52 citations · 256 across the 29 of their papers we have counts for
10 papers · 1 filter
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…
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…
Cyber Risk Frequency, Severity and Insurance Viability
Matteo Malavasi, Gareth W. Peters, Pavel V. Shevchenko +3
In this study an exploration of insurance risk transfer is undertaken for the cyber insurance industry in the United States of America, based on the leading industry dataset of cyb…
Dependent default and recovery: MCMC study of downturn LGD credit risk model
Pavel V. Shevchenko, Xiaolin Luo
There is empirical evidence that recovery rates tend to go down just when the number of defaults goes up in economic downturns. This has to be taken into account in estimation of t…
Markov chain Monte Carlo estimation of default and recovery: dependent via the latent systematic factor
Xiaolin Luo, Pavel V. Shevchenko
It is a well known fact that recovery rates tend to go down when the number of defaults goes up in economic downturns. We demonstrate how the loss given default model with the defa…