SystemwideCommonalities in Market Liquiditymmds-data.org/presentations/2016/s-flood.pdf · Source:...
Transcript of SystemwideCommonalities in Market Liquiditymmds-data.org/presentations/2016/s-flood.pdf · Source:...
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!