Quants unveil new technique for controlling extrapolation by neural networks
In this paper, the authors review the different methods designed to estimate matrixes from their marginals and potentially exogenous information.
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…
A combination of rough volatility and price-feedback effect allows for SPX-Vix joint calibration
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,…
SPX and Vix derivatives are modelled jointly in an arbitrage-free setting
New research addresses fundamental issues with ANN approximation of pricing models
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
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.
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).
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.
Thin trade volumes in local derivatives threaten to undermine key tests for initial margin models
SocGen quants propose technique to more accurately calibrate exotic options
SocGen quants calibrate local stochastic volatility models with stochastic dividends
Nomura quant proposes local volatility model that can directly calibrate to swaption smiles
An easy to calibrate and accurate swap market model is proposed
High-dimension problems can be solved with discretisation techniques
Fiorin, Callegaro and Grasselli show how discretisation methods reduce computing time in high-dimensional problems
Quants develop model that fixes a longstanding problem with pricing American options
This paper proposes a nonparametric local volatility Cheyette model and applies it to pricing interest rate swaptions.
This paper introduces a local volatility model for the valuation of options on commodity futures by using European vanilla option prices.
Andres Hernandez presents a neural network approach to speed up model calibration