Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
CLICK HERE TO VIEW THE PDF
Artificial neural networks have recently been proposed as accurate and fast approximators in various derivatives pricing applications. Their extrapolation behaviour cannot be controlled due to the complex functional forms typically involved. Alexandre Antonov, Michael Konikov and Vladimir Piterbarg overcome this significant limitation and develop a new type of neural network that incorporates large-value asymptotics, allowing explicit control over extrapolation
Artif
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.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
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. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
More on Banking
A comparison of FX fixing methodologies
FX fixing outcomes are mostly driven by length of calculation window
Backtesting correlated quantities
A technique to decorrelate samples and reach higher discriminatory power is presented
CVA sensitivities, hedging and risk
A probabilistic machine learning approach to CVA calculations is proposed
Bridging the gap risk reloaded: modelling wrong-way risk and leverage
A model extends the counterparty risk calculation to include nonlinear and complex portfolios
Weighting for leverage
A credit exposure model for leveraged collateralised counterparties is presented
Rethinking P&L attribution for options
A buy-side perspective on how to decompose the P&L of index options is presented
Volatility shape-shifters: arbitrage-free shaping of implied volatility surfaces
Manipulating implied volatility surfaces using optimal transport theory has several applications
Infrequent MtM reduces neither value-at-risk nor backtesting exceptions
Frequency of repricing impacts volatility and correlation measures
