Tail event

The ISDA Master Agreement The Jolly Contrarian holds forth™ →Go uptown intro sa Resources and Navigation Index: Click ᐅ to expand: ISDA Navigation See ISDA Comparison for a comparison between the 1992 ISDA and the 2002 ISDA. The Varieties of ISDA Experience Subject 2002 (wikitext) 1992 (wikitext) 1987 (wikitext) Preamble Pre Pre Pre Interpretation 1 1 1 Obligns/Payment 2 2 2 Representations 3 3 3 Agreements 4 4 4 EODs & Term Events 5 Events of Default: FTPD • Breach • CSD • Misrep • DUST • Cross Default • Bankruptcy • MWA Termination Events: Illegality • FM • Tax Event • TEUM • CEUM • ATE 5 Events of Default: FTPD • Breach • CSD • Misrep • DUST • Cross Default • Bankruptcy • MWA Termination Events: Illegality • Tax Event • TEUM • CEUM • ATE 5 Events of Default: FTPD • Breach • CSD • Misrep • DUSS • Cross Default • Bankruptcy • MWA Termination Events: Illegality • Tax Event • TEUM • CEUM Early Termination 6 Early Termination: ET right on EOD • ET right on TE • Effect of Designation • Calculations; Payment Date • Payments on ET • Set-off 6 Early Termination: ET right on EOD • ET right on TE • Effect of Designation • Calculations • Payments on ET • Set-off 6 Early Termination: ET right on EOD • ET right on TE • Effect of Designation • Calculations • Payments on ET Transfer 7 7 7 Contractual Currency 8 8 8 Miscellaneous 9 9 9 Offices; Multibranch Parties 10 10 10 Expenses 11 11 11 Notices 12 12 12 Governing Law 13 13 13 Definitions 14 14 14 Schedule Schedule Schedule Schedule Termination Provisions Part 1 Part 1 Part 1 Tax Representations Part 2 Part 2 Part 2 Documents for Delivery Part 3 Part 3 Part 3 Miscellaneous Part 4 Part 4 Part 4 Other Provisions Part 5 Part 5 Part 5 Index: Click ᐅ to expand: Essays

You asked me what’s my pleasure:
A movie or a measure?
I’ll have a cup of tea
And tell you of my dreaming.

—Blondie, Dreaming (1979)

Tail event
/teɪl ɪˈvɛnt/ (n.)

1. Statistics: Of a range of possible independent events, one whose frequency is three or more standard deviations from the mean. An event with a low probability.
2. Work life: An unwanted outcome you didn’t expect, to which you weren’t paying attention, and, therefore, for which you don’t think you should be blamed.

The randomly distributed marketplace

Amarket, in the abstract, looks like what Nancy Cartwright calls a “nomological machine”. A simplified model of the real world having defined boundaries and simplified operating conditions: a finite trading day, a limited number of market participants and a defined set of fungible financial instruments with which participants engage in a limited range of transactions, whose outcomes deterministically set observable prices for that set of traded instruments, which prices bear quantifiable relationships with previously traded prices for the same instruments (in that they will be higher, lower, or the same).

But the real world these instruments represent is intractable. It does not have boundaries. It is inchoate, and our knowledge about it less so: The “instruments” of the real world are not “fungible” — 2 shipments of the same commodity have indefinable idiosyncratic impurities and characteristics — abd the range of possible events that can occur to physical commodities in undefined in a way that the range of events that might occur to financial instruments is not.

“A portfolio of asset-backed securities cannot,” a commodities trader would say, “suffer water damage. They do not rust.”

Not having to deal with rust, water damage or manufacturing defect simplifies the business of investing in the financial instruments. The effects of these events are supposed to play out in the information layer, and translate efficiently into the prices at which related instruments trade. If an oil company’s tanker is wrecked, its share price declines.

It is tempting to infer information from price: to put a drop in the market down to “soft non-farm payroll data”. People make a living reading tea-leaves in this way.

From price information we can derive a relationship between transactions — the price went up, stayed the same, went down — and a trend. A trend is a stab at extracting a signal from the noise.

The signal depends on a theory of the game: otherwise the “relationship” between the two discrete transactions is arbitrary.

Without a theory, everything is noise.

