Option pricing
The relative entropy of expectation and price
The replacement of risk-neutral pricing with entropic risk optimisation
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.
A market-making model for an options portfolio
Vladimir Lucic and Alex Tse fill a glaring gap in European-style derivatives modelling
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.
Quant of the year: Julien Guyon
Risk Awards 2025: Volatility modeller par excellence (and football fan) achieved breakthrough with joint calibration of S&P and Vix options
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.
Podcast: Olivier Daviaud on P&L attribution for options
JP Morgan quant discusses his alternative to Greeks decomposition
Rethinking P&L attribution for options
A buy-side perspective on how to decompose the P&L of index options is presented
FX options traders rethink vol drivers amid macro uncertainty
Market-makers believe more and more events will influence options pricing as political risk bubbles up during 2024
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.
Opra outages cause consternation in options markets
UBS warned clients they were looking at “bad data” on options screens
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.
Skew this: taking the computational burden off basket options
Dan Pirjol presents a snap formula for estimating implied volatility skew in an instant
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
Podcast: Zetocha on mini-futures (not those) and illiquid options
Julius Baer equity quant revels in solving problems for the trading desk
A new approach to marking volatility of illiquid options
Julius Baer quant’s arbitrage-free solution overcomes challenge of sparse data