resilience, not stability

Archive for the ‘Market Efficiency’ Category

A Simple Solution to the Eurozone Sovereign Funding Crisis

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In response to the sovereign funding crisis sweeping across the Eurozone, the ECB decided to “conduct two longer-term refinancing operations (LTROs) with a maturity of 36 months”. Combined with the commitment of the members of the Eurozone excluding the possibility of any more haircuts on private sector holders of Euro sovereign bonds, the aim of the current exercise is clear. As Nicholas Sarkozy put it rather bluntly,

Italian banks will be able to borrow [from the ECB] at 1 per cent, while the Italian state is borrowing at 6–7 per cent. It doesn’t take a finance specialist to see that the Italian state will be able to ask Italian banks to finance part of the government debt at a much lower rate.

In other words, the ECB will not finance fiscal deficits directly but will be more than happy to do so via the Eurozone banking system. But this plan still has a few critical flaws:

  • As Sony Kapoor notes, “By doing this, you are strengthening the link between banks and sovereigns, which has proven so dangerous in this crisis. Even if useful in the short term, it would seriously increase the vulnerability of both banks and sovereigns to future shocks.” In other words, if the promise to exclude the possibility of inflicting losses on sovereign debt-holders is broken at any point of time in the future, then sovereign default will coincide with a complete decimation of the incumbent banks in Europe.
  • European banks are desperately capital-constrained as the latest EBA estimates on the capital shortfall faced by European banks shows. In such a condition, banks will almost certainly take on increased sovereign debt exposures only at the expense of lending to the private sector and households. This can only exacerbate the recession in the Eurozone.
  • Sarkozy’s comment also hints at the deep unfairness of the current proposal. If default and haircuts are not on the table, then allowing banks to finance their sovereign debt holdings at a lower rate than the yield they earn on the sovereign bonds (at the same tenor) is simply a transfer of wealth from the Eurozone taxpayer to the banks. Such a privilege may only be extended to the banks if banking is a “perfectly competitive” sector which it is far from being even in a boom economy. In the midst of an economic crisis when so many banks are tottering, it is even further away from the ideal of perfect competition.

There is a simple solution that tackles all three of the above problems – extend the generous terms of refinancing sovereign debt to the entire populace of the Eurozone such that the market for the “support of sovereign debt” is transformed into something close to perfectly competitive. In practise, this simply requires undertaking a program of fast-track banking licenses to new banks with low minimum size requirements on the condition that they restrict their activities to a narrow mandate of buying sovereign debt. This plan can correct all the flaws of the current proposal:

  • Instead of being concentrated within the incumbent failing banks, the sovereign debt exposure of the Eurozone would be spread in a diversified manner within the population. This will also help in making the “no more haircuts” commitment more time-consistent. The wider base of sovereign debt holders will reduce the possibility that the commitment will be reversed by democratic means. The only argument against this plan is that such a concentrated new bank is too risky but that assumes that there is still default risk on Eurozone sovereign debt and that the commitment is not credible.
  • The plan effectively injects new capital into the banking sector allowing incumbent bank capital to be deployed towards lending to the private sector and households. If sovereign debt spreads collapse, then the plan will also shore up the financial position of the incumbent banks thus injecting further capital available to be deployed.
  • The plan is fair. If the current crisis is indeed just a problem of high interest rates fuelling an increased risk of default, then interest rates will rapidly fall to a level much closer to the refinancing rate. To the extent that rates stay elevated and spreads do not converge, it will provide a much more accurate reflection of the real risk of default. No one will earn a supra-normal rate of return.

On this blog, I have criticised the indiscriminate provision of “liquidity” backstops by central banks on many occasions. I have also asserted that key economic functions must be preserved, not the incumbent entities that provide such functions. In times of crisis, central banking interventions are only fair when they are effectively accessible to the masses. At this critical juncture, the socially just policy may also be the only option that can save the single currency project.

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Written by Ashwin Parameswaran

December 10th, 2011 at 12:57 am

Critical Transitions in Markets and Macroeconomic Systems

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This post is the first in a series that takes an ecological and dynamic approach to analysing market/macroeconomic regimes and transitions between these regimes.

Normal, Pre-Crisis and Crisis Regimes

In a post on market crises, Rick Bookstaber identified three regimes that any model of the market must represent (normal, pre-crisis and crisis) and analysed the statistical properties (volatility,correlation etc) of each of these regimes. The framework below however characterises each regime by the varying combinations of positive and negative feedback processes and the variations and regime shifts are determined by the adaptive and evolutionary processes operating within the system.

