# Derivatives pricing

##### Model risk quantification based on relative entropy

This paper proposes a minimum relative entropy technique for challenging derivatives pricing models that can also assess the model risk of a target portfolio.

##### Getting the jump on pricing dividend-protected derivatives

Morgan Stanley quants show how to avoid mispricing corporate options and convertible bonds

##### How Michael Spector left his mark on quantitative finance

Physicist trained in Soviet scientific centres found elegant solutions to complex problems

##### Buy side looks to cash in on euro swap pricing anomaly

Fixed rates on long-dated €STR swaps now above their Euribor equivalents

##### Banks strive for machine learning at quantum speed

Embryonic work on quantum neural networks raises hope of faster, more accurate models

##### Deep hedging: learning to remove the drift

Removing arbitrage opportunities from simulated data used for training makes deep hedging more robust

##### 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.

##### Hedging valuation adjustment: fact and friction

Transaction costs’ impact on hedging can now be quantified

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