Greeks
Automatic implicit function theorem
New technique can improve use of adjoint algorithmic differentiation in calibration problems
Getting the jump on pricing dividend-protected derivatives
Morgan Stanley quants show how to avoid mispricing corporate options and convertible bonds
Reviving the lost art of perturbation for exotic pricing
Natixis quants find novel way to speed up volatility smile modelling
Chebyshev Greeks: smoothing gamma without bias
A numerical method to obtain stable deltas and gammas for complex payoffs is presented
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
Three adjustments in calibrating models with neural networks
New research addresses fundamental issues with ANN approximation of pricing models
Second-order Monte Carlo sensitivities
This paper considers the problem of efficiently computing the full matrix of second-order sensitivities of a Monte Carlo price when the number of inputs is large.
Floating start date for 2020 stress test alarms EU banks
Regulator proposal could lead to less reliable market risk data, critics warn
Deep hedging and the end of the Black-Scholes era
Quants are embracing the idea of ‘model free’ pricing and hedging
Podcast: Hans Buehler on deep hedging and harnessing data
Quant says a new machine learning technique could change the way banks hedge derivatives
JP Morgan turns to machine learning for options hedging
New models sidestep Black-Scholes and could slash hedging costs for some derivatives by up to 80%
Eurostoxx dislocations signal autocall hedging pain
Swings in dividends and volatility reveal year-end stress as European index slump tests “peak vega”
Currency derivatives house of year: BNP Paribas
Risk Awards 2019: French bank hits options windfall in Turkey during currency crash
Vibrato and automatic differentiation for high-order derivatives and sensitivities of financial options
This paper deals with the computation of second-order or higher Greeks of financial securities. It combines two methods, vibrato and automatic differentiation (AD), and compares these with other methods.
Importance sampling applied to Greeks for jump–diffusion models with stochastic volatility
In this paper, the authors develop a procedure to reduce the variance when numerically computing the Greeks obtained via Malliavin calculus for jump–diffusion models with stochastic volatility.
Volatility trap: how gamma roused a market monster
Rates market is exposed to some of the same factors that caused equity volatility to explode in February
Reducing noise is as important as radical change
Quants study ways to reduce noise in XVA Greeks calculations
Pathwise XVA Greeks for early-exercise products
The calculation of XVA Greeks for portfolios with early-exercise products is discussed
Local-stochastic volatility: models and non-models
Lorenzo Bergomi exposes a condition important to the use of LSV models in trading
Sense and sensitivities: Isda Simm is not so simple
Three industry experts argue initial margin calculations for uncleared trades won't work without centralised calibration of sensitivities
The efficient application of automatic differentiation for computing gradients in financial applications
Automatic differentiation is the theme of this paper. The authors show that many functions in calibration and inverse problems, exhibit a natural substitution structure. A significant speedup is achieved compared with common reverse-mode AD.
Isolating a risk premium on the volatility of volatility
Lorenzo Ravagli shows how to exploit a risk premium embedded in the vol of vol in out-of-the-money options
CVA with Greeks and AAD
Reghai, Kettani and Messaoud present new technique to calculate CVA using adjoints
Managing option-trading risk when mental accounting influences prices
This paper explores the implications for risk management of mental accounting of a call option with its underlying.