1. Normal regimes are resilient regimes. They are characterised by a balanced and diverse mix of positive and negative feedback processes. For every momentum trader who bets on the continuation of a trend, there is a contrarian who bets the other way.

2. Pre-crisis regimes are characterised by an increasing dominance of positive feedback processes. An unusually high degree of stability or a persistent trend progressively weeds out negative feedback processes from the system thus leaving it vulnerable to collapse even as a result of disturbances that it could easily absorb in its previously resilient normal state. Such regimes can arise from bubbles but this is not necessary. Pre-crisis only implies that a regime change into the crisis regime is increasingly likely – in ecological terms, the pre-crisis regime is fragile and has suffered a significant loss of resilience.

3. Crisis regimes are essentially transitional  – the disturbance has occurred and the positive feedback processes that dominated the previous regime have now reversed direction. However, the final destination of this transition is uncertain – if the system is left alone, it will undergo a discontinuous transition to a normal regime. However, if sufficient external stabilisation pressures are exerted upon the system, it may revert to the pre-crisis regime or even stay in the crisis regime for a longer period. It’s worth noting that I define a normal regime only by its resilience and not by its desirability – even a state of civilizational collapse can be incredibly resilient.

“Critical Transitions” from the Pre-Crisis to the Crisis Regime

In fragile systems even a minor disturbance can trigger a discontinuous move to an alternative regime – Marten Scheffer refers to such moves as “critical transitions”. Figures a,b,c and d below represent a continuum of ways in which the system can react to changing external conditions (ref Scheffer et al) . Although I will frequently refer to “equilibria” and “states” in the discussion below, these are better described as “attractors” and “regimes” given the dynamic nature of the system – the static terminology is merely a simplification.

In Figure a, the system state reacts smoothly to perturbations – for example, a large external change will trigger a large move in the state of the system. The dotted arrows denote the direction in which the system moves when it is not on the curve i.e. in equilibrium.  Any move away from equilibrium triggers forces that bring it back to the curve. In Figure b, the transition is non-linear and a small perturbation can trigger a regime shift – however a reversal of conditions of an equally small magnitude can reverse the regime shift. Clearly, such a system does not satisfactorily explain our current economic predicament where monetary and fiscal intervention far in excess of the initial sub-prime shock have failed to bring the system back to its previous state.

Figure c however may be a more accurate description of the current state of the economy and the market – for a certain range of conditions, there exist two alternative stable states separated by an unstable equilibrium (marked by the dotted line). As the dotted arrows indicate, movement away from the unstable equilibrium can carry the system to either of the two alternative stable states. Figure d illustrates how a small perturbation past the point F2 triggers a “catastrophic” transition from the upper branch to the lower branch – moreover, unless conditions are reversed all the way back to the point F1, the system will not revert back to the upper branch stable state. The system therefore exhibits “hysteresis” – i.e. the path matters. The forward and backward switches occur at different points F2 and F1 respectively, which implies that reversing such transitions is not easy. A comprehensive discussion of the conditions that will determine the extent of hysteresis is beyond the scope of this post – however it is worth mentioning that cognitive and organisational rigidity in the absence of sufficient diversity is a sufficient condition for hysteresis in the macro-system.

Before I apply the above framework to some events in the market, it is worth clarifying how the states in Figure d correspond to those chosen by Rick Bookstaber. The “normal” regime refers to the parts of the upper and lower branch stable states that are far from the points F1 and F2 i.e. the system is resilient to a change in external conditions. As I mentioned earlier, normal does not equate to desirable – the lower branch could be a state of collapse. If we designate the upper branch as a desirable normal state and the lower branch as an undesirable one, then the zone close to point F2 on the upper branch is the pre-crisis regime. The crisis regime is the short catastrophic transition from F2 to the lower branch if the system is left alone. If forces external to the system are applied to prevent a transition to the lower branch, then the system could either revert back to the upper branch or even stay in the crisis regime on the dotted line unstable equilibrium for a longer period.

