Balance Sheet Optimization: Can you do it?
A lot has been tried and written on the subject but rare who have been able to achieve meaningful results.
Thank you for Paola Brahimcha for the help
This paper approaches this topic from a different angle, the idea is to understand how components of a balance sheet react under a stressed environment and how the global Profit & Loss is impacted.
This is a holistic approach where Market Risk, Credit Risk, Liquidity Risk, Processing Risk, Margin Requirements and Regulatory Charges will be covered. These are all factors impacting the P&L i.e. the Balance Sheet.
The P&L is the heart of a Balance Sheet Optimization.
It is important to note that I will not try to solve the whole problem of the Balance Sheet Optimization, but more providing good building blocks using the existing technology found in the Capital Market business to achieve a very serious framework to optimize the Profit & Loss and understand the impact on the balance sheet.
Let us first understand what is Balance Sheet Optimization in simple terms (for the common mortals), a simple and complete description found on the web:
Balance Sheet Optimization helps financial institutions determine what to do with their assets to better meet regulatory requirements and achieve higher profitability.
Typical users include investment banks with large derivatives portfolios.
Basel III, EMIR, Dodd-Frank… regulations have imposed higher capital ratios and reduced the risks that banks can take. But reducing the number of assets and the risk that banks take reduces the return on equity, putting pressure on profits and the ability to deliver returns to shareholders. To meet these capital requirements, banks must determine which trades to clear, crush, offset, or exit.
Balance Sheet Optimization determines what to do with existing trades to meet regulatory requirements and maintain profitability.
I identified 5 pillars that can play into the Balance Sheet Optimization solution. There are shown below:
To understand how these pillars interact together, let us draw a high-level bank’s capital market functions heat map:
The chevrons highlighted in red are the ones that will largely affect the P&L (yellow chevron).
The process starts by:
Identifying the tradable (tangible) asset classes impacting the balance sheet, for example:
Some of the products are not typical traded financial instruments per say, hence the need to find a proxy financial product that is close enough to represent this type of investments (A synthetic 'pool' representation of activities). As an example, a bond can be used as a proxy to represent a real-estate investment …
Once the asset classes are identified and mapped to their proxies we need a good pricing and simulation engines to re-price these products.
We then classify the products (collateralized or not, liquid [high, medium, low], strategic / non-strategic investments…) and identify their corresponding pricing model (or use a more generic pricing model like an American Monte-Carlo) with the required ROI per activity (if possible). No need to go nuclear on pricing models, just keep it simple and fast, at the end it is just an approximation.
The input for these engines will be:
Product description (enough to price the product)
Shocked market data per a pre-defined stress scenario (ex. Brexit) using the “What-if” Market-Data simulation engines: