From end-of-day to real-time risk management
Mikael Sörböen, BNP Paribas 12th September 2014
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Explain the title “risk management”, “end-of-day”, “real-time”
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capital markets trading activities
“risk management” market risk
counterparty risk “end-of-day”
“real-time”
E-o-D Risk Management Functional Architecture
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Risk indicators 10+ source systems 100k daily feeds 800m rows per day
Static data Counterparties Underlyings Mappings
Calibration Correlation Volatility Norms
Market data Spot, volatility levels All assets (50k+ simulated assets) Values, curves, surfaces
Deals 10+ source systems 2m stock / 100k per day 100+ attributes “Smart deal” representation
Monte-Carlo internal models for capital VAR / Stress VAR (8k nodes, 90k calculations) Counterparty exposure (38k c’parts)
Limit monitoring Automatic monitoring of all market risk limits (900k) Market risk limit management Counterparty excess management
Risk analysis & reporting Multi-dimensional drill-down capabilities Excel add-in to produce reports Rapid time to market for new views User-defined views “Position age” to help users
Stress testing 10+ daily counterparty stress scenarios Full-reval and “greeks” based market risk stress Market risk stress per counterparty
P&L items CVA calculation Market risk reserves
All business lines
COUNTERPARTY & MARKET
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E-o-D Risk Management “Position Age”
E-o-D Risk Management Technical Architecture
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“big-ish data-base”
GET DATA 10+ sources 100k feeds
500 GB
2E-o-D
Data roll
1
“data management”
TINY 3 days
MIDDLE 50 days
HISTO 1250 days
0
pos (800m) 3 aggregates date split multiplex
40 TB
LOAD + AGG
4
“Monte Carlo calculations”
TRANSFORM 3
SCRIPT ENGINE
business logic 50k jobs 250 types
MATCH ENGINE
id matching build DB file
90k jobs
CALCULATION TRIGGERING 5
EXTRACT – CALCULATE
Value-at-risk
Limits Reserves
150k extracts
6COUNTERPARTY
EXPOSURE
1m PV per deal 1.5m deals
10+ scenarios From 6000 cores
to 25 GPUs
7
MRfleXTM – besoke BI layer DEPENDENCY MANAGEMENT
5
USER INTERFACES
2000 users, 40 sites 50k requests
On-demand services Excel add-in
8
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E-o-D Risk Management Drill-down analysis
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E-o-D Risk Management “Nodes and Risk Factors”
“leaf node”
“risk factor”
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E-o-D Risk Management Zoom on calculation triggering
VaR calc [Leaf node, risk factor]
Exposure calc [Counterparty,
risk site]
CALCULATION TRIGGERING
Data Tracker
Event Engine
Sims (E-o-D)
Mapper (E-o-D)
Risk feeds (Load + Agg)
Deal static (11pm-9am)
due to agg
FO PV (Match)
due to agg
Sims (E-o-D)
Credit params (E-o-D)
Counterparty static
(2am – T or 11pm for T-1)
MA / CSA coverage
(2am – T or 11pm for T-1)
assign deals to counterparty
assign deals to MA / CSA
Real-time Risk Management What’s the story?
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compliance & control Pre-trade checks against counterparty risk lines Compliance with trading mandates Detection and prevention of abnormal trading patterns (market risk) Incremental initial margin requirements from CCPs Control of market risk exposures per counterparty net of collateral resource management
Funding impact – initial margin Cost of capital, allocation Risk reduction strategies
Cost of CVA (and DVA) client service & transparency Inform clients ex-ante on risk exposures - Per transaction or full portfolios including stress What-if scenarios, marginal impact on IM not just a technology challenge …
Mandate check Can the trader make this trade?
Client position Greeks and stress for overall portfolio and per trade
Market risk limit check Does trade respect intra-day limit?
Counterparty checks Is a limit available for this trade? Compute incremental CVA / DVA Compute incremental capital
Funding Liquidity impact Incremental initial margin
real-time risk platform
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Real-time Risk Management Some of the challenges…
deal details greeks,
stress
pricing request
OK / NOK CVA, K
Client position
Clean static data Product, portfolio, currency, counterparty, MA, CSA, pricing representation, credit parameters
Market data What market data to use for real-time calculations? Consistency with “stock” positions Periodic recalculation of whole perimeter?
Which risks? Try to compute all E-o-D risks? If not which subset?
Timing and completeness Synchronisation of trade and hedge Capture all trades and events
latency –
µs, ms, s, m? Pre-trade?
Really? Electronic?
From end-of-day to real-time risk management Key reflections
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sharpen up the requirements, get buy-in not simple
extension, different architecture
clarify who does what? seize the
opportunity to simplify
Questions?
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