Fast Greeks – Algorithmic Differentiation Tutorial

Computing sensitivities is a core need in XVA calculations for risk management and hedging, many pricing algorithms, and model calibration. In this tutorial we zoom in on Algorithmic Differentiation as an efficient and robust alternative to the traditional finite difference (bumping) method. The approach will be illustrated using a practical code example.

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XVA Calculations in a Nutshell

In the aftermath of the 2007/2008 global financial crisis, financial institutions have shifted their focus to more actively manage their risks by means of various valuation adjustments (XVA). This white paper gives an overiew of the different XVA adjustments, shows how they are typically compputed, and outlines where the computational challenges lie.

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A Guide to xVA Algorithmic Optimisations

It is standard practice today to adjust the price of a traded financial instrument for the associated risks by means of valuation adjustments (XVAs). Calculating these is a tremendous computational challenge. The first step to reduce this complexity is to carefully think about possible mathematical optimisations.

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Accelerating CVA Computations the Easy Way

This paper examines the implementation of credit valuation adjustment (CVA) algorithms, outlines their typical structure, and explains how the increasing complexity consumes vast compute resources. It gives a typical implementation before introducing how large speedups can be achieved while preserving developer productivity.

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XVA: Coping with the Tsunami of Compute Load

This article gives an overview of the different xVA adjustments, such as CVA, DVA, and FVA, shows how they are typically computed, and outlines where the computational complexities lie. We show how to achieve high performance, portability, and scalability for centralised xVA implementations.

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Monte-Carlo Methods

Monte-Carlo simulations are among the most common numerical methods in computational finance, used when closed-form solutions or other numerical methods are not practical or do not exist. This white paper shows how to easily implement Monte-Carlo on high-performance hardware.

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Back Testing of Algorithmic Trading Strategies

Algorithmic trading has become ever more popular in recent years. The trading strategies need to be back-tested regularly using historical market data for calibration and to check the expected return and risk. This is a computationally demanding process that can take hours to complete.

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Back Testing of Algorithmic Trading Strategies

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Monte-Carlo Methods

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Accelerating CVA Computations the Easy Way

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XVA: Copying with the Tsunami of Compute Load

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Fast Greeks - AD Tutorial

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XVA Calculations in a Nutshell

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XVA Algorithmic Optimizations

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