The theory-dependence of signal

If events are truly “independent” then any “trend” we draw between them beyond their distribution is, more or less, meaningless. In a first order sense, market events are independent: the participants in the later trade do not know who or where the participants in the earlier are, let alone what were their motivations for trading. All we have is a theory and some mathematics. But we draw the line all the same.

We make assumptions: the market is homogeneous; all participants have similar price information; all are propelled by the same rationale. No trader sells things she expects to do well, nor buys things she expects to do badly.

Private narratives wash out

Given these assumptions, individual investors’ private motivations, opinions, theories and idiosyncrasies cancel each other out, so we can disregard them. They are like the Brownian motion of molecules in a nice hot cup of tea: reversions to the entropic mean; baseline white noise. This is just as well, because otherwise our models would not work. We can ignore individual sentiments because they don’t matter. Until they do.

Put another way: although the “interconnectedness” of similar transactions means they do not have the quality of independence that a normal distribution requires, most of the time they pretty much do: information is chaotic in the immediate term, here the dissimilarities between trader motivations are most pronounced, but over a large aggregation of trades and a longer period a “signal” emerges. This is what Black-Scholes, volatility and convexity models track: as long as all traders all use the same aggregated market information — and the market works hard to ensure they do — a “normal” probabilistic model works fairly well. It’s not a bad model.

So we treat professional market participants as a largely homogenous group from which emerges, over time, a signal. Almost like, you know, like an invisible hand is guiding the market. This gets our model out of the gate. If investors were not broadly homogeneous, our models would not work. “The average height of every item in this shed” is not a particularly useful calculation. Which way the causal arrow flows — whether signal drives theory or theory determines what counts as a signal — is an open question.

But there is a second-order sense in which the earlier and later trades are related, in practice: the later participants know about the earlier trade and its price — it is part of that universal corpus of market information, deemed known by all, it informs price formation process: all can thereby infer the trend from prior trades — and use this abstract information to form their bid or ask.

Nomological machines never quite work in the real world

When you bounce a ball, friction, energy loss, structural imperfections, impurities in the rubber and environmental interference frustrate the conditions needed to satisfy the “nomological machine”: the required conditions for Newton’s laws to hold are not present so, when our bouncing ball never quite conserves momentum, we let it pass. It is close enough and usually no one is counting in any case.

This is the sense in which, as Nancy Cartwright puts it, the laws of physics lie. They don’t represent what happens in the real world.

The same applies to the statistical techniques we use to measure market behaviour. Much of the non-homogenous behaviour cancels itself out. Where it doesn’t — where it creates a persistent variance from how a normal distribution would behave over time, we can model that, too, with measures like volatility. We use probabilistic — that is, independence-assuming —techniques to model these second-order corrections like volatility, too.

Why do we assume independence and homogeneity of events? Because otherwise, we could not predict at all. A human being with free will and moral agency does not obey laws of probability. She can put a coin down heads up every time. She can go out of her way to deliberately frustrate any prediction or suggestion her husband another person makes.

“Oh, you predicted heads? Well, I say tails.”

It’s not just that individual humans can do that: they like doing that. Likewise, you can’t draw models that predict the behaviour of dissimilar objects. Statistical rules require homogeneity. The odds of rolling a six hold true for fair dice, but not for carpet slippers or fish.

But this is the magic, so claimed, of big data. All those idiosyncrasies cancel themselves out and leave us with a set of basically homogenous participants. You night not like rice pudding or lentils but over a whole population, a fairly reliable proportion of the population does. We can ignore individuals. The variances they represent are noise. It is our dystopian lot that our institutions and social systems increasingly are configured to ignore us.

BRIAN: “You’re all individuals!”
CROWD: “Yes! We’re all individuals!”
BRIAN: “You’re all different!”
CROWD: “Yes! We’re all different!”
(small voice at the back): “I’m not.”

—Monty Python’s Life of Brian

Our agency and our idiosyncrasies average out. We all want to eat, be warm and dry and have rewarding careers. That we all go about this in subtly different ways doesn’t, to a data aggregator, much matter. Until it does.

