Central risk and the provision of capital
Development in central risk desks remains
focused across a quorum of Global
multi asset market makers, mirroring
the evolution of buy side automation to
develop a ‘one click’ centralised approach
to risk management.
Centralised risk unwind continues to
remain a sell side priority for those houses
capable of delivering a technology
framework sufficient to manage the
breadth and depth of this mammoth task.
The development of multi-desk integrated
risk management, risk transfer capabilities,
optimisation portfolio management and
quantitative hedging represent some
of the many dependency requirements
within a central risk technology solution.
Sell-side investment in central risk
focuses on leveraging big-data and
modern portfolio theory to minimise
risk / maximise opportunity within a
diversified pool of market marking
inventory; providing buy side investors
with a secondary principal pool of liquidity
working to reduce impact on primary
markets and reduce execution time
horizons.
Hedging efficiency remains the core
objective of CRB automation with
development of smart inventory
management, systematic unwind and
built in risk optimisation providing traders
with lower explicit execution costs. Thus
offering a secondary derivative efficiency
in capital pricing and lower long term cost
unwind benefit to underlying investors
and asset holders.
Citi remains committed to the long
term investment in automated capital
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and central risk management. These
efficiencies provide clients with an optimal
mix of liquidity across both agency and
capital platforms to mitigate market
impact across both large and small
orders types. Ongoing industry adoption
of standardised indication of interest
messaging (IOIs) equally helps to improve
transparency within the framework of
advertised sourced liquidity. Further,
helping clients to improve pre-trade
transparency and deliver a decision tree
approach to primary, MTF, off exchange
(inc SI) and capital liquidity solutions.
Equally, ongoing developments in low
latency trading strategies ensure that anti
gaming, negative selection alpha-risk
management and artificial intelligence
(machine learning solutions) are evolving
towards a mainstream investor audience
acceptance. These developments have
been supported in recent years with the
roll out of trader analytic dashboards and
quantitative big-data execution strategies.
These products work to support and
enhance the existing toolbox to provide
traders with increasing capabilities to take
advantage of short term dislocation