Neural joint S&P 500/VIX smile calibration
A one-factor stochastic local volatility model can solve the joint calibration problem
CLICK HERE TO DOWNLOAD THE PDF
Julien Guyon and Scander Mustapha simultaneously calibrate neural stochastic differential equations to Standard & Poor’s 500 smiles, the Chicago Board Options Exchange’s Volatility Index futures and VIX smiles. The drifts and volatilities are modelled as neural networks. Minimising a suitable loss allows us to fit the market data for multiple S&P 500 and VIX maturities. A one-factor Markovian stochastic local volatility model is shown to fit both smiles and VIX
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 Cutting Edge
Choosing trading strategies using importance sampling
The sampling technique is more efficient than A-B testing at comparing decision rules
A comparison of FX fixing methodologies
FX fixing outcomes are mostly driven by length of calculation window
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
Backtesting correlated quantities
A technique to decorrelate samples and reach higher discriminatory power is presented
A hard exit threshold strategy for market-makers
A closed-form solution to derive optimal stop-loss and profit-taking levels is presented
Pricing share buy-backs: an alternative to optimal control
A new method applies optimised heuristic strategies to maximise share buy-back contracts’ value
CVA sensitivities, hedging and risk
A probabilistic machine learning approach to CVA calculations is proposed