For there is a third order of dissimilarities. In times of market stress, other people’s behaviour directly and directionally affects you and your transactions, and your behaviour affects theirs. This is not the irrationality of panic — if each decision were irrational, the effect would be random and the Brownian cancellation effect would come into play and everything would be fine — but an instinctive imitation of whatever it is the surrounding community is doing. THOSE GUYS ARE RUNNING AWAY. I DO NOT KNOW WHY BUT I MUST PRESUME THEY HAVE A REASON. THEREFORE I AM RUNNING AWAY.

This is “memesis”. Most of the time, thanks to the Dunning-Krueger-by-proxy effect or otherwise, we presume the perspective we can bring to the information we have gives us an edge over the crowd, and we are happy to make our own decisions, whose individual variances boil off into Brownian randomness that can be neatly fitted to a standard deviation from the mean. But there are moments — by nature unexpected — when that confidence vanishes. Suddenly our conscious models, theories and nomological machines are less valuable than the tacit information we gather from the changed behaviour of everyone around us. There is something important we don’t know. It is better to mimic the behaviour of those around us. We presume they know — or that they are imitating the behaviour of someone else who knows.

This is the extraordinary behaviour of fish when a shark bursts through the school. This is the bewitching murmuration of starlings over a twilight meadow. In an instant that entropic, Brownian normalcy disappears and every particle darts the same way at once, as if by magic.

We are mesmerised but not surprised to see starlings perform their aerial magic. We would be gobsmacked if a cup of tea did this.

When the planet has unexpectedly gone into lockdown as a result of a global pandemic, buying habits for toilet paper and, oddly, lentils suddenly change. The fact that there are only three tins of lentils left on the shelf leads you to grab them. The fact that there are none leads to a nationwide run on tinned pulses people don’t, in normal times, much like. The Contrarian household still groans under the weight of tinned borloiti beans years after the last new variant.

There are not just these “cry fire in a crowded theatre” effects whereupon everyone stampedes for the exits at once, but second-order effects. You might not wish to head for the exit: you might be strong-willed enough to rise above the madding crowd — but you might still have no choice. You are not independent when your asthma inhaler is in your spouse’s rucksack.

If you are long “on margin” you might wish to ride out a sudden correction by meeting your margin calls. In most dislocations this is the obvious and — if you can manage it, correct — thing to do. You might, per your own books and records, be solvent, well-capitalised and in good standing with your banks, so why not?

But meeting the margin call means drawing on your standby revolving credit facility — you don’t keeps a yard of spare cash off the table for emergencies, right? — but it turns out your bank is, like everyone else, suffering a liquidity squeeze. It evokes some obscure market conditions CP buried in the docs and suspends drawdowns on the RCF as a result. This is nothing to do with you: the bank is managing its own cash position. It needs the money more than you.

At the same time your margin lenders — usually so patient with you, generally genial, good for a knees-up at Ascot and tolerant of peripheral looseness in your margin operations — have had a sense of humour failure. They are apologetic, but they are shipping a shower of grief from the head of risk and have been told to tell you that you today there is no flex. Today the money must be there on time without fail — and for good measure they are jacking up your IM.

You say this is absurd, that everything is fine, but appeals to their better nature and your solid, five-year track record fall upon deaf ears. Today they don’t know what to believe. Normal conditions of trust and amity are suspended. This could be the final round of the prisoner’s dilemma. Anything they can’t see unaided with their own naked eyes could be fake news. The one thing they can see is that everyone else is running for the door.

The value of all that near-perfect market information evaporates and other information, which the market doesn’t have, but until now took for granted — such as the essential viability of systemically important financial institutions and the strength of the commercial imperative — is suddenly much more important. All at once, no-one fancies “taking a view” on anyone’s credit.

Cash is King, Queen, Jack and Ace. There are dazed people in sharp suits wandering around Canary Wharf clutching Iron Mountain boxes.

All indicators are going one way, across the board, in all markets and all asset classes.

Now we find our model has stopped being largely right, or broadly right, or even vaguely right. It is flat-out wrong.

Twenty-five sigma events

If a coin lands tails a hundred times in a row it is either a unique moment in the life of the cosmos or a dicky coin.

If you are the CFO of a bulge bracket Vampire Squid you will earn limited sympathy if you blame your losses on a statistical model, but absolutely none if you blame it on the misbehaviour of the universe. Do not say things like:

“We were seeing things that were 25-standard deviation moves, several days in a row”

Twenty-five sigma events do not happen once, let alone several days in a row. Your model did not work.

