Trading of Crypto-Assets
 

Instructor:           Andreas Park
 

Rotman – Master in Financial Risk Management

Financial Innovation

Background: Trading Crypto-Assets

• This deck supplements my four part lecture series on Blockchain Technology in Finance
• TWO parts
  • Trading in equity markets today
  • Trading in crypto-securities (including coins and tokens)
• Please note: this presentation is under no circumstances to be taken as investment advice or encouragement to engage in activities that may violate current securities law.

Content Overview

Types of traders

Trading in Equity Markets today

Where does trading occur?

Trading in Equity Markets today

How does trading work?

Trading in Equity Markets today

How to access the market?

Trading in Equity Markets today

Retail: Canada

Trading in Equity Markets today

Retail: U.S.A.

Trading in Equity Markets today

Institutions: U.S.A.

Trading in Equity Markets today

Pro Traders

Trading in Equity Markets today

Big Message

Trading in Equity Markets today

• You commonly don't access the market directly.

• Brokers take many decisions but they are bound by regulations.

• Critical: markets are formally linked by best-price rules.

A glimpse of overall infrastructure

Types of traders

Trading in Crypto Markets

Where does trading occur?

Trading in Crypto Markets

How does trading work?

Trading in Crypto Markets

How do you access the market?

Trading in Crypto Markets

Exchanges

Within exchange operation

Trading in Crypto Markets

trade

lend

Settlement

Trading in Crypto Markets

Fully decentralized 1.0

Concerns

Bitcoin prices in USD, May 25 2018, 17 exchanges

Pre-req for Trading on a Crypto Exchange

Arbitrage on a Crypto Exchange

BTC/USD

ask: 7,600 

bid: 7,550

BTC/USD

ask: 7,500 

bid: 7,450

Arbitrage on a Crypto Exchange

BTC/USD

ask: 7,600 

bid: 7,550

BTC/USD

ask: 7,500 

bid: 7,450

Arbitrage on a Crypto Exchange

Spirit of Blockchain: Fully decentralized

Crypto exchanges are a security risk

Crypto exchanges are a security risk

decentralized exchange

Key Components

Idea:

• create a way to exchange items on-chain
• fully decentralized
• \(\to\) no single controlling entity, or location, everything runs with smart contracts

How should one organize DEX trading?

• "DeFi refers to a fast-growing and highly opaque corner of the cryptocurrency market which allows users to engage in a variety of financial activities – including lending, borrowing, and trading derivatives to take on leverage – without an intermediary like a bank.
   
• Given that participants and project developers may remain anonymous, DeFi could present particularly severe financial stability risks.
   
• According to a 2019 Financial Stability Board report, decentralized financial technologies may raise new forms of concentration risks, unclear allocation of liability, and recovery and resolution challenges."

What is decentralized Finance and why should you care

How should one organize DEX trading?

the constant product pricing function

automated market maker

Price mechanism:

• \(X=\) contract balance of asset \(A\)
• \(Y=\) contract balance of asset \(B\)
• \(k=\) invariance factor
• key relation \(k=X\times\ Y\)

Prices

• when you want to sell \(x\le X\) you receive \(y\) that maintains invariance. 
• implied exchange rate: \(e=\frac{x}{y}\)

• maintain constant product post trade: \[k=(X+x)(Y-y)~ \Leftrightarrow~y=\frac{xY}{X+x}.\]

How do you organize DEX trading? EXAMPLE

automated market maker

Contract deposit:

How do you organize DEX trading? EXAMPLE

automated market maker

sell 4 DAI for USDC

How do you organize DEX trading? EXAMPLE

automated market maker

Problem: large "slippage" (or price impact)

• imagine: deposit is 100 DAI & USDC:
  • \(k=100\times100=10,000~\to\) for \(x=4\) need \(y=100-10000/104=3.85\)
     
• imagine: deposit is 10,000 DAI & USDC:
  • \(k=10,000\times10,000=100,000,000~\to\) for \(x=4\) need \(y=10,000-100,000,000/10,004=3.998\)
     
• ​\(\to\) the more money is in the contracts, the lower the price impact

How do you organize DEX trading? other mechanisms

automated market maker

• anyone can become a liquidity provider when supplying both sides of a pair
   
• trades carry a fee of 30bps \(\to\) paid to liquidity providers (pooled)
   
• LPs still face opportunity costs relative to all other assets \(\to\) income must be sufficient

supercool feature

automated market maker

• establish and sell a new token
   

  • create token
  • deposit token and counterasset (e.g., DAI)
  • \(\to\) opening price
  • \(\to\) new purchases will increase price

 

Dark side of DEx trading: Miner extractable value

Mempool \(\Rightarrow\) Front-Running!

