Fast correlation Greeks by adjoint algorithmic differentiation

Adjoint methods have recently been proposed as an efficient way to calculate risk through Monte Carlo simulation. Luca Capriotti and Mike Giles extend these ideas and show how adjoint algorithmic differentiation allows for fast calculation of price sensitivities in full generality. They illustrate the method for the calculation of correlation risk and test it numerically for portfolio default options

One of the consequences of the recent crisis of the financial markets is a renewed emphasis on rigorous risk management practices. To quantify the financial exposure of financial firms, and to ensure efficient capital allocation and more effective hedging practices, regulators and senior management alike are insisting more and more on a thorough monitoring of risk. Among all businesses, those dealing with complex, over-the-counter derivatives are the ones receiving the most attention.

PLEASE

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

Most read articles loading...

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