An Agent-based Model for Assessing Financial Vulnerabilities Rick Bookstaber

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

2. 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

3. 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

4. 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

5. 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. What is an Agent-based Model (ABM)

6. 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 Applications of the Agent-based Model

7. ABM Schematic – Flows Between the Agents

8. The Agents Caught Up in Fire Sales Asset-based Fire Sale Funding-based Fire Sale ASSET MARKET INVESTORS BANK/DEALER HEDGE FUNDS Prime Brokerage CASH PROVIDERS Finance Desk INSTITUTIONS OTHER BANK/ DEALERS Trading Desk INSTITUTIONS OTHER BANK/ DEALERS Derivatives Desk Treasury Flow of Collateral Flow of Funding

9. Transformations of Flows in the ABM The Financial System as a Production Plant Maturity transformation • Short-term deposits to long-term loans. Credit transformation • Structured products with tranches of varying credit risk. • Safe money into funding for risky hedge funds. Collateral transformation • Lower quality collateral to higher quality collateral. Liquidity transformation • Market making. • Repackaging assets into liquid vehicles, such as ETFs. Risk transformation • Selling off part of the return distribution via derivatives. • Tranches with varying risk characteristics.

10. 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 Two Bank/Dealers: BD1, BD2 One Cash Provider: CP1 Run over 1000 iterations A Model Run HF1, BD1 Portfolio: {A0, A1} HF2, BD2 Portfolio: {A1, A2}

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. Period 0: The Static Stress – A 15% Price Shock to A0 Period 0 • 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

18. Period 2: Cascade in A0 and Contagion through A1 • 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 0 Period 2

19. Period 4: Credit and Funding Effects • 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 Period 2 Period 4

20. Period 6: Collateral and Capital Damaged • The fire sale reaches its end. • In this run BD1, HF1, and HF2 have defaulted Period 4 Period 6

21. Sources of Shock

22. Tracking the Propagation of Shocks

23. 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 ConclusionWhat Does Version 3.0 Mean at the Firm Level

24. 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. ABM and VaR

25. 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 Networks and Agent-based Models

26. 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 Question: Testing the Agent-based Model

27. Data 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 Question: Populating the Model with Data and Rules

28. Cash Provider Home

29. Asset Home

30. Derivatives Desk Home

31. Treasury Home

32. Prime Broker Home

33. Finance Desk Home

34. Hedge Fund / Trading Desk

35. Hedge Fund / Trading Desk Home