Adjoint algorithmic differentiation (AAD)
Banks look back in anger as FRTB revives 1990s risk test
Institutions bemoan need for parallel framework to measure portfolios’ sensitivities to market moves
Automatic implicit function theorem
New technique can improve use of adjoint algorithmic differentiation in calibration problems
Chebyshev Greeks: smoothing gamma without bias
A numerical method to obtain stable deltas and gammas for complex payoffs is presented
Podcast: turbo-charging derivatives pricing
Quants achieve more speed by reducing number of dimensions in price calculations
Axes that matter: PCA with a difference
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Deep XVAs and the promise of super-fast pricing
Intelligent robots can value complex derivatives in minutes rather than hours
A look at future exposures, through a 19th century lens
Can a centenarian maths idea speed up the calculation of forward sensitivities?
Technology innovation of the year: Scotiabank
Risk Awards 2021: new risk engine can run nearly a billion XVA calculations per second
Danske quants discover speedier way to crunch XVAs
Differential machine learning produces results “thousands of times faster and with similar accuracy”
Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations
Second-order Monte Carlo sensitivities
This paper considers the problem of efficiently computing the full matrix of second-order sensitivities of a Monte Carlo price when the number of inputs is large.
Competitive differentiation – Reaping the benefits of XVA centralisation
A forum of industry leaders discusses the latest developments in XVA and the strategic, operational and technological challenges of derivatives valuation in today’s environment, including the key considerations for banks looking to move to a standardised…
Fast stochastic forward sensitivities in Monte Carlo simulations using stochastic automatic differentiation (with applications to initial margin valuation adjustments)
In this paper, the author applies stochastic (backward) automatic differentiation to calculate stochastic forward sensitivities.
Is AD the answer to quicker MVA calculation?
Quants propose faster technique for Simm-MVA based on algorithmic differentiation
Podcast: Antonov on MVA, algorithmic differentiation and model validation
StanChart quant proposes new technique to compute MVA quicker
Efficient Simm-MVA calculations for callable exotics
Algorithmic differentiation are used to simulate sensitivities to calculate MVA
Podcast: Fries on Monte Carlo, Greeks and the future of AAD
Research on AAD is not complete until it becomes easier to implement, says quant
Adjoint algorithmic differentiation tool support for typical numerical patterns in computational finance
This paper demonstrates the flexibility and ease in using C++ algorithmic differentiation (AD) tools based on overloading to numerical patterns (kernels) arising in computational finance.
Nonlinear relationships in a logistic model of default for a high-default installment portfolio
This paper uses data on consumer credit along with generalized additive models to analyze nonlinear relationships and their effect on predicting the probability of default in the context of consumer credit scoring.
Reducing noise is as important as radical change
Quants study ways to reduce noise in XVA Greeks calculations