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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
