Transcript An Agent-based Model for Assessing Financial Vulnerabilities Rick Bookstaber Office of Financial Research Isaac Newton Institute Systemic Risk: Models and Mechanisms August 28, 2014

An Agent-based Model for
Assessing Financial Vulnerabilities
Rick Bookstaber
Office of Financial Research
Isaac Newton Institute
Systemic Risk: Models and Mechanisms
August 28, 2014
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Background: The Office of Financial Research
• Established by the Dodd-Frank Act
• Independent agency, housed in the Department of Treasury
• Tasks are to
– Support the inter-agency Financial Stability Oversight Council
– Facilitate analysis of the financial system
– Improve the quality of financial data available to policymakers
• No regulatory authority
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Risk Management – Versions 1.0 to 3.0
• Version 1.0: Historical Data – VaR Models
• Version 2.0: Static Scenarios – Stress Tests
• Version 3.0: Dynamic Interaction – Agent-based Models
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The Problem to Solve: Fire Sale Dynamics
Asset-based Fire Sale
• Asset (Price) Shock → Forced Sales → Shock to other Assets
=> Cascades + Contagion
Funding-based Fire Sale (Funding Run)
• Funding Shock → Forced Sales → Further Funding Reduction
=> Cascades + Contagion
Leverage- and Liquidity-driven
Asset-based Fire Sales ↔ Funding-based Fire Sales
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What is an Agent-based Model (ABM)
Agents pursue their activities period by period
• Agents are heterogeneous
• Can use heuristics rather than optimize
• Observe and react to the changing environment
• Influence one another; interdependent with dynamic interaction
Example
Analysis of traffic flows
Bookstaber (2012), Using Agent-Based Models for Analyzing Threats to Financial
Stability, OFR Working Paper No. 3.
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Applications of the Agent-based Model
Detect Vulnerabilities (Pre-Shock)
• What are the dynamic, knock-on effects
Weather Service (Post-Shock)
• Are we on the hurricane’s path; how bad will it be
Policy Planning and Actions (Pre- and Post-Shock)
• Where do we put the emergency shut-off valves; which do we close
• When do we provide asset and funding liquidity
Data Needs
• How much can things be improved with better data
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ABM Schematic – Flows Between the Agents
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The Agents Caught Up in Fire Sales
Asset-based Fire Sale
Funding-based Fire Sale
BANK/DEALER
Prime Brokerage
Finance Desk
INSTITUTIONS
OTHER BANK/
DEALERS
CASH
PROVIDERS
Trading Desk
INSTITUTIONS
OTHER BANK/
DEALERS
Derivatives Desk
Treasury
Flow of Collateral
Flow of Funding
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INVESTORS
ASSET MARKET
HEDGE FUNDS
Transformations of Flows in the ABM
The Financial System as a Production Plant
Maturity transformation
•
Short-term deposits to long-term loans.
Credit transformation
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Structured products with tranches of varying credit risk.
Safe money into funding for risky hedge funds.
Collateral transformation
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Lower quality collateral to higher quality collateral.
Liquidity transformation
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Market making.
Repackaging assets into liquid vehicles, such as ETFs.
Risk transformation
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•
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Selling off part of the return distribution via derivatives.
Tranches with varying risk characteristics.
A Model Run
The model can have an any number of agents , markets, and
iterations.
In this model parameterization we have:
• Three Assets: A0, A1, A2
• Two Hedge Funds: HF1, HF2
HF1, BD1 Portfolio: {A0, A1}
HF2, BD2 Portfolio: {A1, A2}
• Two Bank/Dealers: BD1, BD2
• One Cash Provider: CP1
• Run over 1000 iterations
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Schematic for Looking at the Network Dynamics
• Thickness of links shows cumulative effect.
• Color of links shows intensity of effect in the current period.
• Amount of node that is colored shows capital, funding, or price
relative to initial value.
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Schematic for Looking at the Network Dynamics
• Thickness of links shows cumulative effect.
• Color of links shows intensity of effect in the current period.
• Amount of node that is colored shows capital, funding, or price
relative to initial value.
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Schematic for Looking at the Network Dynamics
• Thickness of links shows cumulative effect.
• Color of links shows intensity of effect in the current period.
