Pre-emptive strikes on financial risk through unconventional monetary policy amplifies ‘shadow’ short convexity leading to tail risks that are near impossible to gauge. Shadow short convexity describes an immeasurable fragility to change introduced when participants are encouraged to behave in a way that contributes to feedback loops in a complex system.
Shadow convexity reinforces the dominant trend in a hidden non-linear way and effects all participants. The ultimate non-market example of shadow short convexity is the failure of communism and the fall of the Berlin Wall in 1989. Following decades of oppression, the wall literally and psychologically fell when crowds of East Germans gathered at checkpoints and border guards refused to use mass violence to suppress them. The wall collapsed organically and without violence after the suppressed desire for freedom became a self-reinforcing force of change that could no longer be denied.
The ultimate market example of shadow convexity is the role of portfolio insurance in the 1987 Black Monday crash. The portfolio insurance strategy relied upon selling increasing amounts of financial futures to protect against drawdowns in equity markets. The greater the decline in the market the more financial futures were sold to offset the loss contributing to a non-linear feedback loop and causing a -20% single day decline in the S&P 500 index.
Modern markets contain many new sources of shadow short convexity stimulated by extraordinary global monetary policy.
Examples include risk parity, volatility targeting, machine learning, and exchange traded products. All of these structural devices contain a “shadow gamma” or “shadow liquidity” by reacting to market conditions that they themselves influence resulting in self-reflexivity. Volatility targeting and risk parity strategies create feedback loops by increasing and decreasing risk exposure based on volatility observed in the recent past.
As these strategies dominate markets they can begin to influence the same realized volatility used to make their initial risk decision. Risk parity is an example of a strategy that adds shadow short convexity to the system by leveraging short correlations between stocks and bonds.
Machine learning technology is a tool utilized by many high frequency and quantitative trading firms, including Artemis, and relies on advanced statistical methods to recognize non-linear patterns in historical price data. There are many advanced algorithms with fancy names like “neural networks” and “random forests” but they all rely on the same limited history of financial data to make decisions.
Quality data is always more important than the algorithm used. As machine learning gains widespread adoption the models may begin to recognize patterns in data that they themselves influence resulting in feedback loops.
When a self-driving car uses visual pattern recognition algorithms to react to the road it does not adversely change the fundamental reality it was designed to respond to. Now imagine a fleet of self-driving cars that, upon first sign of any risk, avoid accidents by encouraging other drivers to collide with one another. Now you see the problem.
Exchange traded products (“ETPs”) introduce self-reflexivity by creating a highly liquid security (listed stock) that tracks a potentially illiquid underlying instrument (e.g. high yield bonds, commodity futures). Exchange traded products with illiquid underlying assets remind me of a classic song from the Eagles because “you can check out anytime, but you can never leave”. Leveraged ETPs also add to shadow short convexity by requiring non-linear exposure adjustments to linear moves in asset prices.
When combined with a liquidity mismatch any period of sustained buying and selling becomes self-reinforcing. All of the above structures and systems introduce shadow convexity to markets that reinforce the dominant price direction in a non-linear way. This works very well when central banks are providing ample liquidity and reinforcing the status quo but it can and will cut in the other direction. Lower volatility drives lower volatility… and higher volatility drives higher volatility. Minsky once wrote that stability is the greatest source of instability. Shadow convexity is the reason.
The concept of a ‘Black Swan’, defined as an extreme or rare event, has never been more relevant to markets, but the idea is so frequently abused in financial commentary that it risks becoming a cliché. The absurd meme that central banks have eliminated extreme tail risks through accommodative monetary policy, recently repeated by the head of research at a major bank, is part of the institutionalized narrative of moral hazard. By Taleb’s definition, Black Swan events are unpredictable, so how can a central bank prevent something they can’t even identify in the first place? More to this point the investment community has no consistent definition of what tail risk or high volatility even means.
Volatility is about fear... but extreme tail risk is about horror. The Black Swan, as a negative philosophical construct, is when fear ends and horror begins.
Fear is something that comes from within our scope of thought. True horror is not human fear in a definable world, but fear that comes from outside what is definable. Horror is about the limitations of our thinking.
In the novella, “The Call of Cthulhu”, the horror author H.P. Lovecraft describes an ancient and malevolent entity hibernating deep within the earth the sight of which can drive a man to madness. The imprisoned Cthulhu will destroy the world upon its awakening and this is a source of subconscious anxiety for all humankind even if we are individually unaware of its existence.
Cthulhu is a black swan. I’m sorry but the 2007-2008 financial crash was not a black swan. That is a collective lie propagated by policy makers so they don’t cry themselves to sleep at night. Many different people predicted and profited from the 2008 crisis including this author.
A black swan is when things go from bad to uncontrollably bad, when a linear decline becomes an exponential decline. The black swan resembles what amateur screenwriters call the “all is lost moment”. It is not the first act of the horror movie when people start turning into zombies... it is the end of the second act when the hero realizes he is the only person left who is not a zombie.
2008 was about the fear of failing banks and crashing markets... but the true horror was the impending collapse of the entire fiat money system that never came to be. That was the true black swan.
The unpredictable horror of a black swan often occurs following a predictable period of fear. For example, the Black Monday 1987 crash was an unpredictable event that occurred within a predictable crash. In the late summer of 1987 the market was trending lower and financial stress conditions were rising rapidly. Volatility rose even before that fateful Monday increasing from 21.83 at the start of October to 36.37 the day prior to the big crash. By this point, the S&P 500 had already experienced a -14% peak-to-trough drawdown. Many investors ranging from global macro traders to systematic trend followers correctly predicted a crash. Nobody predicted the market would fall -20% in one day or that volatility would peak at 150.
Ironically, if the same price movement occurred today most people would short volatility and buy equities the day prior to Black Monday in anticipation of policy support.
Most crises occur slowly and then suddenly. A devastating earthquake is a tremor that just didn’t stop… and Black Monday 1987 was a crash that just didn’t stop. To this extent sizing long volatility positions into a crisis can yield life changing returns at the right point. Today, due to the actions of central banks, everyone is doing the exact opposite...
If you short fear you must be prepared for horror... in the Prisoner’s dilemma we are one step closer to HORROR