So what's the Problem?

Problem: MEMPOOL Frontrunning is intrinsically profitable

\(X\)

\(Y\)

Problem: MEMPOOL Frontrunning is intrinsically profitable

\(X\)

\(Y\)

\(Y-y'\)

\(X+x\)

\(Y-y'-y''\)

\(X+2x\)

\(y'=y''~\Rightarrow\)

front-running is not intrinsically profitable

What would be desirable?

Hard-Coded Market Making

1. Time consistent: cannot profitably split orders over time.
    
2. Front-running is not intrinsically profitable. 
    
3. Liquidity splitting invariance
    
4. No Multi-venue arbitrage/ping-pong trading

CPAMM

canonical

How do you organize DEX trading?

Atomic swaps

How do you organize DEX trading?

Liquidity?

• Laissez-faire: Etherdelta or Kyber
  • people submit contracts (limit orders) on-chain
  • system collects info
  • system offers "tools" to trade against standing contracts

• Hybrid: 0x
  • "dark liquidity"
  • off-chain/sidechain purchase/sale agreements
  • system matches compatible orders and posts on-chain

• Automated market maker (AMM) (Uniswap)
  • AMM holds assets on both sides
  • offers two-sided quotes (\(\to\) always liquid)
  • prices adjust continuously to demand/supply shifts

How do you organize DEX trading?

automated market maker

Price mechanism:

• risk-neutral "invariance" pricing
• at price, contract (AMM) is indifferent between buying and selling
• \(X=\) contract balance of asset \(A\)
• \(Y=\) contract balance of asset \(B\)
• \(k=\) invariance factor
• key relation \(k=X\times\ Y\)

Prices

• when you want to sell \(x\le X\) you receive \(y\) that maintains invariance. 
• implied exchange rate: \(e=\frac{x}{y}\)

superannoying feature

automated market maker

• front-running

  • transactions enter mem-pool

  • \(\to\) all visible there

  • arbitrageur make instant-swap trade at higher gas price

    • \(\to\) trade instead of original trade

    • \(to\) reverse to gain slippage from earlier trader

Convenient feature for arbitrage: Flash loans (Flash swaps)

automated market maker

• take three pairs (ignore that BTC is not directly on Ethereum)

  • BTC-DAI

  • ETH-BTC

  • ETH-DAI

• three pairs must satisfy non-arbitrage condition
• e.g. if ETH:DAI =1:100 and BTC:DAI=1:10000 then BTC:ETH=1:100
• say BTC:ETH=1:200 then
  • borrow (say) 10,000 DAI
  • use DAI to buy  1 BTC
  • sell 1 BTC for 200 ETH and
  • sell 200 ETH for 20000 DAI
  • of which you use 10,000 DAI to repay loan and pocket 10,000

How does it look?

automated market maker

Summary of workflow

Main differences

Trading in Equity vs Crypto

Equity Market

Crypto Market

Some data

based on Khapko, Malinova, Park, and Zoican (2018)

Some data

Some data

Some data

Distribution of changes in beneficiary ownership

Transactions costs

Source: Interactive Brokers

Broker
Level

Historically: “Tether Platform currencies are 100% backed by actual fiat currency
assets in our reserve account.”

Text

IS BITCOIN REALLY UN-TETHERED?
JOHN M. GRIFFIN and AMIN SHAMS
Journal of Finance 2020

vs.

Text

IS BITCOIN REALLY UN-TETHERED?
JOHN M. GRIFFIN and AMIN SHAMS
Journal of Finance 2020

vs.

Text

IS BITCOIN REALLY UN-TETHERED?
JOHN M. GRIFFIN and AMIN SHAMS
Journal of Finance 2020

Flow of events

• Tether is authorized
• moved to Bitfinex 
• then slowly distributed to other Tether-based exchanges (Poloniex and Bittrex)
• \(\to\) almost no Tether returns to the Tether issuer to be redeemed
• Kraken (major exchange for Tether\(\to\) USD) accounts for only a small proportion of transactions

Figure 1. Aggregate Flow of Tether between Major Addresses

If Tether is printed independently
of demand and pushed onto the market then ...

