Model calibration
Deep calibration of rough volatility models
Rough vol models are calibrated and fitted to SPX and Vix smiles
Automatic implicit function theorem
New technique can improve use of adjoint algorithmic differentiation in calibration problems
A new fast local volatility model
A local volatility model based on the Bass construction and alternative to Dupire-style models is introduced
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…
Regularization effect on model calibration
This paper compares two methods to calibrate two popular models that are widely used for stochastic volatility modeling (ie, the SABR and Heston models) with the time series of options written on the Nasdaq 100 index to examine the regularization effect…
What quant finance can learn from a 240-year-old problem
Optimal transport theory offers a data-driven way to calibrate derivatives pricing models
Optimal transport for model calibration
Volatility models and SPX/VIX joint dynamics are calibrated using optimal transport theory
Calibration of rating grades to point-in-time and through-the-cycle levels of probability of default
The paper argues for the need for and importance of the dual calibration of a probability of default (PD) model (ie, calibration to both point-in-time and through-the-cycle PD levels.)
Backtesting of a probability of default model in the point-in-time–through-the-cycle context
This paper presents a backtesting framework for a probability of default model, assuming that the latter is calibrated to both point-in-time and through-the-cycle levels.
Black basket analytics for mid-curves and spread options
A new solution to calibrate derivatives with multiple strikes is proposed
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.
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.
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.
Credit migration: generating generators
A stochastic time change helps the modelling of rating transition
Setting boundaries for neural networks
Quants unveil new technique for controlling extrapolation by neural networks
In search of lost edges: a case study on reconstructing financial networks
In this paper, the authors review the different methods designed to estimate matrixes from their marginals and potentially exogenous information.
Neural networks for option pricing and hedging: a literature review
This paper provides a comprehensive review of the field of neural networks, comparing articles in terms of input features, output variables, benchmark models, performance measures, data partition methods and underlying assets. Related work and…
The quadratic rough Heston model and the joint S&P 500/Vix smile calibration problem
A combination of rough volatility and price-feedback effect allows for SPX-Vix joint calibration
Managing a derivatives portfolio through turbulent markets
Steering a portfolio of non-linear derivatives, such as options and more exotic products, is challenging at the best of times. Market risks change as markets move and time passes, risks offset in complex ways and proxy hedging is common. In this feature,…
The joint S&P 500/Vix smile calibration puzzle solved
SPX and Vix derivatives are modelled jointly in an arbitrage-free setting
Three adjustments in calibrating models with neural networks
New research addresses fundamental issues with ANN approximation of pricing models
Deep learning calibration of option pricing models: some pitfalls and solutions
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
On probability of default and its relation to observed default frequency and a common factor
This paper considers a definition of through-the-cycle as independent from an economic state that can result in a time-varying TTC probability of default.
Complexity reduction for calibration to American options
In this paper, the authors propose and investigate a new method for the calibration to American option price data.