The Adoption of Blockchain-based Decentralized Exchanges
Ruizhe Jia & Agostino Capponi
Discussion by Katya Malinova
DeGroote School of Business
McMaster University
NFA 2021 (virtual)
- asset class
- common resource
Decentralized finance (DeFi): why exciting?
DeFi: provision of financial services without the necessary involvement of a traditional financial intermediary
blockchain
Decentralized Exchanges
Idea:
- use blockchain to exchange items
- fully decentralized
- no single controlling entity, nor location,
- everything runs with smart contracts
How?
Decentralized Exchanges
Basic idea: (great!)
- share the common resource:
- pool the liquidity!
- liq. providers:
- deposit funds on both sides (e.g., and ) to a liquidity pool
- share gains & losses, don't compete with each other
- liq. demanders:
- trade against these funds
- e.g., send to the pool and get from the pool
Question:
- how to set the price/exchange rate?
pricing mechanism:
- A= contract balance of token A
- B= contract balance of token B
- k= invariance factor
- key relation k=A×B (constant product rule)
- (Other F(A,B)=k possible)
Automated Market Maker
- send ΔA=5 of tokens A
- → receive ΔB of token B s.t.
- k=10×10=(10+5)×(10−ΔB)
- → get 3.33 tokens B
Does/can this pricing function work?
10
10
15
6.67
- This paper (see also Lehar & Parlour (2021) -- next in this session!):
"Unbundle" liquidity provision & price discovery- → possible exploitable arbitrage opportunities = losses to liq. providers
- → "liquidity freezes" when exchange rates are too volatile
- AMM best for "stable" pairs and/or those with high private value trading vol
- a combo of linear & constant product pricing mitigates -- but doesn't eliminate -- this problem
- → possible exploitable arbitrage opportunities = losses to liq. providers
- Park (2021) (tomorrow at the NFA): Mechanical pricing + "tech details" → possibilities for malicious behaviour (= losses/costs to investors)
- The price computed using contract balances at settlement
- → opportunities for profitable "front-running" while in mempool (between "send the transaction in" and "settle")
- proposes a different, pricing function, drawing on insights from microstructure
- The price computed using contract balances at settlement
Possible issues?
Very interesting paper: cleanly & clearly drills down to the economics of swap exchanges
- participants incentives
- → sources, persistence (and consequences!) of arbitrage opportunities
- mitigating these consequences ...
- when/where the pricing works
- effects on/externalities for the shared resource (gas fees)
Bottom line
- (Minor) too many "moving parts" & notation?
- Closer related to microstructure than the paper suggests ...
- liquidity (and lack of thereof) is HUGE in microstructure ...
- → possible insights into DEX organization?
- Empirics:
- Cool stylized facts! Support model's predictions, though not causal (?)
- Trading volume: theory predictions are on "investor" volume (→fraction of "noise/non-strategic trading") but doesn't your empirical volume measure include arbitrage trades?
Three sets of comments
- Liquidity Providers (LP)
- no pricing decisions
This paper's model vs "traditional" microstructure
- Liquidity Providers (LP)
- Investors
- private values
- incur "slippage"/price impact cost
- + pay a preset % trading fee
- Noise traders (?)
- the bid-ask-spread cost
- LP earns the bid-ask spread
- noise trader loss = LP gain
- LP unable to gain the "spread" (can't liquidate the new (better than original) position for free)
- Arbitrageur: brings the contract reserves back, gains the "spread" but pays exactly that in gas fees → earns zero profit
- Miners (outside the model) earn the "spread"
- LPs earn (twice) the trading fees
- Liquidity Providers (LP)
- earn trading fees, no gains from temp. "noise" price/position dislocations
This paper's model vs "traditional" microstructure
- Exchange rate shock
- the swap (reserve-quantity-based) exchange rate is stale → any trade = loss to LP
- Shock to the fundamental → quotes become stale → loss to LP
- Adverse selection in limit order books b/c of public information:
- Foucault (JFM 1999);
- Budish, Cramton, Shim (QJE 2015)
- Adverse selection in limit order books b/c of public information:
- Equilibrium bid-ask spread increases in prob. of informed trading/prob. of shocks to fundamental
- Lose more on info → extract more from noise traders ....
- Severe adverse selection (large or very likely exchange rate shock)
- → fixed trading fees are insufficient to compensate LPs
- → do not supply liquidity → liq. freeze
- Liquidity Providers (LP)
- earn bid-ask spread
- Is there ever a benefit for the LP to match the gas fee submitted by the arbitrageur in race to withdraw? (No for this model (?).)
- Indirectly: alternate shapes of the pricing function [this paper & Park (2021)]
- Affects noise trader incentives vs. arbitrageur incentives
- → possible to reduce losses to LPs
Mitigating liquidity freezes?
- Involve an Oracle in pricing?
- e.g., adjust contract reserves (→ swap exchange rate) based on external data?
- (not that simple ....)
- Any way for LPs to benefit directly from favourable contract reserve changes (i.e., better than "fair" swap exchange rates) -- akin earning the bid-ask spread?
- Variable trading fees?
- Role of the reserves size in the liquidity pools?
@katyamalinova
malinovk@mcmaster.ca
slides.com/kmalinova
https://sites.google.com/site/katyamalinova/
The Adoption of Blockchain-based Decentralized Exchanges Ruizhe Jia & Agostino Capponi Discussion by Katya Malinova DeGroote School of Business McMaster University NFA 2021 (virtual)