When the data’s not there, expert-led models could help

Missing data is a problem. Expert elicitation taps the knowledge of many, say consultants

Robo advice

If the data’s spotty, fleeting, reflective of nothing real or just not there, how does a quant fill the void? The expert elicitation approach to modelling could offer a way.

In contrast to plain vanilla expert judgement, expert elicitation extracts probabilistic belief statements, often from several authorities, on quantities or parameters. The approach brings a structured procedure to building models from sparse datasets, and adheres to the same scientific rules: transparency, accountability

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

Register

Want to know what’s included in our free membership? Click here

This address will be used to create your account

Digging deeper into deep hedging

Dynamic techniques and gen-AI simulated data can push the limits of deep hedging even further, as derivatives guru John Hull and colleagues explain

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