Bayesian modelling
Multiperiod portfolio selection and Bayesian dynamic models
Kolm and Ritter present a multiperiod, multi-asset selection model with transacion costs, kept computationally tractrable
Hit the floor: banks fear Basel curbs for capital models
Regulators argue a backstop is needed to avoid too-low modelled numbers
Scenario analysis key for more efficient modelling
Assessing exposures and vulnerabilities gives sophisticated risk view
A credit value adjustment scheme for bank loan portfolios
In this study the authors develop an analytical scheme that integrates a large spectrum of typical bank loans and credits, accommodates common bank loan portfolio chronological interdependencies and allows the necessary credit value adjustments (CVAs)…
Paper of the year: Bakhodir Ergashev, Stefan Mittnik and Evan Sekeris
Paper focuses on dealing with sparse data
Portfolio construction and systematic trading with factor entropy pooling
Construction of large portfolios consistent with investors' views and stress test scenarios is a challenging task, considering the volume of information to be processed. Attilio Meucci, David Ardia and Marcello Colasante introduce a technique that…
Stress testing with fully flexible causal inputs
Stress testing with fully flexible causal inputs
Using credibility analysis in operational risk measurement
Continental differences, revisited
The Atlantic divide over scenario analysis
Continental differences
Bayesian lessons for payout structuring
Bayesian lessons for payout structuring
New op risk system patent, using Bayesian networks, awarded to IBM
Patent project spearheaded by Jonathan Rosenoer is one of a very few op risk system patents to be awarded
An operational model
Scarce and shallow loss data has been the bane of operational risk models historically, but a new paper calls for more work on statistical approaches that could improve their sensitivity. By Peter Madigan
Book extract > Integration of Qualitative and Quantitative Operational Risk Data: A Bayesian Approach
The aim of this chapter is to provide a Bayesian model thatallows us to manage operational risk and measure internally thecapital requirement, compliant with the Advanced MeasurementApproaches (AMA) recommended by Basel Committee on BankingSupervision …
Op risk assessment hampered by lack of reliable data
Inadequacy of loss expectation data is a major cause of modelling risk for operational risk strategies, said Carol Alexander, professor of the ISMA Centre at the University of Reading in the UK, at Risk's annual European conference.
Roundtable > Operational risk quantification: a discipline at a crossroads
Operational Risk magazine held its first European roundtable during Risk magazine's annual European congress, in Paris, on April 9.