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Managing xVAs: Why legacy technology systems are feeling the strain

03 November 2017 Allan Cowan, Ph.D.

Derivatives pricing has changed dramatically over the past decade. Once seen as a task in pricing cash flows - albeit often for complex payoffs - it is now commonplace to consider the impact the trade has on the bank's balance sheet when coming up with a price. This entails pricing in the costs of credit risk, funding, collateral/initial margin, and capital. To do this properly, banks' derivative pricing engines must be expanded to capture not only the market risk factors affecting the payoff but also the credit quality of the parties, the banks' funding structure, types of collateral posted and capital requirements. Many seemingly separate issues need to be considered holistically and consistently.

The CVA losses banks experienced during the credit crisis of 2007/2008 illustrated the need for banks to price in these credit losses and properly manage them by hedging. In addition, the liquidity squeeze experienced at the time drastically increased the funding costs for banks. This resulted in the birth of a Funding Valuation Adjustment (FVA) - the cost of funding the unsecured exposure. It soon became apparent that there can be a significant cost of holding a derivatives portfolio, and it should be recognized and managed upfront in order for the bank to manage liquidity.

Another consequence of the credit crisis was that regulators vowed to do more to strengthen the banking system to avoid another crisis. A new CVA capital charge and leverage ratio charge was introduced as were updates to Counterparty Credit Risk capital and Market Risk capital. The capital requirements for banks are increasing due to these reforms and as such banks' appetite to price in the cost of capital and manage the return on capital has grown. The cost of this capital, known as Capital Valuation Adjustment (KVA), has thus become a key ingredient in derivatives pricing.

Another reform was to promote the use of Central Counterparties (CCPs) as clearing agents for derivatives. One method used by CCPs to reduce bilateral risk is to collect Initial Margin from all members, which is then available to cover losses upon member default. To level the costs between cleared and non-cleared trades, regulators have introduced a bilateral Initial Margin charge between counterparties, which is currently being phased in. As such, most derivative trades (cleared or not) are now subject to the costs of funding Initial Margin. The cost of funding Initial Margin has become known as Margin Valuation Adjustment (MVA).

Collectively these Valuation Adjustments are known as xVAs. While the drivers for these xVAs are clear, the task of accurately calculating and managing them can be more challenging. Once calculated, the goal of xVA or resource management desks is to optimize them in order to reduce the balance sheet costs of the derivatives business. This drives more complexity and requires analysis of the connection between these adjustments.

A recent webinar we held with Dr. Jon Gregory discussed the issues of xVA calculation and optimization. One particular challenge is that xVA is no longer a trade level valuation but, in the most general sense, must consider the bank's entire balance sheet. While some measures like CVA can be computed at the netting set level, measures like asymmetric FVA require a calculation that spans the entire derivatives portfolio, while the KVA incorporating the leverage ratio would need to take into account the full balance sheet.

Calculating xVAs at the portfolio or balance sheet level requires a robust enterprise-level system that can efficiently simulate all risk factors and price all trades of the portfolio in a consistent manner. The aggregation of these simulated trade valuations also pushes the memory and performance requirements of hardware being used. Some banks' xVA systems may be designed to work counterparty by counterparty, as historically that was the area of focus for CVA. With the portfolio-wide requirement of some of the newer xVAs, some banks are looking to big data technologies to complete the task.

An additional looming cost for banks is the new CVA capital charge under FRTB (Fundamental Review of the Trading Book). As discussed in an earlier article, the incremental impact of moving from the standard Basel 3 CVA charge with EAD computed with CEM to the new basic approach using the SA-CCR EAD can be significant (a factor of 2 to 4). This is motivating many banks to set up an appropriate CVA desk in order to qualify for the SA-CVA approach under FRTB. Even if a bank qualifies for SA-CVA, the capital requirements of this regulation are still expected to be higher than what banks have currently. As such, the question many xVA traders are asking is: How can I optimize SA-CVA capital?

Some xVA system requirements needed for SA-CVA optimization are discussed in the webinar. A granular breakdown of the risk factors driving the capital provides insight into how to optimize. But an additional critical component is a system that allows banks to compute the pre-trade incremental change in SA-CVA. This allows for traders to do pre-trade what-if checks on the impact of a given trade or hedge before it is executed. Such deal-time decision tools enable deal-time optimization of the capital along with other xVAs.

Incorporating xVAs into the pricing of derivative portfolios has pushed complexity from the pricing of exotics to the incorporation of portfolio and balance sheet wide effects. The industry must adapt its processes and systems to accommodate these calculations. Our webinar serves to illustrate the issues and offers some solutions to dealing with these new xVAs.

Posted 03 November 2017 by Allan Cowan, Ph.D., Global Head, Data Analytics, Financial Risk Analytics, IHS Markit



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