SystemwideCommonalities in Market Liquiditymmds-data.org/presentations/2016/s-flood.pdf · Source: CRSP, Mergent, Bloomberg, WRDS, FINRA, OFR analysis. Global financial crisis •

Systemwide Commonalities in Market Liquidity

Mark Flood – Office of Financial Research (OFR)

John Liechty – OFR, Penn State U.

Tom Piontek – OFR

Workshop on Algorithms for Modern Massive Data Sets (MMDS 2016)MMDS FoundationU. of California, Berkeley, CA, June 24th, 2016

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.1

Disclaimer

Views and opinions expressed are those of the authors and do not necessarily represent official OFR or Treasury positions or policy.

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.2

Liquidity Measurement

What is liquidity?• Good question!

– Vast research literature• Ultimate focus is contract settlement

– Can I “get to cash” to meet my obligations?

Why do we care?• Liquidity is crucial to market functioning

– Most obligations are denominated in cash• Illiquidity is a common feature of market stress

– Symptomatic: both cause and effect

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.3

Liquidity Measurement

Why it’s challenging• Latent

– We care most about illiquidity (when liquidity vanishes)– Often unobserved until it’s too late

• Nonlinear – We care most about liquidating large positions– Small fluctuations are not a good guide for large events

• Emergent – We care most about aggregate liquidity conditions– The whole is not the sum of the parts: liquidity begets liquidity

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.4

Market and Funding Liquidity

Source: OFR analysis

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.5

Is this Big Data?

Some orders of magnitude• Corporate equities

– 5,000+ individual firms traded – High-frequency trading common (ca. μS frequency)

• Corporate bonds– Ca. 100,000 individual issues traded – Weekly average trading frequency more typical

• Exchange-traded futures – 1,000s of distinct contracts (underlying x maturity)– Trading frequency is diverse

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.6

Liquidity Measurement Requirements

Feasibility• Data inputs need to be

available to calculate measureTimeliness• It should be practical to update

the metric at least daily

Comparability• Metric should have same general

statistical characteristics for all markets

Granularity• The measurement should be

resolvable to the level of the individual markets

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.7

Examples of Market Liquidity Measures

Market liquidity – financial equities (SIC6) Jan 1986 – Mar 2014

Source: CRSP, Mergent, Bloomberg, WRDS, FINRA, OFR analysis

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.8

Market-level Price Impact Measures

Market Microstructure Invariance• Kyle and Obizhaeva (2014)

“Market Microstructure Invariants: Theory and Empirical Tests”• Daily measure• Works for many markets (“invariant”)• The calibrated price-impact trading cost, C(X), in basis points:

𝐶𝐶 𝑋𝑋 = �𝜎𝜎 𝜅𝜅0 �𝑊𝑊 �−13 + 𝜅𝜅1 �𝑊𝑊 �1 3

𝑋𝑋�𝑉𝑉

Where:• �𝜎𝜎 = normalized, expected volatility (betting volatility)• �𝑊𝑊 = normalized “trading activity” ∝ price x volume x volatility• 𝑋𝑋 = order size

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.9

Latent Liquidity Structure

Hidden Markov Chain for observed liquidity• For each market, estimates a “latent” or unobserved level of liquidity• Bayesian Hierarchical Model; Inference using Markov Chain Monte Carlo• Detected three distinct liquidity states (levels of the price impact measures)• Estimated level of liquidity for each state and probability of being in a state

Source: CRSP, Mergent, Bloomberg, WRDS, FINRA, OFR analysis

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.10

Estimated Liquidity States

Source: CRSP, Mergent, Bloomberg, WRDS, FINRA, OFR analysis

Average Estimated State Probabilities(Hidden Markov Chains, 33 series, Apr. 2004 – Mar. 2014)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Low Intermediate High

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.11

Heat Map

Mixed Price-Impact States4 Markets, Daily, 2004 – 2014

Source: CRSP, Mergent, Bloomberg, WRDS, FINRA, OFR analysis

Global financial crisis• 8/2007: BNP and quant funds• 2/2008: Bear Stearns failure• 7/2008: Fannie/Freddie failure• 9/2008: Lehman Bros. failure• 3/2009: Federal Reserve stress tests

Equity portfolios Industry groups

SIC 0–8

Corporate bondsRatings buckets

Volatility futures (VIX)

Mat. 1–9 mos.

Oil futures (WTI)Mat. 1–6 mos.

European sovereign debt crisis• 8/2011: S&P downgrades U.S.• 9/2011: Occupy Wall St. begins• 10/2011: Eurozone intervention • 11/2011: International intervention

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.12

Hierarchical Model

What is driving the hidden Markov models?• Eleven financial market summary indicators to predict each latent state• Equity (CRSP) and bond (TRACE) liquidities – here as first principal components

– MCMC Average Hit Rate = 56%, versus Naive Hit Rate = 33%

Source: CRSP, Mergent, Bloomberg, WRDS, FINRA, OFR analysis

Variable Coefficient T-Stat (mean/std)State 2 State 3 State 2 State 3

Intercept -0.51 -0.97 -107.79 -115.08

WTI 0.60 -0.21 29.10 -15.20

3-mo. Repo Rate 0.53 -0.48 12.25 -27.94

TED Spread 0.44 -0.05 23.19 -2.34

5-year Breakeven Inflation -0.02 -0.04 -2.76 -9.76

VIX 0.41 0.01 30.81 1.38

S&P500 Price/Book 0.40 -0.14 22.00 -10.28

Dow Jones Real Estate Index -0.86 0.02 -36.23 1.42

Moody’s BAA Index -0.48 0.28 -21.72 27.88

LIBOR–OIS Spread -0.54 0.17 -13.81 8.30

DXY Dollar Index -0.21 -0.39 -11.35 -124

10yr–2yr Yield Spread 0.22 -0.43 6.73 -65.48

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.13

Hierarchical Model

Interpreting the Probit results – case of the TED spread• TED spread jumps in 2007, peaks after Lehman• Probit over-predicts the probability of State 3, due to policy response

Source: CRSP, Mergent, Bloomberg, WRDS, FINRA, OFR analysis

State 1 (high liquidity)• TED spread (scaled)• Probit predicted (avg.) probability

State 2 (intermediate liquidity)• TED spread (scaled)• Probit predicted (avg.) probability

State 3 (low liquidity)• TED spread (scaled)• Probit predicted (avg.) probability

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.14

Predicting Liquidity Regimes

Method:• Freeze Probit

coefficients in June 2007

• 15-trading-day forecast of state probabilities –forecasts converge on one state

• Models predict low liquidity, starting in August 2007

What would the model have predicted in 2007-2008?

Source: CRSP, Mergent, Bloomberg, WRDS, FINRA, OFR analysis

BNP Paribas halts redemptions Aug 2007

Bear StearnsMar 2008

Lehman BrothersSep 2008

Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.15

Gratitude

Thanks!