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.
Austing and Li provide a continuous barrier options pricing formula that fits the volatility smile
Ignoring valuation adjustments could be storing up problems for the future
Andrew Green and Chris Kenyon show how the decision to exercise an option is influenced by XVAs
The authors provide a bound for the error committed when using a Fourier method to price European options, when the underlying follows an exponential Lévy dynamic.
In this paper the authors present an efficient convergent lattice method for Asian option pricing with superlinear complexity.
The authors present Sequential Monte Carlo (SMC) method for pricing barrier options.
A reduced basis method for parabolic partial differential equations with parameter functions and application to option pricing
The authors introduce an RB space–time variational approach for parametric PPDEs with coefficient parameters and a variable initial condition.
Peter Austing introduces an analytic or semi-analytic valuation of basket options
This paper develops a new scheme for improving an approximation method of a probability density function.
Alexander Passow presents a portfolio performance measure that combines the omega measure with Johnson distributions
The authors propose stratified approximations of option prices using the gamma and lognormal distributions, with an application to bond pricing in the Dothan model.