Semantic Web Technologies The Risk Model Ontology is a framework that aims to represent and categorize knowledge about risk models using semantic web information technologies.
In principle any semantic technology can be the starting point for a risk model ontology. The Open Risk Manual adopts the W3C’s Web Ontology Language (OWL). OWL is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.
Overview of the Julia-Python-R Universe A new Open Risk Manual entry offers a side-by-side review of the main open source ecosystems supporting the Data Science domain: Julia, Python, R, sometimes abbreviated as Jupyter.
Motivation A large component of Quantitative Risk Management relies on data processing and quantitative tools (aka Data Science). In recent years open source software targeting Data Science finds increased adoption in diverse applications. The Overview of the Julia-Python-R Universe article is a side by side comparison of a wide range of aspects of Python, Julia and R language ecosystems.
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.
Reducing variation in credit risk-weighted assets – The benign and vicious cycles of internal risk models March 2016 wasn’t a good month for so called “internal risk models”, the quantitative tools constructed by banks for determining such vital numbers as how much buffer capital is needed to protect the savings of their clients.
First came the Basel Committee’s proposed revision to the operational risk capital framework applicable to banks, next came a similarly fundamental overhaul of what form of risk quantification will be acceptable for calculating credit risk capital requirements.
Top 10 Reasons why Silicon Valley cannot disrupt Wall Street (yet) This is the 2016 update on our independent critical examination of the running meme of “fintech disruption” in the financial services space.
The “Top Ten list of why Silicon Valley cannot Disrupt Wall Street (yet)” was published first here in October 2014
Motivation The possibility, heck inevitability, of “Silicon Valley” (Henceforth abbreviated SV, representing new technology entrants) aiming to “disrupt” Wall Street (Abbreviated WS, representing incumbents) is one of the fascinating memes of our times.
Criteria for identifying simple, transparent and comparable securitisations (See BIS D304)
Our view is that securitisation is fundamental financial technology and there is no intrinsic technical reason why it could not be harnessed to best serve the functioning of modern economies.
We believe, though, that a comprehensive overhaul of historical securitisation practices is the best means of addressing the stigma that has been attached to it in the follow up to the recent financial crisis.
We are happy to publish the first installment of a trilogy that focuses on the risk factors that can turn any credit portfolio toxic.
The first topic is default correlation, a topic that is both core to understanding credit risk and much misunderstood.
Enjoy!
The rationale for continuing with internal capital models in the Basel 3 world Overview of the challenges and opportunities offered by internal capital models (economic capital models) in the post-crisis era. Conference Presentation given at:
Venue: 2nd Annual Capital Modelling under Basel III (Marcus Evans Conference) Location: London Time: January 28th 2014 Link to presentation: Local file
