Technical paper/Option pricing
A review of tree-based approaches to solving forward–backward stochastic differential equations
This paper looks at ways of solving (decoupled) forward–backward stochastic differential equations numerically using regression trees.
Black basket analytics for mid-curves and spread options
A new solution to calibrate derivatives with multiple strikes is proposed
Pricing American options under negative rates
This paper derives a new integral equation for American options under negative rates and shows how to solve this new equation through modifications to the modern and efficient algorithm of Andersen and Lake.
Expansion method for pricing foreign exchange options under stochastic volatility and interest rates
This paper applies the smart expansion method to the Heston–Hull–White model, which admits stochastic interest rates to enhance the model, and obtains the expansion formula for pricing options in the model up to second order.
Option pricing using high-frequency futures prices
The authors examine two potential routes to improve the outcome of option pricing: extracting the variance from futures prices instead of the underlying asset prices, and calculating the variance in different frequencies with intraday data instead of…
An end to replication
Convexity adjustments can be valued with an analytical formula, avoiding replication arguments
The CTMC–Heston model: calibration and exotic option pricing with SWIFT
This work presents an efficient computational framework for pricing a general class of exotic and vanilla options under a versatile stochastic volatility model.
Introducing two mixing fractions to a lognormal local-stochastic volatility model
In this paper, the authors introduce two mixing fractions that can be controlled separately to apply impact to the volatility-of-volatility and the correlation in a lognormal LSV model.
Semi-closed-form prices of barrier options in the Hull-White model
New pricer for options with time-dependent barrier shown to be computationally efficient and stable
A new arbitrage-free parametric volatility surface
A new arbitrage-free volatility surface with closed-form valuation and local volatility is introduced
High-order approximations to call option prices in the Heston model
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…
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.
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
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.
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.
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
XVA at the exercise boundary
Andrew Green and Chris Kenyon show how the decision to exercise an option is influenced by XVAs