Option pricing
A positive response to negative oil prices
Overhauling pricing models could reap rewards even if prices don’t cross zero again
Bachelier – a strange new world for oil options
Model tuned to negative prices has implications for pricing, margining and delta hedging
Two quants use options pricing tools to model Covid-19
New tool aims to gauge wider cost of virus control measures
Numerical simulation and applications of the convection–diffusion–reaction
This paper develops two local mesh-free methods for designing stencil weights and spatial discretization, respectively, for parabolic partial differential equations (PDEs) of convection–diffusion–reaction type.
Three adjustments in calibrating models with neural networks
New research addresses fundamental issues with ANN approximation of pricing models
Deep learning calibration of option pricing models: some pitfalls and solutions
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
ADOL: Markovian approximation of a rough lognormal model
A variation of the rough volatility model is introduced by plugging in a different stochastic process
EU banks grapple with NMRF proposals for volatility models
EBA options for lighter capital treatment of parametric curves could prove impractical
A pairwise local correlation model
In this paper, the authors develop a new local correlation model that uses a generic function 'g' to describe the correlation between all asset–asset pairs for a basket of underlyings.
Hedging of options in the presence of jump clustering
This paper analyzes the efficiency of hedging strategies for stock options in the presence of jump clustering.
Brexit drama muddies water for FX options market
Traders focusing on new dates – and scenarios – after domestic UK criticism of proposed deal
Podcast: SocGen quants on exotics calibration, machine learning and autocallable pricing
Deep learning techniques are being explored by the quants to speed up exotics pricing
Importance sampling for jump–diffusions via cross-entropy
This paper develops efficient importance sampling schemes for a class of jump–diffusion processes that are commonly used for modeling stock prices.
Importance sampling applied to Greeks for jump–diffusion models with stochastic volatility
In this paper, the authors develop a procedure to reduce the variance when numerically computing the Greeks obtained via Malliavin calculus for jump–diffusion models with stochastic volatility.
Cash no longer king in European swaptions
Barclays executives explore weaknesses of current pricing formulas for cash-settled swaptions
Quants needed: how finance can use power of quantum tech
New machines have big potential in AI, valuations and VAR, but tech giants like IBM need help from practitioners
Podcast: Callegaro, Fiorin and Grasselli on quantization
High-dimension problems can be solved with discretisation techniques
Quantitative finance still needs mathematicians
Quants develop model that fixes a longstanding problem with pricing American options
Estimating the tail shape parameter from option prices
In this paper, the author proposes a method to estimate the tail shape parameter of the risk-neutral density.
Model calibration with neural networks
Andres Hernandez presents a neural network approach to speed up model calibration
On empirical likelihood option pricing
This paper investigates the application of the empirical likelihood method in the study of option pricing.
Model-free valuation of barrier options
Austing and Li provide a continuous barrier options pricing formula that fits the volatility smile
Why XVAs need to be factored into options pricing
Ignoring valuation adjustments could be storing up problems for the future
XVA at the exercise boundary
Andrew Green and Chris Kenyon show how the decision to exercise an option is influenced by XVAs