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.