activity
20182020
most citedTensoring volatility calibration

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

q-fin.RM20201 cited

Tensoring volatility calibration

Mariano Zeron, Ignacio Ruiz

Inspired by a series of remarkable papers in recent years that use Deep Neural Nets to substantially speed up the calibration of pricing models, we investigate the use of Chebyshev…

q-fin.RM2020

Dynamic sensitivities and Initial Margin via Chebyshev Tensors

Mariano Zeron, Ignacio Ruiz

This paper presents how to use Chebyshev Tensors to compute dynamic sensitivities of financial instruments within a Monte Carlo simulation. Dynamic sensitivities are then used to c…

q-fin.RM2019

Denting the FRTB IMA computational challenge via Orthogonal Chebyshev Sliding Technique

Mariano Zeron-Medina Laris, Ignacio Ruiz

In this paper we introduce a new technique based on high-dimensional Chebyshev Tensors that we call \emph{Orthogonal Chebyshev Sliding Technique}. We implemented this technique ins…

q-fin.RM2018

An Enhanced Initial Margin Methodology to Manage Warehoused Credit Risk

Lucia Cipolina-Kun, Ignacio Ruiz, Mariano Zero-Medina Laris

The use of CVA to cover credit risk is widely spread, but has its limitations. Namely, dealers face the problem of the illiquidity of instruments used for hedging it, hence forced…

q-fin.RM2018

Dynamic Initial Margin via Chebyshev Tensors

Ignacio Ruiz, Mariano Zeron

We present two methods, based on Chebyshev tensors, to compute dynamic sensitivities of financial instruments within a Monte Carlo simulation. These methods are implemented and run…

q-fin.RM2018

Chebyshev Methods for Ultra-efficient Risk Calculations

Mariano Zeron Medina Laris, Ignacio Ruiz

Financial institutions now face the important challenge of having to do multiple portfolio revaluations for their risk computation. The list is almost endless: from XVAs to FRTB, s…