Model Acceleration

Looking for Trouble: Validating ML Pricers

Nov 2021

Learn about how we validate our super-fast models and prove that we achieve performance improvements of more than a million-fold without compromising accuracy.

Volatility Surface Completion

Stop messing with your volatility surface

Oct 2021

Riskfuel has developed a technology to automatically complete volatility surfaces for even the most illiquid assets.

Model Acceleration

Libor Prompts Quantile Leap: Machine Learning for Quantile Derivatives

July 2021

We show how deep neural networks can be trained to quickly and accurately calculate the value of exotic quantile derivatives.

Volatility Surface Completion

Arbitrage-Free Implied Vol Surface Generation with Variational Autoencoders

Aug 2021

We propose a hybrid method for generating arbitrage-free implied volatility  surfaces consistent with historical data by combining model-free Variational Autoencoders with continuous time stochastic differential equation driven models.

Volatility Surface Completion

Hands-Off Approach to Completing Implied Volatility Surfaces

March 2021

In this talk, Riskfuel’s Director of R&D explains how variational autoencoders can remove human bias from this procedure and let the data speak for itself through unsupervised learning.

Video

Deeply Learning Derivatives: from Hilbert to Riskfuel

March 2021

In this talk, we will explain how graphical solvers of Hilbert’s day fit into the modern deep learning framework and ultimately allow us to build networks that replicate the solutions operator of stochastic differential equations governing the valuation of high dimensional contingent claims.

Volatility Surface Completion

Variational Autoencoders: A Hands-Off Approach to Volatility

Feb 2021

Variational autoencoders can be used to construct a complete volatility surface when only a small number of points are available without making assumptions about the process driving the underlying asset or the shape of the surface.

Model Acceleration

Deep Learning to Jump

Oct 2020

We describe a Jump Unit that can be used to fit a step function with a simple neural network. Our motivation comes from quantitative finance problems where discontinuities often appear

Model Acceleration

Ultra-fast and Accurate Derivatives Pricing with Deep Learning

Jun 2020

Technical details to accompany the article written by Ian Finder of Microsoft.

Model Acceleration

Exploring Riskfuel's Bermudan Swaption Pricing Demo

Feb 2020

This article describes the Bermudan Swaption and its valuation models, and discusses a small case study to illustrate the accuracy of the Riskfuel model and compare its run-time performance again the target Quantlib model.

Model Acceleration

1,000,000x faster models: how it works

Jan 2020

Deep neural networks can be trained to learn a functional approximation of derivatives valuation models that use mathematic simulations. Riskfuel AI-based technology cuts the computation costs to virtually zero allowing for on-demand recalculation of portfolio values and a complete up-to-the-second view on risk

Model Acceleration

Deeply Learning Derivatives: The Paper that Started It All

Oct 2018

This paper shows how we can use deep learning neural networks to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models.

Riskfuel

1,000,000x