Implementation

Python versus R Language: A side by side comparison for quantitative risk modeling

Python versus R Language: A side by side comparison for quantitative risk modeling

Reading Time: 3 min.
Motivation for the comparison: A large component of risk management relies on data processing and quantitative tools. In turn, such information processing pipelines and numerical algorithms must be implemented in computer systems. Computing systems come in an extraordinary large variety but in recent years open source software finds increased adoption for diverse applications (machine learning, data science, artificial intelligence). In particular cloud computing environments are primarily based on open source projects at the systems level.
Version 0.2 of the Open Risk API incorporates the standardized EBA portfolio data templates

Version 0.2 of the Open Risk API incorporates the standardized EBA portfolio data templates

Reading Time: 2 min.
Extending the Open Risk API to include the EBA Portfolio Data Templates: The Open Risk API provides a mechanism to integrate arbitrary collections of risk data and risk modelling resources in the context of assessing and managing financial risk. It is based on two key technologies of the modern Web, RESTful architectures and Semantic Data. OpenCPM, the credit portfolio management platfrom we launched recently fully integrates the latest versions of the Open Risk API.
The Zen of IFRS 9 Modeling

The Zen of IFRS 9 Modeling

Reading Time: 6 min.
The Zen of IFRS 9 Modeling: At Open Risk we are firm believers in balancing art and science when developing quantitative risk tools. The introduction of the IFRS 9 and CECL accounting frameworks for reporting credit sensitive financial instruments is a massive new worldwide initiative that relies in no small part on quantitative models. The scope and depth of the program in comparison with previous similar efforts (e.g. Basel II) suggests that much can go wrong and it will take considerable time, iterations, communication and training to develop a mature toolkit that is fit-for-purpose.
Open Risk API

Open Risk API

Reading Time: 3 min.
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).
The Zen of Modeling

The Zen of Modeling

Reading Time: 1 min.
Risk modeling is as much art as it is science: The Zen of Modeling aims to capture the struggle for risk modeling beauty An undocumented risk model is only a computer program A risk model that cannot be programmed is only a concept A risk model only comes to life with empirical validation Correct implementation of an imperfect model is better than wrong implementation of a perfect model In complex systems there is always more than one path to a risk model There are no persistently true models but there are many persistently wrong models Correlation is imperfectly correlated with causation Nirvana is the simplest model that is fit for purpose Hierarchical systems lead to hierarchical models.
Top Ten Reasons Why Open Source is the Future of Risk Modeling

Top Ten Reasons Why Open Source is the Future of Risk Modeling

Reading Time: 2 min.
Financial Risk Modelling has suffered enormous setbacks in recent years, with all major strands of modelling (market, credit, operational risk) proven to have debilitating limitations. It is impossible to imagine a modern financial system that does not make extensive use of risk quantification tools, yet rebuilding confidence that these tools are fit-for-purpose will require significant changes. These need to improve governance, transparency, quality standards and in some areas even the development of completely new strands of modelling.