This is a tail event. This is what all the meaningful terms in your legal agreements are designed to protect you against.

See also

• Nomological machine
• The purpose of an ISDA
• The map and the territory

References

Tail event
(n.)

1. Statistics: Of a range of possible independent events, one whose frequency is three or more standard deviations from the mean. An event with a low probability.
2. Work life: An unwanted outcome you didn’t expect, to which you weren’t paying attention, and, therefore, for which you don’t think you should be blamed.

Tumbling dice as a nomological machine

When we calculate probabilities — when we roll dice — we are in situations of known risk. Even though dice trajectories are chaotic; even though no two rolls are identical, all this intractable uncertainty is wiped out when the dice come to rest. At that stage we know the range of possible outcomes and their calculated probabilities. On a flat, hard surface, one side must come to rest face-up. There are six equal sides. We deduce each side has a ⅙ probability.

Now every fair die has these same characteristics. It is not just an average across all dice: that some some dice yield probabilities of ⅐, others ⅕ but, on average, they shake out at about ⅙. Every individual die must, within minimal tolerance, yield a ⅙ probability. All dice are functionally identical.

Therefore, when we roll dice to determine an outcome we do not build a statistical model that predicts a ⅙ probability: we build the dice to yield that outcome. A rolling die on a flat surface is what Nancy Cartwright might call a “nomological machine”

By way of side-note, this is a terrible, if accurate, label. “Nomological” means “denoting principles that resemble laws, especially ones describing brute facts of the universe”, so it is spot on, but it is intimidating to a lay reader. It sounds, and is meant to sound, clever.

A “nomological machine” is carefully designed, constrained, hermetically-sealed: a simple system designed to generate the specific outcome an existing theory predicts. It is not a means of proving a theory so much as articulating it. It may be abstract and not even possible in the real world. Rolling fair dice on a flat surface illustrate probabilities. We can co-opt them for a game of monopoly, as a means of generating a random outcome. We can roll dice and say, “look: just as probability theory predicts, over time each side comes up one-sixth of the time.”

Note that if, over time, our dice don’t yield that outcome, we don’t conclude the ⅙ outcome is wrong: we throw out the defective dice.

The “map” and “territory” are, thus, transposed: where usually the have is the abstract simplification of an intractable real world territory, here the “real-world” dice is the map of the territory of a theoretical probability. But it is a map on a 1:1 scale: as far as engineering permits, identical to the territory. Its substrate need not take the form of dice: it could be any contraption that reliably yields a ⅙ probability. Now we all carry difference engines in our pocket, we could get the same outcome with a random number-generator.

Machined dice and the flat, constrained surface on which they fall are not meant to represent “the real world”. They aspire to an idealised platonic utopia, free of friction and caprice, where abstract objects behave yield obediently to the expected statistical outcome: ⅙.

A “loaded” die is a flawed nomological machine. So is a surface like sand which allows a die an ambiguous resting place upon its edge. If, over time you get don't get the ⅙ outcome you expect you don't chuck out the probability theory: you chuck out the dice.

Likewise, if, inside your nomological machine there is a mischievous imp who catches and places the die as it sees fit, the conditions for your probabilistic calculations do not prevail. There must be no interfering causal agency.

“Nomological machines” are highly constrained, artificial environments. If all their conditions are not satisfied in the real world, and we find the world does not obey the model, this does not invalidate the model. This is how, as Nancy Cartwright put it “the laws of physics lie”.

In any case, the circumstances in which the laws of probability hold are highly limited and very artificial. Should the universe “misbehave” then the conditions required for the nomological machine cannot be present.

Boy, did I get side-tracked.

But hold map and territory — model and reality — as an immutable dualism. We live in the territory, and to abstract from territory to map is to cross the mythical threshold from ordinary world to magical model kingdom. Unlike its fictional archetype the model kingdom cannot change the real world. The less correspondence there is between the two, the greater the peril.

