Dynamically controlled kernel estimation

Gordon Lee
19 Jan 2022

Tweet
Facebook
LinkedIn
Save this article
Send to
Print this page

CLICK HERE TO DOWNLOAD THE PDF

Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular Gaussian process regression and kernel density estimation stabilised with a control variate. The method is applied to pricing and hedging of (multidimensional) exotic options for different models, including the rough Bergomi model

For many financial applications it is

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 [email protected] or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to print this content. Please contact [email protected] to find out more.

You are currently unable to copy this content. Please contact [email protected] to find out more.

You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/

If you would like to purchase additional rights please email [email protected]

You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/

If you would like to purchase additional rights please email [email protected]