Technical paper/Option pricing
Reconciling P- and Q-calibration with path-dependent volatility
A mixed approach designed to improve calibration robustness is presented
The relative entropy of expectation and price
The replacement of risk-neutral pricing with entropic risk optimisation
Robust financial calibration: a Bayesian approach for neural stochastic differential equations
This paper offers a Bayesian framework for the calibration of financial models using neural stochastic differential equations.
Deep self-consistent learning of local volatility
This paper offers an algorithm for calibrating local volatility from market option prices using deep self-consistent learning, by approximating both market option prices and local volatility using deep neural networks.
Return to the barrier: option pricing and calibration in foreign exchange markets
The authors investigate return barrier options and how failing to capture the market’s implied volatility surface can lead to mispricing of these options.
The power of neural networks in stochastic volatility modeling
The authors apply stochastic volatility models to real-world data and demonstrate how effectively the models calibrate a range of options.
On the boundary conditions adopted in stochastic volatility option pricing models
The authors recommend boundary conditions that should be adopted when pricing European- and American-style options under the Heston model.
A multidimensional transform for pricing American options under stochastic volatility models
The authors put forward a transform-based method for pricing American options which is computationally efficient and accurate under under low-dimensional stochastic volatility models.
Rethinking P&L attribution for options
A buy-side perspective on how to decompose the P&L of index options is presented
Optimal damping with a hierarchical adaptive quadrature for efficient Fourier pricing of multi-asset options in Lévy models
The authors put forward a method for pricing European multi-asset options intended to address challenges related to the choice of damping parameters and the treatment of high dimensionality when designing methods for Fourier pricing options.
Neural variance reduction for stochastic differential equations
This paper proposes the use of neural stochastic differential equations as a means to learn approximately optimal control variates, reducing variance as trajectories of the SDEs are simulated.
The importance of being scrambled: supercharged quasi-Monte Carlo
The authors propose a randomized quasi-Monte Carlo method which outperforms both the Monte Carlo and standard quasi-Monte Carlo methods.
Smile-consistent basket skew
An analytic approximation for the implied volatility surface of basket options is introduced
A robust stochastic volatility model for interest rates
A swaption pricing model based on a single-factor Cheyette model is shown to fit accurately
The quintic Ornstein-Uhlenbeck model for joint SPX and VIX calibration
A new model that jointly fits the smiles of VIX and SPX is presented
Stability and convergence of Galerkin schemes for parabolic equations with application to Kolmogorov pricing equations in time-inhomogeneous Lévy models
In this paper the authors derive stability and convergence of fully discrete approximation schemes of solutions to linear parabolic evolution equations governed by time-dependent coercive operators.
Robust product Markovian quantization
In this paper the authors formulate the one-dimensional RMQ and d-dimensional PMQ algorithms as standard vector quantization problems by deriving the density, distribution and lower partial expectation functions of the random variables to be quantized at…
Automatic differentiation for diffusion operator integral variance reduction
This paper demonstrates applications of automatic differentiation with nested dual numbers in the diffusion operator integral variance-reduction framework originally proposed by Heath and Platen.
Regularization effect on model calibration
This paper compares two methods to calibrate two popular models that are widely used for stochastic volatility modeling (ie, the SABR and Heston models) with the time series of options written on the Nasdaq 100 index to examine the regularization effect…
Probabilistic machine learning for local volatility
In this paper, the authors propose to approach the calibration problem of local volatility with Bayesian statistics to infer a conditional distribution over functions given observed data.
Rainbows and transforms: semi-analytic formulas
In this paper the authors show how the techniques introduced by Hurd and Zhou in 2010 can be used to derive a pricing framework for rainbow options by using the joint characteristic function of the logarithm of the underlying assets.