In this post I would like to present examples of visualizations for IFRS 9 using the BI app built in the previous post with Python and atoti: “Tutorial: How to Build an IFRS 9 BI app with Python and atoti”.
Please refer to the previous post for a guide on building a BI app for IFRS 9 analytics, or simply download and run this Jupyter Notebook.
To view the dashboards in the newly built app, run session.url and launch the user interface.
This blog provides a guide on how to address daily credit portfolio monitoring needs, in particular to track and explain expected credit losses for IFRS 9, perform vintage analysis, drill-down to loan-level data, analyze changes between periods and determine the main drivers behind the portfolio risks.
I will furnish you with a step-by-step outline on how to build an IFRS 9 analytical app in Python using a Jupyter Notebook and atoti.
In the post “How to explain non-additive measures” we talked about how to use “parent” and “child” data relationships in atoti to navigate data and implement your on-the-fly allocation rules. Today let’s make use of these atoti functions to define the percentage of parent calculation.
The example I’ll be using is an e-commerce product catalog, and the same technique can be applied to create natural hierarchies with many levels: for example, legal entities structure, regional hierarchies and so on.
In this post, I’d like to show how to quickly create a Tableau-like BI application on top of your data in a Jupyter Notebook using the atoti python module. It can give your users an easy way to:
In the study “2020 State of Data Science — moving from hype towards maturity”, Anaconda discovered that
This article is a part of a series. Check also: Part 1: Pro-rata allocation for a generic example of allocation into additive components and Part 2: Marginal contribution for an incremental analysis example. In this post, we’ll explore a technique that is handy for historical VaR decomposition — the “LEstimated VaR”, aka “Component VaR”.
If you wish to read what non-additive measures are and why we may want to have them decomposed — please refer to the Pro-rata allocation post.
The name “LEstimated VaR” refers to the term “L-estimator” — a linear combination of order statistics — as we’ll be…
In this post, I want to discuss a faster approach to compute the variance-covariance formulae present by regulatory capital models — FRTB SBM and CVA Risk Framework — as well as ISDA SIMM and internal sensitivity-based VaR-type models, and illustrate this approach with a sample implementation of SBM Equity Delta aggregation in atoti. I would like to thank Robert Mouat for sharing his ideas on multi-threading in the FRTB Accelerator and on the matrix formula optimization.
This article is the second part of a series of tutorials about interactive analytics in in atoti’s dynamic pivot tables. Check out Part 1: Pro-rata allocation if you wish to read what non-additive measures are and why we may want to have them decomposed.
This article is the first part of a series of tutorials about interactive analytics in atoti’s dynamic pivot tables. Check also: Part 2: Marginal contribution.
In this post, I will show how you can implement an interactive analytical app for SA-CCR analytics in python using Jupyter Notebook and atoti. You can take this example as a starter, and adapt it to your data model or adjust the calculation logic, for instance, to enable sensitivity-based AddOns.
As a quick reminder, the SA-CCR is a regulatory methodology for computing EAD (Exposure At Default) which is part of the consolidated Basel framework. It is already implemented for financial institutions in Europe by the Regulation (EU) 2019/876 (CRR II) and will be applicable from June 2021 and January 2022…
Anastasia is a quantitative financial analyst and risk management practitioner experienced in modern data analysis tools and frameworks.