Derivatives pricing
Like your CSA dirty? It’ll cost more
Buy-side firms have to pay up if they want to post corporate bonds to their dealers, but prices vary
Robust pricing and hedging via neural stochastic differential equations
The authors propose a model called neural SDE and demonstrate how this model can make it possible to find robust bounds for the prices of derivatives and the corresponding hedging strategies.
Pricing in the gap risk of mini-futures
Mini-futures need to be priced and hedged taking sudden jumps into account
FVA hedge omission from FRTB set to cause headaches
Lack of carve-out for market risk hedges could create hedging issues, experts say
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
Podcast: the right way to wrong-way risk and climate risk in XVA
MUFG quant thinks outside the box on risk management
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