Alternatives to deep neural networks in finance

Two methods to approximate complex functions in an explainable way are presented

CLICK HERE TO DOWNLOAD THE PDF

Alexandre V. Antonov and Vladimir V. Piterbarg develop two methods for approximating slow-to-calculate functions and for conditional expected value calculations: the generalised stochastic sampling (gSS) method and the functional tensor train (fTT) method, respectively. These are proposed as high-performing alternatives to the generic deep neural networks (DNNs) currently routinely recommended in derivatives pricing and other quantitative finance applications. The

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact info@risk.net to find out more.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here