Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Frédéric Vrins"'
Publikováno v:
Risks, Vol 10, Iss 6, p 124 (2022)
While previous academic research highlights the potential of machine learning and big data for predicting corporate bond recovery rates, the operations management challenge is to identify the relevant predictive variables and the appropriate model. I
Externí odkaz:
https://doaj.org/article/82c52ea04b37417b91e329f8e5698fc7
Autor:
Frédéric Vrins
Publikováno v:
Risks, Vol 6, Iss 3, p 64 (2018)
Statistical modeling techniques—and factor models in particular—are extensively used in practice, especially in the insurance and finance industry, where many risks have to be accounted for. In risk management applications, it might be important
Externí odkaz:
https://doaj.org/article/e606a04e5fac46fda2987cbae36e44de
Autor:
Frédéric Vrins, Linqi Wang
Publikováno v:
Quantitative Finance. 23:279-295
Autor:
Frédéric Vrins
Publikováno v:
Regards économiques.
Le 17 mars 2023, Silicon Valley Bank (SVB), 16ème banque aux Etats-Unis par sa taille, déposait le bilan. Le jour même, l’action de Crédit Suisse, 17ème plus grande banque d’Europe, chutait de plus de 60% et sera rachetée deux jours plus ta
Publikováno v:
Operations Research, Vol. 70, no. 1, p. 55-72 (2022)
A natural approach to enhance portfolio diversification is to rely on factor-risk parity, which yields the portfolio whose risk is equally spread among a set of uncorrelated factors. The standard choice is to take the variance as risk measure, and th
Autor:
Frédéric Vrins, Nathan Lassance
Publikováno v:
European Journal of Operational Research, (2023)
We introduce a general framework to the portfolio-selection problem in which investors aim at targeting a distribution of returns, which can accommodate a wide range of preferences. The resulting optimal portfolio has a return density that is as clos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34a5b2f9592f91e4f31efdaf03ba5d8a
https://hdl.handle.net/2078.1/272598
https://hdl.handle.net/2078.1/272598
Autor:
Matteo Barbagli, Frédéric Vrins
Publikováno v:
Economic Modelling. 125:106321
Publikováno v:
BASE-Bielefeld Academic Search Engine
We compare the performances of a wide set of regression techniques and machine learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorit
Publikováno v:
Stochastic Processes and their Applications, (2019)
Stochastic Processes and Their Applications, Vol. 130, no. 7, p. 3895-3919 (2020)
Stochastic Processes and Their Applications, Vol. 130, no. 7, p. 3895-3919 (2020)
It is known since Kellerer (1972) that for any peacock process there exist mar-tingales with the same marginal laws. Nevertheless, there is no general method for finding such martingales that yields diffusions. Indeed, Kellerer's proof is not constru
Autor:
Cheikh Mbaye, Frédéric Vrins
Publikováno v:
Mathematical Finance, Vol. 32, no. 2, p. 678-724 (2022)
We address the so-called calibration problem which consists of fitting in a tractable way a given model to a specified term structure like, e.g., yield, prepayment or default probability curves. Time-homogeneous jump-diffusions like Vasicek or Cox-In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::226e6da3a3e7f2b001e3d3f83a14c62a
https://hdl.handle.net/2078.1/254447
https://hdl.handle.net/2078.1/254447