Option pricing
Podcast: Olivier Daviaud on P&L attribution for options
JP Morgan quant discusses his alternative to Greeks decomposition
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
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.
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…