• Amount of node that is colored shows capital, funding, or price
relative to initial value.
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Schematic for Looking at the Network Dynamics
• Thickness of links shows cumulative effect.
• Color of links shows intensity of effect in the current period.
• Amount of node that is colored shows capital, funding, or price
relative to initial value.
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Schematic for Looking at the Network Dynamics
• Thickness of links shows cumulative effect.
• Color of links shows intensity of effect in the current period.
• Amount of node that is colored shows capital, funding, or price
relative to initial value.
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Schematic for Looking at the Network Dynamics
• Thickness of links shows cumulative effect.
• Color of links shows intensity of effect in the current period.
• Amount of node that is colored shows capital, funding, or price
relative to initial value.
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Period 0: The Static Stress – A 15% Price Shock to A0
Period 0
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•
•
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A0 experiences a 15% price shock
BD1 and HF1 hold A0 in their portfolio
CP holds A0 as collateral
The end of the story for the standard stress test
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Period 2: Cascade in A0 and Contagion through A1
Period 0
•
•
•
•
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Period 2
BD1 and HF1 decrease positions in both A0 and A1
This creates a downward cycle for A0 and a drop in A1
It also affects other agents holding A0 or A1
CP1 reduces funding as its collateral value drops
Period 4: Credit and Funding Effects
Period 2
Period 4
• The drop in prices ignites a funding-based fire sale through CP1
• The dynamic spreads due to credit exposure from BD1 to BD2
• This can lead to difficulty in identifying the source of contagion
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Period 6: Collateral and Capital Damaged
Period 4
Period 6
• The fire sale reaches its end.
• In this run BD1, HF1, and HF2 have defaulted
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Sources of Shock
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Asset Market: Price Shock
Cash Provider: Funding Shock
Bank/Dealer: Credit Shock
Hedge Fund: Redemption Shock
Tracking the Propagation of Shocks
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Asset Market: Price Shock
Cash Provider: Funding Shock
Bank/Dealer: Credit Shock
Hedge Fund: Redemption Shock
Conclusion
What Does Version 3.0 Mean at the Firm Level
Dynamic Stress Testing
• Are you on the hurricane's path?
• Will you become collateral damage
Salvaging VaR
• Crisis VaR and the VaR multiplier
Catching Falling Knives
• Being a liquidity supplier of last resort
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ABM and VaR
20%
10%
0%
-10%
-20%
-30%
-40%
-50%
-60%
-70%
Capital Change of Firm
Capital of Firm ($BB)
$14
$12
$10
$8
$6
$4
$2
$0
0
2
4
6
8 10 12 14
Period Post Asset 1 Shock
99.0%
5.0%
-5
95.0%
1.0%
Mean
-3 -1 1 3 5 7 9
Period Post Asset 1 Shock
• The fire sale cascade leads to a downward skew for the capital post-shock.
• Red lines are the mean and 5%/95% envelope, 1000 runs of a 15% shock in Asset 1.
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Networks and Agent-based Models
Using the “Maps” to Create a Multi-layer Network
• Funding Map
• Collateral Map
• Assets Map
What agents faciliate movement from one layer to another
Using the ABM to Create and Analyze Dynamical Networks
• Nodes provide transformations and to respond to the environment
• Changes in the size (and existence) of nodes
• Links vary in size of flows, and in their effect on the behavior of the
transformations in the nodes
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Question: Testing the Agent-based Model
Parameter Realism
• Do parameter values of real-world agents lead to real-world dynamics
Comparative Statics
• Do things move in the right direction, by the right amount, from a reasonable
initial value
• Is there common sense consistency
Stylized Facts
• Do we see agents and markets behave in the right way
Back Testing
• Can we reproduce past events
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Question: Populating the Model with Data and Rules
Data
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Exposures: dominant investment themes, credit
Funding: sources, durability, leverage, collateral
Prices: “big trade” liquidity
Frequency: Exposures and funding build and change slowly
Completeness: More is better; less can still work
Agents’ Rules
• Many actions during stress are pre-determined and non-proprietary
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Cash Provider
Home
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Asset
Home
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Derivatives Desk
Home
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Treasury
Home
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Prime Broker
Home
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Finance Desk
Home
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Hedge Fund / Trading Desk
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Hedge Fund / Trading Desk
Home
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