Technical paper/Stochastic volatility
On the boundary conditions adopted in stochastic volatility option pricing models
The authors recommend boundary conditions that should be adopted when pricing European- and American-style options under the Heston model.
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
Trading the vol-of-vol risk premium
Applications of the vol-of-vol parameter for cross-asset derivatives are presented
Swap rate: cash-settled swaptions in the fallback
A fallback pricing method that reduces vanilla swaptions’ complexity is introduced
Singular exotic perturbation
A solution based on local volatility and sensitivities is proposed to calculate exotics' prices
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…
Optimal transport for model calibration
Volatility models and SPX/VIX joint dynamics are calibrated using optimal transport theory
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.
Sticky varswaps
Bergomi's skew-stickiness ratio is extended to the setting of variance swaps
An artificial neural network representation of the SABR stochastic volatility model
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.
The impact of compounding on bond pricing with alternative reference rates
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.
Explaining credit ratings through a perpetual-debt structural model
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.
The step stochastic volatility model
Extreme short-dated skew can be obtained by decomposing it in two parts
The CTMC–Heston model: calibration and exotic option pricing with SWIFT
This work presents an efficient computational framework for pricing a general class of exotic and vanilla options under a versatile stochastic volatility model.
Numerical techniques for the Heston collocated 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…
Introducing two mixing fractions to a lognormal local-stochastic volatility model
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.
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.
Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations
A new arbitrage-free parametric volatility surface
A new arbitrage-free volatility surface with closed-form valuation and local volatility is introduced