The Magnetar Trade revisited

In an earlier post, I analysed how the infamous Magnetar Trade could be explained with a framework that incorporates catastrophic transitions between alternative stable states. As I noted: “The Magnetar trade would pay off in two scenarios – if there were no defaults in any of their CDOs, or if there were so many defaults that the tranches that they were short also defaulted alongwith the equity tranche. The trade would likely lose money if there were limited defaults in all the CDOs and the senior tranches did not default. Essentially, the trade was attractive if one believed that this intermediate scenario was improbable…Intermediate scenarios are unlikely when the system is characterised by multiple stable states and catastrophic transitions between these states. In adaptive systems such as ecosystems or macroeconomies, such transitions are most likely when the system is fragile and in a state of low resilience. The system tends to be dominated by positive feedback processes that amplify the impact of small perturbations, with no negative feedback processes present that can arrest this snowballing effect.”

In the language of critical transitions, Magnetar calculated that the real estate and MBS markets were in a fragile pre-crisis state and no intervention would prevent the rapid critical transition from F2 to the lower branch.

“Schizophrenic” Markets and the Long Crisis

Recently, many commentators have noted the apparently schizophrenic nature of the markets, turning from risk-on to risk-off at the drop of a hat. For example, John Kemp argues that the markets are “trapped between euphoria and despair” and notes the U-shaped distribution of Bank of England’s inflation forecasts (table 5.13). Although at first glance this sort of behaviour seems irrational, it may not be – As PIMCO’s Richard Clarida notes: “we are in a world in which average outcomes – for growth, inflation, corporate and sovereign defaults, and the investment returns driven by these outcomes – will matter less and less for investors and policymakers. This is because we are in a New Normal world in which the distribution of outcomes is flatter and the tails are fatter. As such, the mean of the distribution becomes an observation that is very rarely realized”

Richard Clarida’s New Normal is analogous to the crisis regime (the dotted line unstable equilibrium in Figures c and d). Any movement in either direction is self-fulfilling and leads to either a much stronger economy or a much weaker economy. So why is the current crisis regime such a long one? As I mentioned earlier, external stabilisation (in this case monetary and fiscal policy) can keep the system from collapsing down to the lower branch normal regime – the “schizophrenia” only indicates that the market may make a decisive break to a stable state sooner rather than later.

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Written by Ashwin Parameswaran

July 29th, 2010 at 3:27 am

Do Investors Prefer Negative Skewness?

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Bootvis asks in a comment on my previous post:

“Financial theory says that rational investors should prefer positive skewness. This is proven under some weak assumptions in “On The Direction of Preference for Moments of Higher Order Than The Variance” by Scott and Horvath (1980) (I can only find it on jstor, behind a wall ).What’s your view on this discrepancy?”

I have not read the above paper and do not have access to JSTOR either. So the below response is just my broad view on the topic.

Agents prefer Negative Skewness

My emphasis so far has been on the preference for maximising negative skewness from an agent’s perspective in a principal-agent relationship. This preference is exacerbated by the moral hazard subsidy. I conclude that the combination of the moral hazard subsidy and the principal-agent problem allows agents to simultaneously maximise negative skewness and improve the risk-return trade-off for owners by increasing leverage.

Whether investors who are not agents would prefer negative skewness is a trickier question. Taleb in this paper clearly concludes that investors prefer negatively skewed bets. But as Bootvis mentions, this contradicts the consensus opinion of financial theory that investors prefer positive skewness. An obvious example of the preference for positive skewness is the phenomenon of “longshot bias” or the popularity of lotteries.

Kahneman-Tversky on Longshots and Black Swans

Kahneman and Tversky offer one way to reconcile these two viewpoints in this paper where they argue that “impossible” events, i.e. black swans, are neglected whereas “possible” but low probability events, i.e. longshots, are overweighted. Preference for negative skewness is not operative for mildly skewed payoffs. It is operative for severely skewed payoffs. As expressed by Kahneman and Tversky: “A change from impossibility to possibility or from possibility to certainty has a bigger impact than a comparable change in the middle of the scale.” In other words, there is a “category-boundary effect” when an event deemed impossible becomes possible. The event is significantly underweighted when deemed impossible and overweighted when it is suddenly deemed possible i.e. the lottery effect only kicks in when the event is deemed possible.

This phenomenon also explains the violence of market reaction and the dramatic move in market prices around this boundary. In fact, it can be argued that the change in market prices itself can cause a move in investor views across this category-boundary in a positive feedback process. For example, if market prices suggest that a tail risk is not improbable, this alone may incentivise economic actors to purchase insurance against the event.

Any behavioural explanation that invokes Kahneman and Tversky does not apply to “rational” investors as defined in modern financial theory. For example, the underweighting of tail events can be explained as a result of investors utilising the “Availability Heuristic” and inducing the probability distribution from past experience. As Andrew Haldane notes: “The longer the period since an event occurred, the lower the subjective probability attached to it by agents (the so-called “availability heuristic”). And below a certain bound, this subjective probability will effectively be set at zero (the “threshold heuristic”).”

