In this paper the universal approximation theorem of artificial neural networks (ANNs) is applied to the stochastic alpha beta rho (SABR) stochastic volatility model in order to construct highly efficient representations.
This paper looks at the impact of compounding on zero-coupon bond prices by considering the short rate when it follows a Gaussian diffusion process or a stochastic volatility jump-diffusion process.
Forecasting stock market volatility: an asymmetric conditional autoregressive range mixed data sampling (ACARR-MIDAS) model
This paper proposes an extension of the classical CARR model, the ACARR-MIDAS model, to model volatility and capture the volatility asymmetry as well as volatility persistence.
This paper calibrates a perpetual-debt structural model (PDSM) by using Moody’s historical credit ratings.
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.
Extreme short-dated skew can be obtained by decomposing it in two parts
This work presents an efficient computational framework for pricing a general class of exotic and vanilla options under a versatile stochastic volatility model.
In this paper, the authors discuss all aspects of derivative pricing under the Heston–CLV model: calibration with an efficient Fourier method; a Monte Carlo simulation with second-order convergence; and accurate partial differential equation pricing…
In this paper, the authors introduce two mixing fractions that can be controlled separately to apply impact to the volatility-of-volatility and the correlation in a lognormal LSV model.
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.
A derivative pricing approximation method using neural networks and AAD speeds up calculations
A new arbitrage-free volatility surface with closed-form valuation and local volatility is introduced
In the present paper, a decomposition formula for the call price due to Alòs is transformed into a Taylor-type formula containing an infinite series with stochastic terms. The new decomposition may be considered as an alternative to the decomposition of…
This paper develops two local mesh-free methods for designing stencil weights and spatial discretization, respectively, for parabolic partial differential equations (PDEs) of convection–diffusion–reaction type.
Thomas Roos presents the expressions for the implied volatilities of European and forward starting options
New paper by Nomura quant applies volatility model used in equities to exotic rate hedging
The combination of two popular volatility models sharpens the hedging of exotic rate derivatives
Risk Awards 2020: New machine learning techniques bring ‘rough volatility’ models to life
A variation of the rough volatility model is introduced by plugging in a different stochastic process
The main goal of this paper is to perform a comprehensive nonparametric jump detection model comparison and validation. To this end, the authors design an extensive Monte Carlo study to compare and validate these tests.
In this paper, the authors construct a Heath-Platen-type Monte Carlo estimator that performs extraordinarily well compared with the crude Monte Carlo estimation.
El Euch, Rosenbaum, Gatheral combine a rough volatility model with the classical Heston model
In this paper, the authors give a preprocessing step for Fourier methods that involves projecting the Green’s function onto the set of linear basis functions.
In this paper, the author describes a simple adaptive Filon method that performs better and more accurately than various popular alternatives for pricing options under the Heston model.