Tether Crypto Props up Toxic Crypto Carousel

Tether Props up Toxic Crypto Carousel : This is from the FT today: “The CFTC order found that Tether relied on unregulated and third-party entities to hold funds, including reserves.

The regulator’s order also found that Tether transferred reserves to Bitfinex, including when the exchange was experiencing what its chief financial officer described as a liquidity crisis.”

Back in 2018, when USDT cap was less than $3 billion, Tether’s “reserves” were loans to Bittrex — and other crypto exchanges — with little (or only non-fiat) collateral.

Today, we have $69 billion USDT cap of which around 50% of the reserves are loans to Binance and other exchanges. Since at least 2020 these exchanges have unwittingly been operating as toxic playgrounds for shark prop traders.

A number of questions arise: First, why does Binance need these USDT loans?

Binance brokerage takes fiat currency from retail investors in return for USDT, in order that retail can trade on the Binance exchanges. In fact, Binance has grown so quickly, precisely because retail traders (little fish) attract bigger and bigger fish. The big fish want to avoid trading with each other (that is what we call “toxic flow”) but there are plenty of little fish on Binance — and other exchanges like Bybit, OKEx and Huobi, but Binance is by far the biggest.

So, with all this fiat on–boarding through its brokerage services, Binance custody services should be holding a lot in fiat reserves, yes? Actually no, read on…., your fiat currency (dear retail investor) goes to prop up the losses made by the Binance clearing house.

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Binance has an “insurance fund” which takes over liquidated positions. When volatility arrives, the fund trades in the open market to close out all the liquidated positions it has assumed, using a high-frequency algo of course. But Binance’s algo is no match for those developed by shark prop traders such as Alameda, Cumberland, Jump etc.

Sharks like Alameda and Cumberland can use Binance as their playground.

Not just Binance, most of the other exchanges too — except perhaps not FTX, because it is owned by a shark, and sharks don’t poison their own ground.

The exchange’s insurance fund (sometimes called a guarantee fund) is forced to assume liquidated positions that must be traded against the sharks. But at least it is on their home playground, albeit one that has been made rather toxic by the sharks. No wonder, therefore, that Binance likely pulled their own plug on May 19. Simple logic tells us that when the lights went out on this crazy Binance carousel, the insurance fund instantly stopped bleeding.

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The sharks target a playground exchange and do a high-frequency ‘pump-and-dump’ of prices on it, thus creating volatility. Once the retail investors are wiped out by this volatility, the Binance clearing house insurance fund is left facing the sharks on the Binance playground. Of course, not just Binance — all these exchanges insurance/guarantee funds have been losing heavily — their algos are helpless compared with those used by the sharks.

So, when the sharks stir up the volatility, Binance (and other exchanges) experience a liquidity crisis.

Then Tether comes in with another loan of USDT — to keep on the lights of this toxic crypto carousel. But the exchanges have nothing like the fiat reserves that they should have as collateral for these USDT loans. Your precious fiat currency deposit with Binance brokerage (dear retail crypto investor) didn’t stay with Binance custody for long. It was long ago eaten by the sharks .

I think the shark prop traders are the real culprits, although at this point, now the story is clear, neither Binance nor Tether has any excuse to allow the toxic crypto carousel to keep on turning.

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Tether Props up Toxic Crypto Carousel Written by Professor Carol Alexander

Professor of Finance at Sussex, a visiting Professor at Peking University HSBC Business School, and Co-Editor of the Journal of Banking and Finance.  I have a BSc Maths with Experimental Psychology and a PhD in Algebraic Number Theory. I took a post-doc in Amsterdam, worked as a bond analyst for Phillips and Drew. And attended the London School of Economics as a Research Assistant in game theory/labour economics also taking their MSc in Mathematical Economics and Econometrics. In 1985 I took a lectureship in Mathematics and Economics at the University of Sussex. While also designing risk, pricing, hedging and trading models for investment banks, fund managers and exchanges.

Then I dropped the Economics side at Sussex to work half-time as Academic Director for Algorithmics Inc.

My PhD thesis, entitled Integral Bases of Dihedral Number Fields was supervised by Walter Ledermann at the Universty of Sussex. After a post-doc at the Universty of Amsterdam. A rather tortuous year as a bond analyst for Phillips and Drew. In addition, a delightful Masters in Mathematical Economics and Econometrics at the London School of Economics. I returned to Sussex as a lecturer in Mathematics and Economics. That was in 1985, by which time my research interests had turned to game theory.

However, after the 1987 Black Monday crash in global financial markets my econometric skills were in greater demand. And my social conscience drew me away from game theoretic research into something more practical. I undertook various consultancy roles for investment banks and other financial institutions. Where I worked with computer programmers to implement models for risk analysis and portfolio management. This way, I became drawn to research in financial risk management. Investigating the properties of various new econometric models for market risk. Including different types of generalised autoregressive conditional heterscedasticity (GARCH) models. As well as applied research on active and passive fund management. From that time on, almost all my research has been with the wonderful PhD students that I have had the privilege to supervise.

In 1997 I left academia entirely to be a director of Nikko Global Holdings and Head of Market Risk Modeling (UK).

I briefly led a team of about a dozen PhDs. We designed and built new indexing products, but the London office closed shortly after I started. I took the opportunity to write my first book (Market Models, Wileys 2001). Then, in 1999, I became a professor of finance at the ICMA Centre at Reading University. I was also Risk Research Advisor, SAS (USA) and Chair of the Board of PRMIA (Professional Risk Manager’s International Association).  In 2013 I returned to Sussex, heading the development of the Department of Business and Management. Before it split into three to become the new Business School.

Further econometric research on estimation of general discrete-time stochastic processes for financial asset returns naturally shifted my attention towards the implied measure. At which point I necessarily became a rather inefficient autodidact in various elements of mathematical finance. In this sphere I developed pricing and hedging models for various types of options, exotic and otherwise. And with two very talented PhDs we proved some classic theoretical results on scale invariance and generalised aggregation properties.

Likewise, more applied mathematical finance research converged on volatility indices. And higher moment risk premia, and on trading these premia through futures and exchange traded products.

While writing my 4-volume textbook Market Risk Analysis (Wileys, 2008). Walter Ledermann read parts of the first volume Quantitative Methods in Finance. In his early career as a young mathematician in Edinburgh Walter had done some interesting research on correlation matrices. And after reading my textbook he proved the last theorem of his life at the age of 97. By coincidence, I was supervising the PhD of his grandson at the time. Together Dan, Walter and I wrote the first paper on random orthogonal matrix (ROM) simulation. Timing the publication for the centenary of Walter’s birth. In his honour, we named the Ledermann matrix that Walter discovered. As the first in a whole class of L-matrices that Dan and I developed. I continue to work on ROM simulation. In addition, have also developed another new type of simulation model based on factor quantiles.

Currently, the main focus of my research is on the exciting new universe of crypto assets and their derivatives.

Tether Props up Toxic Crypto Carousel