Podcast: Zetocha on mini-futures (not those) and illiquid options
Julius Baer equity quant revels in solving problems for the trading desk
UK budget gives sterling options traders a wild ride
Reaction to tax cuts sparks most active week for GBP/USD options traders since Brexit negotiations
Pricing barrier options with deep backward stochastic differential equation methods
This paper presents a novel and direct approach to solving boundary- and final-value problems, corresponding to barrier options, using forward pathwise deep learning and forward–backward stochastic differential equations.
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.
Rough volatility moves to exotic frontiers
New simulation scheme clears the way for broader application of the rough Heston model
Range accruals under spotlight as Taiwan prepares for FRTB
Taiwanese banks review viability of products offering options on long-dated rates
Calibration of local-stochastic and path-dependent volatility models to vanilla and no-touch options
In this paper, the authors consider a large class of continuous semi-martingale models and propose a generic framework for their simultaneous calibration to vanilla and no-touch options.
The volatility paradigm that’s stirring up options pricing
‘Rough volatility’ models promise better pricing and hedging of options. But will they catch on?
Solving final value problems with deep learning
Pricing vanilla and exotic options with a deep learning approach for PDEs
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
Pricing multiple barrier derivatives under stochastic volatility
This work generalizes existing one- and two-dimensional pricing formulas with an equal number of barriers to a setting of n dimensions and up to two barriers in the presence of stochastic volatility.
Breaking barriers in options pricing
A new technique for pricing exotic options unifies two classic models
Finite difference schemes with exact recovery of vanilla option prices
A model unifies the classic local vol and binomial trees to accurately price options
Monte Carlo pathwise sensitivities for barrier options
In this work, we present a new Monte Carlo algorithm that is able to calculate the pathwise sensitivities for discontinuous payoff functions.
How Goldman’s algos adapted to virus vol
Interview: Ralf Donner explains why algo usage is up while markets are down
FX options see record volumes as yen goes off-script
Coronavirus outbreak and recession fears trigger frenzied trading in USD/JPY options
Efficient conservative second-order central-upwind schemes for option-pricing problems
In this paper, the authors propose improvements to the approach of Ramírez-Espinoza and Ehrhardt (2013) for option-pricing PDEs formulated in the conservative form.
How old calibration techniques can be applied to exotics pricing
SocGen quants propose technique to more accurately calibrate exotic options
Nervy Korean autocall investors lean on lizards
After volatility surge, buyers give up coupons for better chance of early redemption
Volatility risk structure for options depending on extrema
In this paper, the authors give a decomposition formula to calculate the vega index (sensitivity with respect to changes in volatility) for options with prices that depend on the extrema (maximum or minimum) and terminal value of the underlying stock…
Model-free valuation of barrier options
Austing and Li provide a continuous barrier options pricing formula that fits the volatility smile
Valuation of barrier options using sequential Monte Carlo
The authors present Sequential Monte Carlo (SMC) method for pricing barrier options.
Quantized calibration in local volatility
Quantization is applied to price vanilla and barrier options