Python

Release of version 0.3 of the Concentration Library

Release of version 0.3 of the Concentration Library

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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.
Transition Matrix Library First Release

Transition Matrix Library First Release

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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

Loan Level Templates Using Python

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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?

How much digital bank can we fit in a 50 euro bill?

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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!
Google Summer of Code Ideas List Page

Google Summer of Code Ideas List Page

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Google Summer of Code Ideas List Page: Over the course of the years we have seen many an open source project that we love and use daily participate as mentoring organizations in Google’s great communal activity. This year Open Risk applied to join the effort to promote open source, in particular as it applies in the less visited area of financial risk management. The following is a list of ideas for projects where students can participate (subject to us getting approved as mentoring organization!
Open Source Risk Data with MongoDB and Python

Open Source Risk Data with MongoDB and Python

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Open Source Risk Data with MongoDB and Python: Open source software is all the rage those days in IT and the concept is making rapid inroads in all parts of the enterprise. An earlier comprehensive survey by Gartner, Inc. found that by 2011 more than half of organizations surveyed had adopted open-source software (OSS) solutions as part of their IT strategy. This percentage may have currently exceeded the 75% mark according to open source advisory firms.
Open Source Risk Modeling Manifesto

Open Source Risk Modeling Manifesto

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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.