risks, fast and slowpast.rinfinance.com/agenda/2017/talk/davedemers.pdfmore systematic risk ideas....
TRANSCRIPT
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RISKS,
FAST AND SLOW
David DeMers
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AGENDA
How I will talk about risk.
The good old days of quant quakes and liquidity crises.
Some bedtime stories
Classification for (perhaps) actionable purposes.
Regimes
Crises
Outliers
WTF
Why bother.
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RISKS, FAST AND SLOW
Let’s parse the title of the talk
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RISK
Merriam-Webster: possibility of loss or injury
Elroy Dimson: "Risk means more things can happen than will happen." It is not standard deviation. It is not variability. It is this sense that the future events are highly variable and unknowable that gives us the best sense for risk.
common practice to use s and call it “risk”
I will discuss ways to guess s conditional on current information
Which is clearly not the whole story.
Epistemology can be discussed at Jak’s tonight with beer …
“You don’t have to solve everyone’s problems but it’s good to be able to solve someone’s problem.”
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FAST AND SLOW
“Fast” – using real time data
“Slow” – mostly not… (mostly…)
We know about things like factor exposures that matter
So I’ll focus on not that, I’m thinking about “state changes”
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REGIMES/MARKET “STATE”
CNN MONEY FEAR & GREED INDEX
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REGIMES / MARKET “STATE”
Ex post it is easy to identify periods that we can classify into two
or more regimes or states. For example,
NBER Recession
“Risk on” vs “Risk off”
Alpha-seeking vs. liquidity seeking?
Can we identify anything that leads to meaningful predictions
conditioned on our guess of market state?
Some stats “work”
but not a lot of events thus not a lot of power
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It’s a Bunny!
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It’s a cloud!
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REGIMES / MARKET “STATE”
Stuff we can just look up
Implied Vol (VIX)
Implied correlation (ICJ)
Bloomberg FCON <GO>
IMF toolkit: systematic risk indicators
OFR suite of macro indicators
ECB risk dashboard
Fun with FRED
CONTINUED
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REGIMES / MARKET “STATE”
How do we map from data to policy?
WTF does [DATA ITEM] actually mean?
Lots of DATA, not so much INFORMATION
CONTINUED
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REGIMES / MARKET “STATE”
RECESSION MODELING
“30 days in the lab saves 3 hours in the library” – Hal White
Many studies. Hundreds if not thousands…
Term spread?
Employment?
Spending?
We have a swiss army knife.
Yesterday’s talk should help.
Let’s try some things
CONTINUED
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REGIMES / MARKET “STATE”
TERM SPREAD
10Y3M spread
Since 1982,
All recessions preceded by negative value
All negative values precede a recession
No false positives nor negatives!
But,
it’s only 3 for 3
Lead time is 1-18 months
There is some theory
However, we can always make up a story ex post, even if we didn’t start with any theory…
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REGIMES / MARKET “STATE”
LATHER, RINSE, REPEAT
Data
Idea
Stats
Guess
causality
(modeling)
Create Policy
Act on model
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ECONOMIC CONSEQUENCES
Could just be the shrooms talking wishful thinking, but
Seems like there is potential for regime timing
Consider the cost of being wrong
False positives -> buy too much insurance / overhedge
False negatives -> suffer from left-tail exposure
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REGIMES / MARKET “STATE”
MARKET ACTIVITY
What can we infer from market data?
Degrees of freedom / concentration of drivers
Entropy measures
Kritzman’s Absorption Ratio
Market model goodness of fit
Momentum crash
The list goes on
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REGIMES / MARKET “STATE”
ABSORPTION RATIO EXAMPLE
Absorption ratio:
% of variance contained in first N principal components
Compare value over short window to value over long window
E.g. 3 weeks vs 1 year
Q: components of what, exactly?
Assets (e.g. SPX 100)
Indices (e.g. global equities)
Asset classes (e.g. commodities, FX, credit, etc)
Factor returns (e.g. momentum, size, liquidity, … )
CONTINUED
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REGIMES / MARKET “STATE”
MORE IDEAS, SAME SPIRIT
Entropy measure:
Sum(Eigen(cor(X))$values * log(eigen(cor(X))$values))
Model fit:
𝑅2 from e.g. fit of [your favorite model] over trailing window
High 𝑅2 -> “fragile”, Low 𝑅2 -> “robust”
Prefer to get a daily value using 1-min bars on SP 100
CONTINUED
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REGIMES / MARKET “STATE”
MOMENTUM CRASH
Momentum crash:
I refer you to excellent papers by Kent Daniel for details…
We might not be able to predict them but
6 out of 6 in sample lead to “regimes” with specific characteristics
1 out of 1 out of sample consistent with this story
Momentum crash of March 2016, policy suggests BUY market
CONTINUED
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REGIMES / MARKET “STATE”
WHAT ARE THE PLAYERS DOING?
Co-holdings liquidation risk (Amaranth trigger)
13F of peers.
