Model calibration
Neural joint S&P 500/VIX smile calibration
A one-factor stochastic local volatility model can solve the joint calibration problem
Automatic adjoint differentiation for special functions involving expectations
The authors put forward AAD algorithms for functions involving expectations and use their technique to calibrate European options.
BoE model risk rule may drive real-time monitoring of AI
New rule requires banks to rerun performance tests on models that recalibrate dynamically
A robust stochastic volatility model for interest rates
A swaption pricing model based on a single-factor Cheyette model is shown to fit accurately
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
Taking the measure of CMS pricing
Bank of America quants propose comprehensive framework for modelling rate derivatives
Calibration alternatives to logistic regression and their potential for transferring the statistical dispersion of discriminatory power into uncertainties in probabilities of default
This paper compares four calibration approaches to linear logistic regression in credit risk estimation and proposes two new single-parameter families of differentiable functions as candidates for this regression.
Rising star in quantitative finance: Sigurd Emil Rømer
Risk Awards 2023: Doctoral dissertation outlines more efficient way to simulate rough volatility models
Margin for non-cleared European energy trades to jump 80%
Annual recalibration of Simm could catapult some energy firms over relief thresholds
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