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On January 14th 2016, the Basel Committee on Banking Supervision (BCBS) published its revised capital requirements for market risk. The final standard, also known as the Fundamental Review of the Trading Book (FRTB), is intended to harmonise the treatment of market risk across national jurisdictions and will generally result in higher global capital requirements for financial institutions, both with IMA and Standard Approach. Following this, banks have asked Avantage Reply to analyse the calculation of the capital requirements under the Standardised Approach.
Avantage Reply is assisting banking groups with the implementation of the new market risk capital framework using the following strategic approach:
In this respect, Avantage Reply is supporting companies in the definition of methodological and functional requirements necessary for the development of two numerical methodologies: Adjoint Algorithmic Differentiation and Machine Learning.
Adjoint Algorithmic Differentiation (AAD) is an advanced numerical methodology for the calculation of sensitivities. Avantage Reply uses this technique in order to support the business in:
Calculating capital requirements for market risk management;
Calculating measures as required by the new regulations: FRTB – SA, SIMM, FRTB – CVA;
Supporting the front office through enhanced pre-deal analysis;
Actively managing regulatory capital (RWA).
AAD is a numerical technique popularised by prof. Luca Capriotti in his paper of 2012. It enables faster computation of sensitivities as compared with the traditional 'bump and re-price' approach. The use of AAD provides the ability to compute in near real time the sensitivities of NPV and XVA. This would not be possible with standard methods.
Formally, the AAD consists of three main steps:
Splitting: the pricing or XVA function is decomposed in simpler pieces;
Forward Propagation: the function value is computed via intermediate steps;
Adjoint Differentiation: the computation of the gradient is performed going «backward».
Machine learning uses artificial intelligence and deep learning to understand patterns in data sets as to parametrise models based on a given objective function.
Avantage Reply implemented Reinforcement Learning to constrained optimization problems such as P&L maximization and capital minimization (as computed under the new Standardised Approach).