Almost every week a new article about how poorly a bank is doing surfaces. Bank X failed the stress test; bank Y is poorly capitalised; or bank Z is facing a huge fine that could jeopardise its balance sheet.
Today, the questions are aimed at Italian banks, again, whereas in previous years the concerns were about Spanish, Greek and French banks, too. With so much information and so many metrics available on the financial stability of banks, it can be very difficult to decide which banks are safe to transact with.
So, how can we best evaluate the economic health of financial counterparties? There is no precise definition of the ‘best bank’ and the definition for each corporate can be different, depending on what type of transactions we want to enter into. The question must, therefore, be rephrased: what is important to us when evaluating banks, and how do we identify and monitor the right risk indicators?
Any risk indicator can be used as a stand-alone measure, but usually multiple indicators should be combined to achieve a more accurate picture of the financial counterparty.
The five important areas when evaluating financial counterparties are:
What about the viability of real-time data on banks versus data reported by banks periodically? This is easier to explain through an example: Traditionally, corporates use a combination of credit ratings, including tier 1 capital ratio and some other capital measures as risk indicators to evaluate banks.
These indicators are generally a good start, since they cover credit quality and capital adequacy, but they are not updated in real time and therefore don’t react quickly when a crisis looms. Therefore, we need a mix of real-time data (the latest view) and periodically reported data (the most recent historical view).
In the selection and sourcing of suitable risk indicators, data quality and availability are key. Below are some guidelines to take into account when looking for the right risk indicators:
1. Data must be available for all or most of the entities of the banks being analysed
Only then can the data be compared or ranked. It is important to make sure that we know which bank entity we are transacting with. A bank subsidiary is not the same as the listed parent company of a banking group in terms of risk.
We need to look at the bank structure and confirm the details with the bank’s relationship manager: is the listed parent company guaranteeing the bank entity or does the entity rely on its own capital to back up its transactions?
Unfortunately, most new risk indicators that are being introduced through new regulations, such as net stable funding ratio, liquidity coverage ratio or total loss-absorption capacity, are not yet published by all banks and their entities.
Moody’s deposit ratings could be a useful indicator to reflect the safety of bank deposits. Unfortunately, their use is limited. Deposit ratings are currently available only for countries that have implemented the EU Bank Recovery and Resolution Directive, such as Germany and the UK.
2. Data should be regularly updated and easily accessible
While there might be enough time during the periodic counterparty reviews to extensively search for data, up-to-date data must be easy to collate when certain events are impacting financial stability.
It is best to have an automatic download from a single database. Note that balance-sheet figures, such as net profit and long-term liabilities, for smaller bank entities are often challenging to find.
Using these guidelines, we can start an iterative process of finding the right risk indicators across the five areas listed above. Unfortunately, there is no shortcut for this process. The challenge is to choose what is relevant for the transaction we are pursuing.
We shouldn’t use deposit ratios as a risk indicator, if we are pursuing a derivatives transaction with an entity that exclusively trades derivatives. A list of easily downloadable risk indicators is usually available from market data providers, such as Bloomberg, Reuters and S&P, etc.
As mentioned, credit quality is our main concern. There are a number of tools available in the market that provide data to assess the credit quality of banks and are a significant enhancement over the raw rating agency ratings.
The Bloomberg credit risk model DRSK is based on the Merton distance-to-default measure, which is a widely recognised methodology to analyse credit risk.
The model uses a mix of real-time and financial data reported by the banks: share price, market capitalisation, price volatility, short-term debt, long-term debt, total debt, loans loss reserve and non-performing loans. Default probabilities are mapped to a credit risk measure scale. This scale is not to be taken as a rating from a rating agency, but as an up-to-date indicator of the financial health of a bank entity.
Overall, we should keep in mind that a limited set of indicators will never comprehensively reflect reality
DRSK also derives implied credit default swap (CDS) curves to assess credit risk. Implied CDS curves are usually available in DRSK for bank entities that have published financial data within the past six months. Since market CDS curves are not always available for all bank entities, the implied CDS curves are a good substitute.
They also have the advantage of being free from market noise, for example, the lack of liquidity of a market CDS curve or the volatility of the underlying stock prices.
Moody’s has a similar tool available. Moody’s KMV calculates Expected Default Frequencies using the market value of the assets, level of firms’ obligations and asset volatility as inputs. S&P’s Capital IQ also calculates probability of defaults using banks’ financial data and macroeconomic factors.
Overall, we should keep in mind that a limited set of indicators will never comprehensively reflect reality. Other controls, beyond the monitoring of quantitative risk indicators, are required. The set of indicators also needs to evolve when market circumstances change or new regulatory requirements arise. The end goal is to achieve peace of mind with respect to our financial counterparties.
Claudia Villasis is a risk manager in group treasury at BHP Billiton.
This article was taken from the February 2017 issue of The Treasurer magazine. For more great insights, log in to view the full issue or sign up for eAffiliate membership