CLICK HERE TO VIEW THE PDF
Pascal Traccucci, Luc Dumontier, Guillaume Garchery and Benjamin Jacot present an extended reverse stress test (ERST) triptych approach with three variables: level of plausibility, level of loss and scenario. Any two of these variables can be derived, provided the third is given as input. A new version of the Levenberg-Marquardt optimisation algorithm is introduced to derive the ERST in certain complex cases
Academic theory has been mined to support the development
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 print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
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 info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
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 info@risk.net
More on Investments
Dynamic margining long/short equity trading strategies
A repo haircut model extends a previous solution for long-only strategies
The cost of mis-specifying price impact
Expected returns can be significantly affected by the wrong use of impact models
Optimal allocation to cryptocurrencies in diversified portfolios
Asset allocation methods assign positive weights to cryptos in diversified portfolios
Getting more for less: better A / B testing via causal regularisation
A causal machine learning algorithm is used to estimate trades’ price impact
Fat tails and optimal LDI portfolios
A portfolio optimisation technique for pension funds and insurance portfolios is presented
Trading the vol-of-vol risk premium
Applications of the vol-of-vol parameter for cross-asset derivatives are presented
Asset allocation with inverse reinforcement learning
Using reinforcement learning to help replicate asset managers' allocation strategy
Sculpting implied volatility surfaces of illiquid assets
From the stock cumulative distribution function an arbitrage-free volatility surface is derived
Most read
- Top 10 operational risks for 2024
- Filling gaps in market data with optimal transport
- The American way: a stress-test substitute for Basel’s IRRBB?