• \(\nearrow\) money supply in crypto
• \(\nearrow\) cryptocurrency prices through artificial demand 
• if traded strategically, Tether can further impact and manipulate Bitcoin prices

Is the Price Effect Economically Important?

the "normal-times" returns

Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?
Foley, Karlsen, Putniņš, 2019, Review of Financial Studies

Cryptocurrency Pump-and-Dump Schemes
Tao Li, Donghwa Shin, and Baolian Wang, 2020

• prices of pumped cryptocurrencies begin rising five minutes before a P&D starts.

• some pump group organizers offer premium memberships to allow certain investors to receive pump signals before others do (\(\to\) insiders)
• Average P&D insiders make one Bitcoin (about $10,000) in profit
• only investors who buy in the first 20
  seconds after a P&D begins make a profit

• P&D= price manipulation that involves artificially inflating an asset price before selling the cheaply purchased assets at a higher price.
• Such schemes are most common with microcap stocks and have recently become popular in the cryptocurrency market (Shifflet and Vigna, 2018).
• U.S. Securities and Exchange Commission (SEC): P&D is illegal in the stock market
• Regulation of P&Ds in the cryptocurrency market is weak or nonexistent.

Market Design with Blockchain Technology

Katya Malinova and Andreas Park

What is different?

1. Multiple trading protocols are possible

User-facing exchange mask

Fully Decentralized, "OTC",

Peer-to-Peer Exchange

What is different?

2. High Level of Transparency

See transactions between "addresses" (="IDs")

What is different?

3. You can tell who owns what

To sum up: What is different?

1. Exchange-trading and Peer to Peer is possible
   • current world peer-to-peer -- through intermediaries
     • a dealer/market maker is on one side of trade
     • parties know who they are trading with
   • technology enables frictionless value transfer
2. Past transactions are visible
   • may be able to see frequent "traders"
3. Current holdings are visible
   • may be able to tell who the "whales" are

=> Informational environment changes drastically

 Key: wallets/addresses = IDs  but NOT = traders

Research Question

How does the design of ledger transparency and identifier-usage with possible P2P interactions affect trading behavior and economic outcomes?

• possible ledger transparency regimes:
  • visible to all
  • hidden (from some)
• possible identifier-usage regimes:
  • mandate single IDs per entity
  • allow multiple IDs
    • allows to obfuscate holdings (Buterin 2015)

Who benefits and loses under which regime?

Literature

1. Economics of blockchain protocols and transaction costs
   • there is a large literature in computer science, e.g., Eyal and Sirer (2014)
   • Gans and Halaburda (2015); and Halaburda and Gandel (2016)
   • Budish (2018), Saleh (2017), Biais, Bidiere, Bouvard, Casamatta (2018)
   • Huberman, Leshno, and Moallemi (2017), Easley, O'Hara, Basu (2018)
2. Smart contracts and other uses of blockchain
   • Cong and He [2017], Yermark (2017)
3. Blockchain and financial securities/markets
   • Boehm et al [2015]; Harvey [2016], Raskin and Yermack [2016; 2017]; Aune, Krellenstein, O’Hara, and Slama [2017]

Model Ingredients

• Risky asset, normally distributed 
• Two large investors

  • Each period one is hit with size Q=1 liquidity shock.

  • Other can absorb the shock at zero cost.

• ​Continuum of 1/    small investors
  • ​trade with probability       at "public" price
  • each period, mass 1 wants to buy, mass 1 wants to sell
• Infinitely many trading periods

Model Ingredients:
Trading and Timing

• When hit with a shock, the "liquidity trader" (LT) may:
  • trade peer-to-peer (OTC)  (with small and/or large peers)
    • other large: "liquidity provider" (LP)
  • trade with a risk-averse intermediary at
    
     
    • Intermediary's inventory I "shifts" the public price
  • net-trades with intermediary = inefficient transfer of risk 
• Unfilled positions clear with intermediary at end of stage game.

