Release of version 0.3 of the Concentration Library: Further building out the OpenCPM set of tools, we release version 0.3 of the Concentration Library. This python library for the computation of various concentration, diversification and inequality indices. The below list provides documentation URL’s for each one of the implemented indexes Atkinson Index Concentration Ratio Berger-Parker Index Herfindahl-Hirschman Index Hannah-Kay Index Gini Index Theil Index Shannon Index Generalized Entropy Index Kolm Index The image illustrates a simple use of the library where the HHI and Gini indexes are computed and compared for a range of randomly generated portfolio exposures.
Motivation for Building an open source database based on EBA’s Standardized NPL Templates In a recent insightful piece “Overcoming non-performing loan market failures with transaction platforms”, Fell et al. dug deeply into the market failures that help perpetuate the NPL problem. They highlight, in particular, information asymmetries and the attendant costs of valuing NPL portfolios as key obstacles. In the same wavelength, the European Banking Authority published standardized NPL data templates as a step towards reducing the obstacles that prevent the reduction of NPL’s.
Open Risk released version 0.1 of the Transition Matrix Library Motivation: State transition phenomena where a system exhibits stochastic (random) migration between well defined discrete states (see picture below for an illustration) are very common in a variety of fields. Depending on the precise specification and modelling assumptions they may go under the name of multi-state models, Markov chain models or state-space models. In financial applications a prominent example of phenomena that can be modelled using state transitions are credit rating migrations of pools of borrowers.
Loan Level Templates Using Python: In this Open Risk Academy course we figure step by step how to use python to work with Loan Level Templates, using the ECB SME template as an example. Overview of the loan level template Manipulating spreadsheets with Python The Python Dictionary Organization of Portfolio Data Generating Test Portfolios Get an Open Risk Academy account and get started with the course here
How much digital bank can we fit in a 50 euro bill? Much has been said about the impact of Big Data and high-end GPU computing on the provision of digital financial services. At Open Risk we wanted to explore the boundary of what is possible at the diametrically opposite end of the cost spectrum: What is the absolutely minimum cost for providing digital financial services? . In this post we begin the journey of finding out the answer to that question and it promises to be fascinating!
Open Risk API: If you work in financial risk management you will most likely recognize where the following sentence is coming from: One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks information technology (IT) and data architectures were inadequate to support the broad management of financial risks. This had severe consequences to the banks themselves and to the stability of the financial system as a whole For those lucky few risk managers not being affected by inadequate IT systems, the excerpt is from the Basel Committee’s Principles for effective risk data aggregation and risk reporting (2013).
Revisiting simple concentration indexes: Our white paper Revisiting simple concentration indexes reviews the definitions of widely used concentration metrics such as the concentration ratio, the HHI index and the Gini and clarify their meaning and relationships. This new analytic framework helps clarify the apparent arbitrariness of simple concentration indexes and brings to the fore the underlying unifying concept behind these metrics, thereby enabling their more informed use in portfolio and risk management applications.
Open Source Risk Modeling Manifesto: This post is a summary of a presentation given at the 2014 Autumn TopQuants Meeting, aka, the Open Source Risk Modeling Manifesto. The dismal state of quantitative risk modeling The current framework of internal risk modeling at financial institutions has had a fatal triple stroke. We saw in quick sequence: market risk, operational risk, and credit risk measurement failures, covering practically all business models. This fact left the science and art of quantitative risk modeling reeling under the crushing weight of empirical evidence.