Is a Preference for Severe Negative Skewness Irrational?

I would argue that using such heuristics may even be rational when not judged against the unrealistic standards of homo economicus. Inducing probabilities from past experience may be entirely “rational” given bounded rationality and an uncertain environment. As WB Arthur puts it: “Agents “learn” which of their hypotheses work, and from time to time they may discard poorly performing hypotheses and generate new “ideas” to put in their place. A belief model is clung to not because it is “correct”—there is no way to know this—but rather because it has worked in the past, and must cumulate a record of failure before it is worth discarding.”

It can be extremely difficult to ascertain the true distribution of an extremely negatively skewed bet from historical data. A long run without an observed loss makes us less confident about any initial negative thesis. This is also the primary explanation for why we prefer longshots in horse races or play the lottery. Both are fundamentally less uncertain than financial markets. At least we know the full set of outcomes that are possible in a horse race ! Real life markets are nothing like betting markets. They are dominated by true uncertainty and practitioners derive shaky conclusions from historical data and experience. Statistically, it can be extremely difficult to differentiate between alpha and extreme negative skew.

A More Profound “Moral Hazard”

Severely negatively skewed bets usually blow up under conditions of severe distress in the economy when the government is likely to intervene strongly to prevent systemic collapse. As David Merkel mentions in this note, the Great Moderation has been characterised by a Fed that is willing to cut interest rates at the smallest hint of trouble, even in situations where systemic risk was far from severe.

The current “no more Lehmans” policy is practise means that the Fed and the Treasury will do anything to prevent negative tail scenarios. In the face of such an explicit insurance policy, selling tail events may be entirely rational.

Negative Skewness and Fixed Income Markets

Taleb essentially denies that even longshots are overpriced in financial markets. I am not convinced that moderate negative skewness is at all “preferred”. Moreover, most of the empirical evidence he presents pertains to severely skewed payoffs. But there is one point he raises in a reply to Tyler Cowen’s review that deserves more analysis. The vast majority of blowups that Taleb recounts are in the fixed income markets.

Indeed, I think the preference for negative skewness is most relevant in fixed income markets. The original fixed income instrument i.e. the bond has an extremely negatively skewed payoff by construction as does the original “alpha” strategy, the carry trade. Secondly, fixed income markets are dominated to a much larger extent by banks and other agents who are compromised by the moral hazard and/or principal-agent problem. Third, the nature of structured product markets in fixed income are dominated by new methods to construct negatively skewed payoffs. To give a few examples, callable range accruals in interest rate products, the PRDC in currency products and almost any credit structured product that aims to achieve a AAA rating like the leveraged super-senior.

This is not to deny the popularity of severely negatively skewed payoffs in equities (for e.g. the reverse convertible note). But they are nowhere near as predominant.


The moral hazard subsidy, the principal-agent problem and investor “irrationality” each incentivise economic actors to take on considerably negatively skewed bets. Assessing the relative contributions of each from historical market data is extremely difficult given that there is no plausible way to separate the effect of the three causes. The problem is exacerbated by the difficulty in drawing any conclusions about tail events from a study of historical data. However, the concentration of historical blowups in fixed income markets leads me to suspect that the combination of moral hazard and the principal-agent problem had a more prominent role in fuelling the crisis than genuine “irrationality”.

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Written by Ashwin Parameswaran

January 13th, 2010 at 5:17 pm

Efficient Markets and Pattern Predictions

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Markets can be “inefficient” and yet almost impossible to beat because of the existence of “Limits to Arbitrage” . It is essential not only to have the correct view but also to know when the view will be realised.

Why is it so difficult to time the market? Because the market is a complex adaptive system and complex adaptive systems are amenable only to what Hayek called “pattern predictions”. Hayek introduced this concept in his essay “The Theory of Complex Phenomena” where he analysed economic and other social phenomena as “phenomena of organised complexity” (A term introduced by Warren Weaver in this essay).

In such phenomena, according to Hayek, only pattern predictions are possible about the social structure as a whole: As Hayek explained in an interview with Leo Rosten:

“We can build up beautiful theories which would explain everything, if we could fit into the blanks of the formulae the specific information; but we never have all the specific information. Therefore, all we can explain is what I like to call “pattern prediction.” You can predict what sort of pattern will form itself, but the specific manifestation of it depends on the number of specific data, which you can never completely ascertain. Therefore, in that intermediate field — intermediate between the fields where you can ascertain all the data and the fields where you can substitute probabilities for the data–you are very limited in your predictive capacities.”

