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

October 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.

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

Ultra-fast and Accurate Derivatives Pricing with Deep Learning

July 2021 — We show how deep neural networks can be trained to quickly and accurately calculate the value of exotic quantile derivatives.

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

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