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

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 2 Background: The Office of Financial Research

Version 1.0: Historical Data – VaR Models Version 2.0: Static Scenarios – Stress Tests Version 3.0: Dynamic Interaction – Agent-based Models 3 Risk Management – Versions 1.0 to 3.0

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 4 The Problem to Solve: Fire Sale Dynamics

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. 5

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 6

ABM Schematic – Flows Between the Agents 7

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

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. 9 Transformations of Flows in the ABM The Financial System as a Production Plant

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 Two Bank/Dealers: BD1, BD2 One Cash Provider: CP1 Run over 1000 iterations 10 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.

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

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

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 4: Credit and Funding Effects 19 Period 2Period 4

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

Sources of Shock 21 Asset Market: Price ShockCash Provider: Funding Shock Bank/Dealer: Credit ShockHedge Fund: Redemption Shock

Tracking the Propagation of Shocks 22 Asset Market: Price ShockCash Provider: Funding Shock Bank/Dealer: Credit ShockHedge 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 23

ABM and VaR 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. 24

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 25

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 26

Question: Populating the Model with Data and Rules 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 27

Cash Provider 28 Home

Asset 29 Home

Derivatives Desk 30 Home

Treasury 31 Home

Prime Broker 32 Home

Finance Desk 33 Home

Hedge Fund / Trading Desk 34

Hedge Fund / Trading Desk 35 Home