University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Computing risk measures by importance sampling

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Computing risk measures by importance sampling

Download to your calendar using vCal

If you have a question about this talk, please contact Mustapha Amrani .

Stochastic Processes in Communication Sciences

Computation of extreme quantiles and tail-based risk measures using standard Monte Carlo simulation can be inefficient. A method to speed up computations is provided by importance sampling. We show that importance sampling algorithms, designed for e cient tail probability estimation, can signi.cantly improve Monte Carlo estimators of tail-based risk measures. In the heavy-tailed setting, when the random variable of interest has a regularly varying distribution, we provide su cient conditions for the asymptotic relative error of importance sampling estimators of risk measures, such as Value-at-Risk and expected shortfall, to be small. The results are illustrated by some numerical examples.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Wolfson College, room TBC Webinar & FW11, Computer Laboratory, William Gates Building. BCNI seminar room, Sir William Hardy Building

Other talks

LCLU Annual Talk: The Early Aqueous Environment of Mars Inferred From Mission Lifetime Results by the Curiosity Rover at Gale Crater Complete and Submit Project Choice Forms Clonal haematopoiesis, early detection and pre-leukemia to leukemia development Title TBC The Play Paradox: How video games build better humans Title to be confirmed
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity