Mark Spitznagel won’t reveal how he made a 4,144% return, but he does discard plenty of rival trades
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.
Technique aims to lower initial margin calls in times of stress without sacrificing risk sensitivity
This research develops a new fast and accurate approximation method, inspired by the quadratic approximation, to get rid of the time steps required in finite-difference and simulation methods, while reducing error by making use of a machine learning…
‘Rough volatility’ models promise better pricing and hedging of options. But will they catch on?
Dealers say CME, Cboe settlement time shift for S&P 500-linked options causes risk management headache
This paper provides an efficient and accurate hybrid method to price American standard options in certain jump-diffusion models and American barrier-type options under the Black–Scholes framework.
Differential machine learning produces results “thousands of times faster and with similar accuracy”
In this paper, the authors investigate a nonlinear generalization of the Black–Scholes equation for pricing American-style call options, where the volatility term may depend on both the underlying asset price and the Gamma of the option.
SocGen quant uses deep learning technique to optimise collateral substitution
In this paper, the authors investigate a path-dependent American option problem and provide an efficient and implementable numerical scheme for the solution of its associated path-dependent variational inequality.
In this paper, the authors propose and investigate a new method for the calibration to American option price data.
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.
In this paper, the authors construct strategies for an American option portfolio by exercising options at optimal timings with optimal weights determined concurrently.
Research on AAD is not complete until it becomes easier to implement, says quant
High-dimension problems can be solved with discretisation techniques
Fiorin, Callegaro and Grasselli show how discretisation methods reduce computing time in high-dimensional problems
In this paper, the authors study a hybrid tree/finite-difference method, which allows us to obtain efficient and accurate European and American option prices in the Heston–Hull– White and Heston–Hull–White2d models.
Quants develop model that fixes a longstanding problem with pricing American options
De Marco and Henry-Labordère provide an approximation of American options in terms of the local volatility function
Ignoring valuation adjustments could be storing up problems for the future
This paper presents a high-performance spectral collocation method for the computation of American put and call option prices.
The authors propose a novel method for efficiently comparing the performance of different stopping times.
Time constraints can be binding for ‘heavy’ Monte Carlo calculations of risk analytics – value-at-risk, potential future exposure, credit valuation adjustment – in intraday risk monitoring, so fast approximations are sometimes preferred. Vladislav…