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Recurrent tasks such as pricing, calibration and risk assessment need to be executed accurately and in real-time. Simultaneously we observe an increase in model sophistication on the one hand and growing demands on the quality of risk…

Computational Finance · Quantitative Finance 2016-07-11 Maximilian Gaß , Kathrin Glau , Mirco Mahlstedt , Maximilian Mair

The challenge to measure exposures regularly forces financial institutions into a choice between an overwhelming computational burden or oversimplification of risk. To resolve this unsettling dilemma, we systematically investigate replacing…

Computational Finance · Quantitative Finance 2025-07-15 Domagoj Demeterfi , Kathrin Glau , Linus Wunderlich

The implied volatility is a crucial element of any financial toolbox, since it is used for quoting and the hedging of options as well as for model calibration. In contrast to the Black-Scholes formula its inverse, the implied volatility, is…

Computational Finance · Quantitative Finance 2017-10-06 Kathrin Glau , Paul Herold , Dilip B. Madan , Christian Pötz

Approximation theorem is one of the most important aspects of numerical analysis that has evolved over the years with many different approaches. Some of the most popular approximation methods include the Lebesgue approximation theorem, the…

Numerical Analysis · Mathematics 2024-04-16 Ishmael N. Amartey

Treating high dimensionality is one of the main challenges in the development of computational methods for solving problems arising in finance, where tasks such as pricing, calibration, and risk assessment need to be performed accurately…

Computational Finance · Quantitative Finance 2019-02-13 Kathrin Glau , Daniel Kressner , Francesco Statti

Chebyshev interpolation is a highly effective, intensively studied method and enjoys excellent numerical properties. The interpolation nodes are known beforehand, implementation is straightforward and the method is numerically stable. For…

Numerical Analysis · Mathematics 2016-11-29 Kathrin Glau , Mirco Mahlstedt

The computation of Greeks is a fundamental task for risk managing of financial instruments. The standard approach to their numerical evaluation is via finite differences. Most exotic derivatives are priced via Monte Carlo simulation: in…

Computational Finance · Quantitative Finance 2021-06-24 Andrea Maran , Andrea Pallavicini , Stefano Scoleri

Exposure simulations are fundamental to many xVA calculations and are a nested expectation problem where repeated portfolio valuations create a significant computational expense. Sensitivity calculations which require shocked and unshocked…

Risk Management · Quantitative Finance 2024-01-23 Griselda Deelstra , Lech A. Grzelak , Felix L. Wolf

Variable Annuity (VA) products expose insurance companies to considerable risk because of the guarantees they provide to buyers of these products. Managing and hedging these risks requires insurers to find the value of key risk metrics for…

Computational Finance · Quantitative Finance 2017-01-17 Seyed Amir Hejazi , Kenneth R. Jackson , Guojun Gan

We introduce a new method to calculate the credit exposure of Bermudan, discretely monitored barrier and European options. Core of the approach is the application of the dynamic Chebyshev method of Glau et al. (2019). The dynamic Chebyshev…

Computational Finance · Quantitative Finance 2019-05-02 Kathrin Glau , Ricardo Pachon , Christian Pötz

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 inside the systems of a tier-one bank, and used it to…

Risk Management · Quantitative Finance 2020-12-11 Mariano Zeron-Medina Laris , Ignacio Ruiz

We introduce a new method to price American options based on Chebyshev interpolation. In each step of a dynamic programming time-stepping we approximate the value function with Chebyshev polynomials. The key advantage of this approach is…

Computational Finance · Quantitative Finance 2018-06-15 Kathrin Glau , Mirco Mahlstedt , Christian Pötz

The dominant cost in solving least-square problems using Newton's method is often that of factorizing the Hessian matrix over multiple values of the regularization parameter ($\lambda$). We propose an efficient way to interpolate the…

Machine Learning · Computer Science 2015-06-11 Da Kuang , Alex Gittens , Raffay Hamid

Pricing of financial derivatives, in particular early exercisable options such as Bermudan options, is an important but heavy numerical task in financial institutions, and its speed-up will provide a large business impact. Recently,…

Quantum Physics · Physics 2021-08-23 Koichi Miyamoto

When the Orthogonal Chebyshev Sliding Technique was introduced it was applied to a portfolio of swaps and swaptions within the context of the FRTB-IMA capital calculation. The computational cost associated to the computation of the ES…

Risk Management · Quantitative Finance 2025-03-27 Mariano Zeron , Meng Wu , Ignacio Ruiz

This paper concerns the design of a multidimensional Chebyshev interpolation based method for a differential game theory problem. In continuous game theory problems, it might be difficult to find analytical solutions, so numerical methods…

Numerical Analysis · Mathematics 2023-07-11 Carmelo de Castro , Víctor Gatón , Beatriz Gómez

Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation…

Computational Finance · Quantitative Finance 2016-06-28 Seyed Amir Hejazi , Kenneth R. Jackson

Butterfly algorithms are an effective multilevel technique to compress discretizations of integral operators with highly oscillatory kernel functions. The particular version of the butterfly algorithm considered here realizes the transfer…

Numerical Analysis · Mathematics 2018-08-20 Steffen Börm , Christina Börst , Jens Markus Melenk

In current textbooks the use of Chebyshev nodes with Newton interpolation is advocated as the most efficient numerical interpolation method in terms of approximation accuracy and computational effort. However, we show numerically that the…

Numerical Analysis · Mathematics 2016-09-29 Michael Breuß , Friedemann Kemm , Oliver Vogel

A fast multipole method (FMM) for asymptotically smooth kernel functions (1/r, 1/r^4, Gauss and Stokes kernels, radial basis functions, etc.) based on a Chebyshev interpolation scheme has been introduced in [Fong et al., 2009]. The method…

Numerical Analysis · Computer Science 2012-11-21 Matthias Messner , Bérenger Bramas , Olivier Coulaud , Eric Darve
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