So the relationship between map and territory is fraught. Map, territory. Model, reality. Online, offline. Formal, informal. Narnia, the real world. The longer the stay in Narnia, the more we are persuaded by it: the more we build it out by reference to its own terms, its own logical imperatives. As we flesh out the theoretical and logical implications of our models without checking them back to the territory they originally meant to map, we are in danger of amplifying inadvertent implications of the buried differences between our maps and our models. The map of theoretical physics has long since parted from the point where practical comparison is even theoretically possible. There is no possible real world evidence for string theories, multiverses, dark energy or the cosmological constant. For some of these things, we are told, the very act of looking for evidence would destroy it. This is a skeptic-defeat device as powerful as anything found in religion. These are all pure functions of extrapolation from the model. If the model is wrong, all this fantastical superstructure, also, is wrong. Yet the whole superstructure the investment in it, the careers, the billion-dollar particle accelerators, the industrial academic complex behind it — these exist in the real world. These are, seemingly, reason enough to believe, notwithstanding the apparently, unfalsifiably bonkers things these things, with a straight face, tell us must be true.

This is not to say any of this higher order theoretical physics is not true or correct. We laypeople have no reason to doubt the maths . But mathematics is the business of internal logical consistency. It is a closed logical system; a linguistic game. It is the language in which we articulate the model. It has nothing to say about its relationship to the territory. Maths is a language: it is not science.

First, be sure you know which domain is which. Are you trying to fit the world to a model — as you do when flipping a coin or rolling dice — or a model to the world? Volatility calculations, Black-Scholes formulae, You can abstract fit real world to the model a normal distribution is a For events in the real world to confirm to normal distributions, standard deviations, and confident probabilities they must meet the criteria of a nomological machine. All potential events must known, and be independent of each other and our observation of them. If a motivated agent intervenes it can upset the observed behaviour of the system. If you have all that all risks can be calculated and probabilities assigned.

Markets, in the abstract, look just like such a machine. There is a bounded environment, a finite trading day and a limited number of market participants and financial instruments which one can buy or sell. In the modern days of computerised trading everything is very clean, tidy observable, unitary and discrete.

Derivatives trading

In the context of trading derivatives, things that (a) you didn't reasonably expect and that . (b) bugger up your contract.

Credit defaults

A swap being a private, bilateral affair, the most obvious category of tail events is “things which mean your counterparty cannot, or will not, or has not, performed its end of the deal”.

Straight out refusal to — repudiation — is rare, at least without the cloak of some kind of dispute as to whether the party was under such an obligation in the first place.

Inability is the main player here: generally captured by insolvency, and correlative defaults under other agreements.

Much of financial services being a play on leverage — the name of the game being to earn more, with other people’s money, than it costs you to borrow it — many market participants flirt with various formulations of insolvency as a basic business model, so there tend to be some pushback on the parameters of these correlative failures and “ostensible inabilities” to perform. Much of a negotiator’s life is spent haggling about them.

Where refusal or inability to perform cannot be proven, actual failure to pay or deliver ends all arguments. If you actually haven’t performed, it no longer matters why.

There is therefore a sort of hierarchy of these events. Actual default is the safest, and most common, default trigger. Bankruptcy is the next — though there is more looseness around some of its limbs, an administrator actually being appointed, or a petition actually being filmed is clean, public and unlikely to prompt many arguments. Default Under Specified Transaction — that transaction being one to which you are directly a party,

The remaining events are sketchy and unpopular, depending as they do on private information you most likely won't have about thresholds you can't easily calculate. We may argue till we are hoarse about Cross Default. We will not invoke it.

Externalities

There are a category of events which make it impossible even for a solvent counterparty to perform. Change in law, for example — it is not beyond possibility that certain kinds of swaps might be restricted or outlawed altogether or Tax events that make the transaction uneconomic as originally envisaged.

Secondary events of this kind — things that limit a dealer’s ability to hedge, or materially increase its costs of doing so, tend not to be Termination Events partly this reflects a fact not often stated, but nonetheless true: there is a price at which the parties will agree to terminate any swap. Just because a party doesn't have an economic option to terminate the trade doesn't mean it can't terminate the trade. It always has an “at market” option. In liquid markets during times of fair weather this is a source of great comfort; in illiquid markets and at times of stress, less so. A dealer will say, “I will always show you a price. You just might not mind the price, is all.”

Customers have less incentive to break trades if it means realising

See also

• The map and the territory