budgeting Allocation for Dynamic Risk Measures
Silvana M. Pesenti, Sebastian Jaimungal, Yuri F. Saporito +1
We define and develop an approach for risk budgeting allocation - a risk diversification portfolio strategy - where risk is measured using a dynamic time-consistent risk measure. F…
Optimal Trading in Automated Market Makers with Deep Learning
Sebastian Jaimungal, Yuri F. Saporito, Max O. Souza +1
This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interact…
A mathematical framework for dynamical social interactions with dissimulation
Yuri Saporito, Max O. Souza, Yuri Thamsten
Modeling social interactions is a challenging task that requires flexible frameworks. For instance, dissimulation and externalities are relevant features influencing such systems -…
Statistical Learning and Inverse Problems: A Stochastic Gradient Approach
Yuri R. Fonseca, Yuri F. Saporito
Inverse problems are paramount in Science and Engineering. In this paper, we consider the setup of Statistical Inverse Problem (SIP) and demonstrate how Stochastic Gradient Descent…
Price formation in financial markets: a game-theoretic perspective
David Evangelista, Yuri Saporito, Yuri Thamsten
We propose two novel frameworks to study the price formation of an asset negotiated in an order book. Specifically, we develop a game-theoretic model in many-person games and mean-…
Functional Classification of Bitcoin Addresses
Manuel Febrero-Bande, Wenceslao González-Manteiga, Brenda Prallon +1
This paper proposes a classification model for predicting the main activity of bitcoin addresses based on their balances. Since the balances are functions of time, we apply methods…
Optimal Trading with Signals and Stochastic Price Impact
Jean-Pierre Fouque, Sebastian Jaimungal, Yuri F. Saporito
Trading frictions are stochastic. They are, moreover, in many instances fast-mean reverting. Here, we study how to optimally trade in a market with stochastic price impact and stud…
KrigHedge: Gaussian Process Surrogates for Delta Hedging
Mike Ludkovski, Yuri Saporito
We investigate a machine learning approach to option Greeks approximation based on Gaussian process (GP) surrogates. The method takes in noisily observed option prices, fits a nonp…
Pricing Path-Dependent Derivatives under Multiscale Stochastic Volatility Models: a Malliavin Representation
Yuri F. Saporito
In this paper we derive a efficient Monte Carlo approximation for the price of path-dependent derivatives under the multiscale stochastic volatility models of Fouque \textit{et al}…
PDGM: a Neural Network Approach to Solve Path-Dependent Partial Differential Equations
Yuri F. Saporito, Zhaoyu Zhang
In this paper, we propose a novel numerical method for Path-Dependent Partial Differential Equations (PPDEs). These equations firstly appeared in the seminal work of Dupire [2009],…
Avoiding zero probability events when computing Value at Risk contributions
Takaaki Koike, Yuri F. Saporito, Rodrigo S. Targino
This paper is concerned with the process of risk allocation for a generic multivariate model when the risk measure is chosen as the Value-at-Risk (VaR). We recast the traditional E…
Extensions of the Deep Galerkin Method
Ali Al-Aradi, Adolfo Correia, Danilo de Frietas Naiff +2
We extend the Deep Galerkin Method (DGM) introduced in Sirignano and Spiliopoulos (2018)} to solve a number of partial differential equations (PDEs) that arise in the context of op…
Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning
Ali Al-Aradi, Adolfo Correia, Danilo Naiff +2
In this work we apply the Deep Galerkin Method (DGM) described in Sirignano and Spiliopoulos (2018) to solve a number of partial differential equations that arise in quantitative f…
First-Order Asymptotics of Path-Dependent Derivatives in Multiscale Stochastic Volatility Environment
Yuri F. Saporito
In this paper, we extend the first-order asymptotics analysis of Fouque et al. to general path-dependent financial derivatives using Dupire's functional Ito calculus. The main conc…
The Calibration of Stochastic-Local Volatility Models - An Inverse Problem Perspective
Yuri F. Saporito, Xu Yang, Jorge P. Zubelli
We tackle the calibration of the so-called Stochastic-Local Volatility (SLV) model. This is the class of financial models that combines the local and stochastic volatility features…
Stochastic Control with Delayed Information and Related Nonlinear Master Equation
Yuri F. Saporito, Jianfeng Zhang
In this paper we study stochastic control problems with delayed information, that is, the control at time can depend only on the information observed before time for some…
Heston Stochastic Vol-of-Vol Model for Joint Calibration of VIX and S&P 500 Options
Jean-Pierre Fouque, Yuri F. Saporito
A parsimonious generalization of the Heston model is proposed where the volatility-of-volatility is assumed to be stochastic. We follow the perturbation technique of Fouque et al (…
Stochastic Control and Differential Games with Path-Dependent Influence of Controls on Dynamics and Running Cost
Yuri F. Saporito
In this paper, we consider the functional Itô calculus framework to find a path-dependent version of the Hamilton-Jacobi-Bellman equation for stochastic control problems that featu…
Functional Meyer-Tanaka Formula
Yuri F. Saporito
The functional Ito formula, firstly introduced by Bruno Dupire for continuous semimartingales, might be extended in two directions: different dynamics for the underlying process an…
Multiscale Stochastic Volatility Model for Derivatives on Futures
Jean-Pierre Fouque, Yuri F. Saporito, Jorge P. Zubelli
In this paper we present a new method to compute the first-order approximation of the price of derivatives on futures in the context of multiscale stochastic volatility of Fouque \…
Functional Ito Calculus, Path-dependence and the Computation of Greeks
Samy Jazaerli, Yuri F. Saporito
Dupire's functional Itô calculus provides an alternative approach to the classical Malliavin calculus for the computation of sensitivities, also called Greeks, of path-dependent de…