Model Ingredients: Costs

• Data processing/complexity to contact q
• Quadratic cost to contact mass q of IDs:
  • cost c is a loss to aggregate welfare
  • pay        and trade quantity
• Linear mining/validation cost:
  • pay       to trade with     IDs

Direct

• LT to LP: Buy quantity Q at price p?
• 1. LP buys Q from intermediary and moves the "public price" P to​
      
  2. LP to LT: "sell you Q at price ≫ p?"
• Front-runner pays validation costs.

Indirect

Model Ingredients: Transparency of Ownership

1. Full transparency = common knowledge of who is large
   • assume single ID (since validation costs increase in # of IDs)
2. No transparency
   • only single ID allowed
3. No transparency (ownership cannot be inferred)
   • continuum of IDs (to obfuscate ownership)

Benchmark:
fully transparent (single ID) ownership

Options for Large Trader

Trade with small investors and intermediary

Trade with large investor
 

• costs:
  • complexity + validation
  • intermediation

• costs
  • reveal info about the trading needs
  • => [model choice]:
    LT may get “front-run” by LP.

​​

Repeated setting:

Front-running is punished by “grim trigger” & trade forever with small and intermediary.

Single shot:
LP always extracts all surplus (or would front-run).

The Benchmark Equilibrium

1. In a repeated game, "social norms" have bite and front-running can always be avoided.
2. LT always trades with LP.
3. LT and LP share the cost savings.
4. Price concession
   • For small discount factor (≈ infrequent interaction) price concession is necessary.
   • For large enough discount factors (≈ frequent interactions), price concession = 0 is an equilibrium.

Opaque single ID ownership

Equilibrium

• The optimal mass of IDs to contact is independent of the intermediary's inventories/public price.
• Mass x* depends on:
  •   : probability of small traders accepting the offer
  •   : the (il-)liquidity of the intermediated market
  •   : complexity/data processing costs.

• When the  validation cost is not too large,              , the liquidity trader trades with both continuum & intermediaries

Opaque multi-ID ownership

Acceptance Probabilities in Opaque Settings

small traders

large trader

small traders

large trader

small traders

large trader

filled

unfilled

Opaque Single ID

Opaque Multi-ID: LP accepts

Opaque Multi-ID: LP rejects

Decision problem LT

accept offer

"target" small investors only

• (small) price concession to entice the large trader (but also paid to and "wasted on" small traders)
• larger amount => complexity costs

• no price concession
• smaller amount
• => expensive interaction with intermediary
• => smaller complexity cost

Decision problem LP

"target" IDs of both: large and small

front run

• incurs validation fees when front-running

Equilibrium & More

Result 1: There exists an equilibrium with no front-running where

• LP accepts
• price concession = 0

provided

• the discount factor is large enough
  • = frequent interactions.
• or the intermediated market is sufficiently liquid
  • = front running not very profitable (small quantity and low price advantage)
• or validation costs are sufficiently high
  • = sunk cost for front-running too high.

Equilibrium & More

Result 2 (numerical): For small discount (=infrequent interaction) factors, the equilibrium with no front-running where LP accept does not exist. Then:

• In equilibrium, LT offers p = 0 to the continuum, and
• LP's IDs reject the offer.
   

=> "over-trading" with intermediary

• Observation: an increase in the validation cost may curb front-running.

Comparing the designs

Observations

• Trades with intermediary => socially inefficient 
  • better if large traders interact
  • otherwise: intermediary faces imbalance 
• Small with large traders => complexity costs
• By construction, payoffs under the full transparency benchmark are highest.
• The trade-off for opaque regimes are:
  • complexity cost vs
  • intermediation cost

Comparing multi- vs single-ID opaque designs

• Finding 1:
  • When large traders do not trade with each other, the welfare is the same in both opaque systems, irrespective of the ID-ownership setup.
• Finding 2:
  • When large do trade with one another with multi-ID ownership, the welfare in this setting is higher than in the single-ID setting.

Payoffs to Large Traders

Finding 3:

For the average equilibrium stage payoffs of large traders.

1. In multi-ID, when large traders do not interact, eq. payoffs lower than in opaque single-ID.
2. In multi-ID, when large traders interact and p=0, eq.  payoffs larger than in opaque single-ID.
    

Finding 4: (Numerical)

There exist parametric configurations such that large traders trade with each other at p > 0 in the multi-ID ownership setting, but their average equilibrium payoff in the opaque single-ID setting is higher.

Summary

1. "Back office" settlement has important front office implications!
   • with peer-to-peer there are critical design choices
     • Who can see the ledger?
     • How are virtual identities managed?
2. Findings:
   • Transparent ledger with single IDs is welfare optimal and has lowest wealth redistribution (almost by construction)
   • Between (A) public blockchain solution with multiple IDs and (B) private, non-transparent ledger with single IDs:
     • public blockchain privacy solution has higher aggregate welfare
     • but does not necessarily lead to higher payoffs for large investors.