Introducing an algorithm for computing vega sensitivities at all strikes and expiries
The authors develop and apply a formula to derive closed-form expressions in particular quantitative finance cases.
New technique can improve use of adjoint algorithmic differentiation in calibration problems
A numerical method to obtain stable deltas and gammas for complex payoffs is presented
Quants achieve more speed by reducing number of dimensions in price calculations
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Intelligent robots can value complex derivatives in minutes rather than hours
Can a centenarian maths idea speed up the calculation of forward sensitivities?
Risk Awards 2021: new risk engine can run nearly a billion XVA calculations per second
Differential machine learning produces results “thousands of times faster and with similar accuracy”
A derivative pricing approximation method using neural networks and AAD speeds up calculations
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.
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.
Quants propose faster technique for Simm-MVA based on algorithmic differentiation
StanChart quant proposes new technique to compute MVA quicker
Algorithmic differentiation are used to simulate sensitivities to calculate MVA
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.
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.
Quants study ways to reduce noise in XVA Greeks calculations
The calculation of XVA Greeks for portfolios with early-exercise products is discussed
Proposed CCAR changes make KVA calculations even more complex