Model calibration
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.
Validation of the backtesting process under the targeted review of internal models: practical recommendations for probability of default models
This paper provides practical recommendations for the validation of the backtesting process under the targeted review of internal models (TRIM).
The extended SSVI volatility surface
This paper extends Gatheral and Jacquier’s surface stochastic volatility-inspired (SSVI) parameterization by making the correlation maturity dependent and obtaining the necessary and sufficient conditions for no calendar-spread arbitrage.
Data shortage hits margin models for Asia banks
Thin trade volumes in local derivatives threaten to undermine key tests for initial margin models
How old calibration techniques can be applied to exotics pricing
SocGen quants propose technique to more accurately calibrate exotic options
Equity modelling with local stochastic volatility and stochastic discrete dividends
SocGen quants calibrate local stochastic volatility models with stochastic dividends
Swaptions vol modelling tweak opens up pricing possibilities
Nomura quant proposes local volatility model that can directly calibrate to swaption smiles
The swap market model with local stochastic volatility
An easy to calibrate and accurate swap market model is proposed
Podcast: Callegaro, Fiorin and Grasselli on quantization
High-dimension problems can be solved with discretisation techniques
American quantized calibration in stochastic volatility
Fiorin, Callegaro and Grasselli show how discretisation methods reduce computing time in high-dimensional problems