The benefits of a centralised valuation engine for derivatives portfolios have become more apparent for banks amid the pandemic and as the final Basel requirements loom.
The 2007-08 global financial crisis (GFC) exposed a lack of visibility into the value of derivative portfolios, which made predicting cashflows and liquidity difficult. At the time, this was seen as a significant threat to the global financial system, one with systemic risk consequences.
Similar concerns were brought to the fore at the height of the Covid-19 pandemic in March and April 2020, when market volatility once again spiked. However, the experience made clear that regulatory reforms introduced in the aftermath of the GFC have largely helped to address concerns about market risk.
Approaches to how banks trade derivatives have significantly changed in the past decade with the adoption of more complex valuation methodologies and reporting requirements. While the reforms have been onerous to implement, banks are now better placed to manage the risks they face.
Notable among the changes was the introduction of a suite of valuation adjustments for derivatives, known collectively as XVAs. This made it possible for banks and regulators to obtain a more complete view of the true values of derivatives portfolios, while also enabling a better understanding of the cost of building and maintaining them.
XVAs have helped to facilitate better trading and risk management decisions by banks, change attitudes towards data management, and promote greater responsibility over the use of capital. Firms are now better able to quantify the risks they face and better understand their exposures at any given point in time.
While the use of XVAs arose out of the GFC, the surge in volatility that financial markets experienced during the early days of the pandemic validated the need for sensitive measurement and monitoring of risk particularly in times of crisis.
Improving risk management
There are three XVAs that banks would most commonly build into the price of a derivatives trade, the credit valuation adjustment (CVA), funding valuation adjustment (FVA) and capital valuation adjustment (KVA).
CVA is the most well-known and considered the most significant of the XVAs. It measures the potential cost that a counterparty default would have on a portfolio of derivatives, drawing from credit risk scores, collateral values, market data on CDS spreads, and other inputs.
CVA measurements enable firms to price the cost of hedging counterparty credit risks over the life of a trade. Notably, Basel III requires banks to include CVA risk in their market risk capital calculations, and add a CVA charge to their minimum capital requirements.
FVA is the expected value of the funding cost associated with entering an uncollateralised derivatives trade, where a bank has to hedge that trade with a collateralised one. This incurs a cost, which is represented by FVA.
KVA is the cost associated with holding regulatory capital against a trade to guard against unexpected credit, market, or operational risk losses.
While calculating these valuation adjustments can be onerous, they help banks to improve risk management and identify transactions that are assumed to be profitable but might actually be loss-making, after taking into account the costs of entering and maintaining a trade.
An approach that has become increasingly more common to make XVA processes more efficient is to stage data – sourced directly from a bank’s systems – in a separate environment, standardising and reconciling the data to ensure accuracy and consistency before calculations are performed.
This approach reduces the amount of time and effort banks must spend sourcing and cleansing data and ensures the analytics that are performed are using the correct information. Notably, it also ensures the same data can be reused for multiple use cases.
For instance, the data can be fed into the financial close process at month-end, allowing for greater operational efficiency and speed, real-time visibility, enhanced control, and fewer errors. In addition, the results from XVA measurements can be fed into business decision making processes.
For example, if a bank finds that their cost of capital has risen, this information can be used to identify which of its businesses should be targeted to ensure return on equity is maintained. Effective XVA modelling practices can also reveal previously hidden costs and allow for the identification of new sources of profit.
Many banks, however, are not able to gain the necessary insights needed to facilitate decision-making or comprehensively evaluate their sensitivities to market events, as they are not yet set up to analyse real-time portfolio data or run high-volume what-if scenarios across their actual and potential derivative portfolios.
Central valuation engine
The pandemic has highlighted a need for faster and more reliable approaches to valuing derivatives portfolios, because of heightened market volatility, increased economic uncertainties, and concerns around credit risk – given that governments and central banks will eventually have to unwind support measures.
Meanwhile, revisions to the CVA risk framework by the Basel Committee on Banking Supervision (BCBS) – once adopted by each national regulator – will force banks to align their CVA modelling approaches to the revised market risk framework (commonly referred to as FRTB) as well as the capital requirements for bank exposures to CCPs.
Rather than build out new systems, or invest the time and effort required to update existing systems, banks have started to recognise the benefits of adopting a central valuation engine as a service from technology providers, ahead of the final Basel framework taking effect from 1 January 2023 (for jurisdictions adopting the BCBS timeline).
Traditionally, financial institutions work in silos and are not always able to streamline information flows across the enterprise. While many banks have enhanced their data management capabilities in recent years, there is now greater urgency to streamline XVA modelling and valuation approaches, ahead of the final Basel requirements coming into force.
A centralised valuation engine is able to draw from a bank’s data across all asset classes and departments, provide the computation facilities necessary to support the capital assessment needs across the enterprise, and deliver full transparency over data lineage and regulatory calculations.
“Banks have been doing valuations for derivatives portfolios for years, so the models that go into these processes are standard,” says Abhishek Srivastava, Product Manager at Oracle Financial Services. “By design, a central valuation engine solves a lot of the problems that come with FRTB, and you eliminate disconnected values across different lines of business.”
Deployed on cloud-based systems, a central valuation engine also means that the large capital outlays that would typically go into building and maintaining on-premise infrastructure are eliminated. According to Srivastava, it is important that cloud services are built according to SaaS principles, where microservices and containerization are used to speed up deployment and enhance performance.
“Using this approach, you don’t just get valuation as an output, you get real-time actionable information on your sensitivities, stress tests, XVAs and CVAs,” Srivastava says. “These should all be available in real time and on demand, because that’s when you can use these numbers in your day-to-day decision making.”
This article was jointly developed by Regulation Asia and Oracle Financial Services. Oracle won Best Solution in Enterprise Risk Management in the Regulation Asia Awards for Excellence 2020.