Derivatives pricing
Podcast: UBS’s Gordon Lee on conditional expectations and XVAs
Top quant explains why XVA desks need a neighbour and a reverend
Rough volatility moves to exotic frontiers
New simulation scheme clears the way for broader application of the rough Heston model
What quant finance can learn from a 240-year-old problem
Optimal transport theory offers a data-driven way to calibrate derivatives pricing models
An ‘optimal’ way to calculate future P&L distributions?
Quants use neural networks to upgrade classic options pricing model
Axes that matter: PCA with a difference
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Derivatives pricing starts feeling the heat of climate change
Quants find physical and transition risks can lead to significant rise in CVA
Show your workings: lenders push to demystify AI models
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
Capturing the effects of climate change on CVA and FVA
A framework to incorporate climate change risk into derivative prices is presented
How XVA quants learned to trust the machine
Initial scepticism about using neural networks for derivatives pricing is giving way to enthusiasm
Deep XVAs and the promise of super-fast pricing
Intelligent robots can value complex derivatives in minutes rather than hours
Hedging valuation adjustment gets cold shoulder from banks
Dealers back the idea of charging for hedging costs but not as part of a new XVA
Podcast: Piterbarg on medians and machine learning
How the Libor transition inspired NatWest quant’s latest paper on exotic derivatives valuation
The arcsine law for quantile derivatives
A new pricing model for quantile-based derivatives, such as Napoleon options, is presented
Dealers applaud proposal to halt yen Libor swaps after Q3
BoJ working group timetable viewed as likely to boost liquidity in nascent Tonar market
The cost of hedging XVA
HVA is framed consistently with other valuation adjustments
Gradient boosting for quantitative finance
In this paper, the authors discuss how tree-based machine learning techniques can be used in the context of derivatives pricing.
XVAs ate $401m of JP Morgan’s revenues in 2020
Credit valuation adjustment on derivatives cost $337 million alone
Hedging valuation adjustment: fact and friction
Transaction costs’ impact on hedging can now be quantified
Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
Goldman, IBM lay out quantum road map for derivatives pricing
Researchers estimate 7,500 logical qubits and 46 million T-gates would be needed to price options
A step closer to the perfect volatility model
Research on ‘rough volatility’ gives fresh insight into financial fluctuations, quant expert explains
Finite difference schemes with exact recovery of vanilla option prices
A model unifies the classic local vol and binomial trees to accurately price options
TSE outage throws structured notes into tailspin
Trading shutdown on October 1 disrupted observation dates for some structured products
Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations