52 citations · 256 across the 29 of their papers we have counts for
38 papers
On the approximation of posterior laws in compound loss models by conditional Wasserstein GANs
Aleksandar Arandjelovic, Pavel V. Shevchenko, George Tzougas
Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior specifications. Outside conjugate cases, this may require repeated nu…
Multi-Factor Polynomial Diffusion Models and Inter-Temporal Futures Dynamics
Peilun He, Nino Kordzakhia, Gareth W. Peters +1
In stochastic multi-factor commodity models, it is often the case that futures prices are explained by two latent state variables which represent the short and long term stochastic…
State-Space Dynamic Functional Regression for Multicurve Fixed Income Spread Analysis and Stress Testing
Peilun He, Gareth W. Peters, Nino Kordzakhia +1
The Nelson-Siegel model is widely used in fixed income markets to produce yield curve dynamics. The multiple time-dependent parameter model conveniently addresses the level, slope,…
The Nature of Losses from Cyber-Related Events: Risk Categories and Business Sectors
Pavel V. Shevchenko, Jiwook Jang, Matteo Malavasi +3
In this study we examine the nature of losses from cyber related events across different risk categories and business sectors. Using a leading industry dataset of cyber events, we…
Cyber Loss Model Risk Translates to Premium Mispricing and Risk Sensitivity
Gareth W. Peters, Matteo Malavasi, Georgy Sofronov +3
We focus on model risk and risk sensitivity when addressing the insurability of cyber risk. The standard statistical approaches to assessment of insurability and potential misprici…
Importance sampling for option pricing with feedforward neural networks
Aleksandar Arandjelović, Thorsten Rheinländer, Pavel V. Shevchenko
We study the problem of reducing the variance of Monte Carlo estimators through performing suitable changes of the sampling measure which are induced by feedforward neural networks…