“Our capacity of prediction in a scientific sense is very seriously limited. We must put up with this. We can only understand the principle on which things operate, but these explanations of the principle, as I sometimes call them, do not enable us to make specific predictions on what will happen tomorrow.”

Hayek was adamant however that theories of pattern prediction were useful and scientific and had “empirical significance”. The example he drew upon was the Darwinian theory of evolution by natural selection, which provided only predictions as to the patterns one could observe over evolutionary time at levels of analysis above the individual entity.

Hayek’s intention with his theory was to debunk the utility of statistics and econometrics in the forecast of macroeconomic outcomes (See his Nobel lecture). The current neoclassical defense against their inability to predict the crisis takes the other extreme position i.e. our theories are right because no one could predict the crisis. This contention explicitly denies the possibility of “pattern predictions” and is not a valid defense. Any macroeconomic theory should be capable of explaining the patterns of our economic system – no more, no less.

One of the key reasons why timing and exact prediction is so difficult is the futility of conventional cause-effect thinking in complex adaptive systems. As Michael Mauboussin observed, ” Cause and effect thinking is futile, if not dangerous”. The underlying causes may be far removed from the effect, both in time and in space and the proximate cause may only be the “straw that broke the camel’s back”.

Many excellent examples of “pattern prediction” can be seen in ecology. For example, the proximate cause of the catastrophic degradation of Jamaica’s coral reefs since the 1980s was the mass mortality of the dominant species of urchin (reference). However, the real reason was the progressive loss of diversity due to overfishing since the 1950s.

As CS Holling observed in his analysis of a similar collapse in fisheries in the Great Lakes:

“Whatever the specific causes, it is clear that the precondition for the collapse was set by the harvesting of fish, even though during a long period there were no obvious signs of problems. The fishing activity, however, progressively reduced the resilience of the system so that when the inevitable unexpected event occurred, the populations collapsed. If it had not been the lamprey, it would have been something else: a change in climate as part of the normal pattern of fluctuation, a change in the chemical or physical environment, or a change in competitors or predators.”

The financial crisis of 2008-2009 can be analysed as the inevitable result of a progressive loss of system resilience. Whether the underlying cause was a buildup of debt, moral hazard or monetary policy errors is a different debate and can only be analysed by looking at the empirical evidence. However, just as is the case in ecology, the inability to predict the time of collapse or even the proximate cause of collapse does not equate to an inability to explain macroeconomic patterns.

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Written by Ashwin Parameswaran

December 31st, 2009 at 10:52 am

John Hempton on Efficient Markets

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John Hempton has a great post on the difficulty of “beating the market” even if one possesses superior insight or knowledge. I would just add the following:

It is common among commentators to conflate two very different assertions:

  1. It is extremely difficult to “beat the market”
  2. Markets are efficient.

The common error is to assume that 1 proves 2 which is most definitely not the case. Shleifer and Vishny discussed this in their paper on the “Limits to Arbitrage”.

Timing is extremely important especially when taking a short position for many reasons:

  1. Short equity is a short volatility position i.e. limited upside, unlimited downside. At the very least, margining requirements in the interim “inefficient” period may kill you before the market corrects. Long equity positions can atleast be left alone if liquidity permits and if the position is not leveraged.
  2. Principal-agent problem: Fund managers need to get the timing spot on when they are taking on contrarian positions. Else, they will be fired by their investors long before the market corrects.
  3. Even if one is not an agent, simple uncertainty means that we’re never certain about our judgement. The longer the market refuses to come around to our viewpoint, the less certain we become and the more tempted we are to liquidate.

As John mentions, timing the market requires not only holding the contrarian view but knowing when this view will dissipate through the market. “Wisdom of the Crowds” explanations of the market require that the uninformed majority hold sufficiently diverse opinions. Given that betting on Kodak’s demise is tantamount to betting on a technological paradigm shift, the “crowd” by definition is not diverse and wedded to the old paradigm.

This is essentially the reason most contrarian investors are long-only long-term investors. This is the style of investing that Jack Treynor called betting on “slow travelling ideas”. The always excellent Michael Mauboussin has a discussion on wisdom of crowds and slow travelling ideas here and here.

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Written by Ashwin Parameswaran

December 30th, 2009 at 11:28 am

Posted in Market Efficiency