Correlation with our long book vs with our short book
If we believe they are smart and we are smart, we can make inferences
Hard to get cross asset linkages -
E.g. When will a credit issue be reflected in equity trading?
Liquidity, duration, holdings…
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SUMMARY: REGIMES / MARKET “STATE”
WHAT’S THE POINT?
Is current market state typical?
Is market “fragile”?
Are we changing regimes?
Entering or exiting recession?
CONTINUED
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REAL TIME DETECTION OF
(SOMETHING INTERESTING)
Coming up on the 10 year anniversary of Quant Quake
Still afflicted with PTSD and obsess about market disruptions
“Dash to cash” vs. “Dash to trash”
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REAL TIME DETECTION OF
(SOMETHING INTERESTING)
Trading records show
“regimes” (maybe)
Flash crashes
Flight to liquidity
Flight to quality
can occur over long periods
E.g. May-Sept, 1998
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REAL TIME DETECTION OF
(SOMETHING INTERESTING)
Track stats on instruments along dimensions of
Some kind of Beta
E.g. Dow < SPX < NDQ < RUT
Inverted return to beta
Duration
E.g. Bonds, BUT can calculate effective duration for many things
Term curve inversion
Quality
E.g. Sovereign > AAA > JNK
“Dash to trash” or “Flight to Quality”
Liquidity
E.g. “Typical” market depth, turnover, $ volume / $ float, etc.
Sell what you can, not necessarily what you want to.
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REAL TIME DETECTION OF
TOXIC TRADING
VPIN on hedging instruments like ES, US, Gold…
Is current activity significantly different from “typical”
Most of the time nothing interesting
BUT when crisis occurs (occurred?) we see extreme value
HFT Activity
Can we identify presence of more aggressive HFT algos?
Can we game them by understanding their patterns of
accumulation and distribution?
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REAL TIME DETECTION
CROSS SECTIONAL TICK DATA
“Turbulence” – compare current vector of returns to “typical”
Degrees of freedom / number of independent drivers
Statistical process control ideas (CUSUM, runs tests)
Can we predict longer horizon vol with high frequency data?
(Yes)
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REAL TIME DETECTION
CROSS SECTIONAL TICK DATA
I will tell a story
(since I didn’t want to take the time to reproduce old results…)
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WHY BOTHER?
Insurance is expensive
Even more so when we need it
When it might no longer be available
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RISK PACKAGES?
MANY packages calculate things of interest.
E.g. see CRAN Task Views on
• Econometrics, Time Series, Finance, Optimization
• ExtremeValue
• I might like to see a dashboard,
• Maybe in a Shiny app…
• Most of these ideas are easy to code
• As usual, challenge is data management…
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APPENDIX
“the appendix is small and has no known function” – Oxford Dictionary
http://www.womenshealthmag.com/health/facts-you-need-to-know-about-your-appendix
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LAST RECAP: WHY BOTHER?
MAYBE GET POLICY IDEAS…
Regime / market state
If “benign” swim naked. Better yet, sell vol.
If not, buy some insurance
Not gonna mention counterparty risks here…
If you can react & trade fast without “too much” market impact
Track critical things in real time
For example:
VPIN on instruments used for hedging; SPY, US, Gold?
Degrees of freedom on suite of indices
E.g. Factors (value, momentum, any other likely smart beta things…)
CONTINUED
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WHAT ABOUT
BLACK SWANS
In 16th century London, a statement that describes impossibility was referred to as a “black swan” because all
historical records of swans up to that time had reported that swans only had white feathers. The Greek
philosopher Aristotle first used the term “white swan” as an example of necessary relations and the term “black
swan” as the expression of the incomprehensible. Further developed by Pyrrhon.
A Black Swan is NOT just some outlier
It’s an event that we did not believe was possible
either because
• we were “sure” about it or
• we did not even think about it
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WHAT ABOUT SENTIMENT?
Twitter or other real time social media?
Fake news?
I don’t deal with this but probably worth thinking about
I read claims about successes but have yet to see evidence
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MORE SYSTEMATIC RISK IDEAS
Model Authors
CoVar Adrian and Brunnermeier
Co-Risk Chan-Lau, Espinosa, Giesecke, and Solé
Network Models and Financial Stability Nier, Yang, Yorulmazer, and Alentorn
Agent-Based Model of the Leverage Cycle Thurner
Systemic Expected Shortfall Acharya, Pedersen, Philippon, and Richardson
Distress Insurance Premium Huang, Zhou, and Zhu
PCA and Granger-Causality Billio, Getmansky, Lo and Pelizzon
Distress Dependency Segoviano and Goodhart
Systemic CCA Gray and Jobst
(Swiped from one of the reports on next page)
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A FEW REFERENCES
https://www.financialresearch.gov/annual-reports/
https://www.mitre.org/sites/default/files/pdf/12_1870.pdf
https://www.imf.org/external/pubs/ft/wp/2011/wp11235.pdf
https://sdw.ecb.europa.eu/reports.do?node=1000003268
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