most citedThe Structural Modelling of Operational Risk via Bayesian inference: Combining Loss Data with Expert Opinions

15 citations · 23 across the 3 of their papers we have counts for

collaborators

5 papers

q-fin.RM2009

A "Toy" Model for Operational Risk Quantification using Credibility Theory

Hans Bühlmann, Pavel V. Shevchenko, Mario V. Wüthrich

To meet the Basel II regulatory requirements for the Advanced Measurement Approaches in operational risk, the bank's internal model should make use of the internal data, relevant e…

q-fin.RM20095 cited

The Quantification of Operational Risk using Internal Data, Relevant External Data and Expert Opinions

Dominik D. Lambrigger, Pavel V. Shevchenko, Mario V. Wüthrich

To quantify an operational risk capital charge under Basel II, many banks adopt a Loss Distribution Approach. Under this approach, quantification of the frequency and severity dist…

q-fin.CP20093 cited

Addressing the bias in Monte Carlo pricing of multi-asset options with multiple barriers through discrete sampling

P. V. Shevchenko

An efficient conditioning technique, the so-called Brownian Bridge simulation, has previously been applied to eliminate pricing bias that arises in applications of the standard dis…

q-fin.RM200915 cited

The Structural Modelling of Operational Risk via Bayesian inference: Combining Loss Data with Expert Opinions

P. V. Shevchenko, M. V. Wüthrich

To meet the Basel II regulatory requirements for the Advanced Measurement Approaches, the bank's internal model must include the use of internal data, relevant external data, scena…

q-fin.RM2009

Dynamic operational risk: modeling dependence and combining different sources of information

Gareth W. Peters, Pavel V. Shevchenko, Mario V. Wüthrich

In this paper, we model dependence between operational risks by allowing risk profiles to evolve stochastically in time and to be dependent. This allows for a flexible correlation…