Technical paper/Derivatives pricing
AI as pricing law
A neural network tailored to financial asset pricing principles is introduced
New proxy schemes for swing contracts
The authors investigate the valuation of swing contracts for energy markets and propose two methods which offer more accurate calculated prices than commonly used methods.
SABR convexity adjustment for an arithmetic average RFR swap
A model-independent convexity adjustment for interest rate swaps is introduced
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
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
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.
Deep hedging: learning to remove the drift
Removing arbitrage opportunities from simulated data used for training makes deep hedging more robust
Axes that matter: PCA with a difference
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Capturing the effects of climate change on CVA and FVA
A framework to incorporate climate change risk into derivative prices is presented
The arcsine law for quantile derivatives
A new pricing model for quantile-based derivatives, such as Napoleon options, is presented
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
Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
Finite difference schemes with exact recovery of vanilla option prices
A model unifies the classic local vol and binomial trees to accurately price options
Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations
Second-order Monte Carlo sensitivities
This paper considers the problem of efficiently computing the full matrix of second-order sensitivities of a Monte Carlo price when the number of inputs is large.
The homotopy analysis method for derivatives pricing under wrong-way risk
Derivatives pricing is approximated with a computationally efficient homotopy-based application that accounts for WWR
In the balance redux
Mats Kjaer developes a dynamic balance-sheet pricing model for valuation adjustments
Roughening Heston
El Euch, Rosenbaum, Gatheral combine a rough volatility model with the classical Heston model