## 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**