A new arbitrage-free volatility surface with closed-form valuation and local volatility is introduced
In the present paper, a decomposition formula for the call price due to Alòs is transformed into a Taylor-type formula containing an infinite series with stochastic terms. The new decomposition may be considered as an alternative to the decomposition of…
Risk-neutral densities: advanced methods of estimating nonnormal options underlying asset prices and returns
This work expands the analysis in Cooper (1999) and Santos and Guerra (2014), and the performance of the nonstructural models in estimating the "true" RNDs was measured through a process that generates "true" RNDs that are closer to reality, due to the…
Overhauling pricing models could reap rewards even if prices don’t cross zero again
Model tuned to negative prices has implications for pricing, margining and delta hedging
New tool aims to gauge wider cost of virus control measures
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.
New research addresses fundamental issues with ANN approximation of pricing models
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
A variation of the rough volatility model is introduced by plugging in a different stochastic process
EBA options for lighter capital treatment of parametric curves could prove impractical
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.
This paper analyzes the efficiency of hedging strategies for stock options in the presence of jump clustering.
Traders focusing on new dates – and scenarios – after domestic UK criticism of proposed deal
Deep learning techniques are being explored by the quants to speed up exotics pricing
This paper develops efficient importance sampling schemes for a class of jump–diffusion processes that are commonly used for modeling stock prices.
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.
Barclays executives explore weaknesses of current pricing formulas for cash-settled swaptions
New machines have big potential in AI, valuations and VAR, but tech giants like IBM need help from practitioners
High-dimension problems can be solved with discretisation techniques
Quants develop model that fixes a longstanding problem with pricing American options
In this paper, the author proposes a method to estimate the tail shape parameter of the risk-neutral density.
Andres Hernandez presents a neural network approach to speed up model calibration
This paper investigates the application of the empirical likelihood method in the study of option pricing.