new doors and doing new things,
because we are curious, and curiosity keeps
leading us down new paths.
This is an extensive overview of most of the major changes to Basel II since its original publication. It focuses particularly on the changes brought about by the December 2017 release, especially on credit risk and RWA, changes to both standardised and IRB approaches of calculating RWA and whether the latter is allowed to be used or not. It also discusses the floored inputs. The next section discusses Market Risk and consequently FRTB. It gives a summary of the fundamental changes and then dwells deeper into the specific changes in standardised approach and internal models approach. The last part focuses on Interest Rate Risk in the Banking Book – more specifically on the governance, measuring and modelling of the IR risk and changes to the disclosure requirements. This paper does not address Operational risk as it is addressed elsewhere.
ReadThis article – a courtesy of our partners from Clearstream, focuses on collateral management as a part of derivatives trading, in light of the adoption of EMIR. It discusses the general landscape of markets in OTC and non-OTC derivatives, as well as the main collateral related topics – variation margin and initial margin, but also challenges in exchanging collaterals. There is a sizable list of suggestions on how to deal with said challenges and how to generally move in this landscape and how to navigate the regulatory requirements as well as scramble for liquidity. It ends with a short note on TARGET-2 and the way it has helped to simplify the collateral challenge.
ReadThis Expert input delves into the new EBA definition of default that will apply as of January 2021. It examines who is going to be most impacted by the change and what is going to be the difference in the impact on institutions that use the IRB approach versus institutions that use the Standardised approach. It looks at the most substantial changes in the new definition – like the differences in unlikeliness to pay, past due criterion and the criteria for return to the non-defaulted status. Lastly, it addresses briefly the challenges related to the use of external data – particularly for those institutions that use IRB only.
ReadThis expert input focuses on the validation of Expected Credit loss model validation; more specifically, it explains why it is a good idea now, after the scramble to have IFRS 9 compliant models in time, to consider validation. This input addresses the challenges of a methodological review of all models, and more specifically, it addresses the review of selected variables – macroeconomic factors, obligor characteristics, etc. It shows how to make the best use of the new ability to compare the outcomes of the models against the observed losses. In addition, this article tackles another challenge: the review of data quality – particularly of the modelling data set and new data.
ReadThis Expert input concerns itself with the valuation of Private Equity, an intriguingly difficult topic, but also one well worth exploring, considering just how large a portion of the equity takes the form of a private equity. It shows why the private equity is an interesting prospect for investors and in what way the independence of the valuation is desirable and demanded by regulators. It also depicts how investing in private equity is often conducted and what such an approach entails for the investor. Lastly, it depicts various techniques used to estimate a private equity value and how the preferred technique is chosen depending on the information available.
ReadThis Expert input works as an overview of the basis of most available machine learning techniques and serves as a great stepping stone to get into the intricate world of ML. Whilst mostly written with credit risk in mind – offering some advice for the use of machine learning to help us model Expected credit loss and its components (PD, LGD, EAD), the list of the potential techniques depicted steps far beyond this relatively singular use and examines a multitude of approaches, ranging from Supervised ML (Decision trees, Artificial Neural networks, etc.) through ensemble ML (Random forests, Gradient boosting), to unsupervised ML (Deep learning, Clustering methods, etc.). The input concludes with several general tips and tricks regarding machine learning.
ReadThis Expert input gives a basic overview of IFRS 17 and how it is going to be different from the current standards (IFRS 4). We note that there are some similarities between some of the aspects of Solvency II and IFRS standards and examine them, as well as the differences. Furthermore, there are predictions on how the standards are going to influence the operations of insurers and their contracts. Since the insurers are likely to implement IFRS 17 in concert with IFRS 9, the expert input also focuses on how this joint implementation is likely to proceed, what the main challenges are and why it is ultimately a good decision to implement them together.
ReadThis expert input addresses PSD 2 and the possibly underappreciated impact it could have on (and arguably beyond) financial services, opening many hitherto closed doors. It looks at the regulatory progress all the way from PSD 1 to PSD 2. It notes that the development of the Single Euro Payments Area – SEPA – would not have been possible without PSD 1, and an analogous paradigm shift may be expected with PSD 2. The article notes that PSD 2 may lead to the emergence of new and a separation of old services in the hands of innovative FinTech companies, though the established players still hold a very strong position to compete, if only they adjust to the new reality.
ReadThis Expert input addresses the EMIR-related requirement of keeping a variation and initial margin; more specifically, the initial Margin is the main focus of the article. It lists the advantages of using the scheduled-based approach but shows that the margin requirements using this methodology may be a bit too steep. It further examines the possibility of internal models and concludes that their development could be just too cumbersome for most. It proposes ISDA SIMM as having the advantage of simplicity together with lower (or at least more realistic) requirements. The article finishes with comparing ISDA SIMM with the sensitivity-based approach for FRTB.
ReadThis expert input follows the release of a discussion paper on the use of big data by financial institutions published by the Joint Committee of the European Supervisory Authorities. It examines the general data-related regulatory framework that financial institutions are facing or will face in the future, all the way from very general requirements (GDPR) to requirements specific to financial services (EMIR & MiFID II). The paper also discusses the potential uses of big data by financial institutions and how it would influence the operations of those institutions, their potential costs and benefits. The expert input concludes with our evaluation of the possibilities arising from big data as well as our projection of the regulatory future regarding this topic.
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