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Tarun Chitra Profile picture
Tarun Chitra
@tarunchitra
ヽ(⌐■_■)ノ♪♬ @gauntlet_xyz/@robotventures/@aerafinance/@thelatestindefi/@_choppingblock/@zeroknowledgefm // main: @guilleangeris
Feb 12 • 30 tweets • 9 min read
⚠️ New week, New Paper

Q: What was the best way to profit market volatility in DeFi over the last year?

A: @JupiterExchange's JLP pool

How does this pool work? Is this yield real? Is it safe?

@theo_diamandis, @RiskRinger, @perpsjesus, @ns_gauntlet, and I explain! Image @weremeow @sssionggg Paper: arxiv.org/abs/2502.06028
Oct 30, 2024 • 16 tweets • 3 min read
TL: Financial nihilism 🌕
Me: Financial optimism ☀️

On-chain DeFi intrinsically embeds collateral reqs — TradFi often misprices collateralized credit risk due to transparency

Pitch to (mid) economists: If crypto works… who fucking needs Basel III vs. a validated state root? The idea that on-chain finance needs undercollateralized lending has been an analogy searching for a solution

DeFi is most successful when you have assets where *verifiable* guarantees on collateral are paramount to position value

But: undercollateralized == not verifiable
Aug 6, 2024 • 20 tweets • 7 min read
Are Liquid Restaking Tokens (LRTs) essential to restaking security and even risk mitigation, vs. being a source of systemic risk?

Surprisingly, yes! New paper w/ @malleshpai shows that smart allocation to AVSs is crucial for security against cascading failures Image @ether_fi @RenzoProtocol @swellnetworkio @puffer_finance @KelpDAO @Eigenpiexyz_io First: What does this have to do with LRTs? They are the largest allocators + should be 'smart money' (due to economies of scale), with outsized impact on restaking security (see, e.g., )

We *quantify* when LRTs have a positive impact on securityethresear.ch/t/the-risks-of…
Jul 26, 2024 • 13 tweets • 4 min read
Repeated arguments over economic security + issuance are a reminder Proof of Stake's threat model is completely and utterly broken — BFT models assume the worst economic attack is a double spend

Doesn't make sense when:

stablecoin supply + non staked TVL in DeFi > ETH staked 2024: We should analyze principal-agent relationships vs. reducing everything to double spends

P-A model?
1. Users (principals)
2. Validators (agents)
3. Attackers (malicious agents)

Compute max profit for each principal-agent interaction (e.g. DS, oracle manipulation, etc.)
Mar 6, 2024 • 20 tweets • 7 min read
Three items are behind a wall and a solver is going to get one of them for you

Do you get a goat or a car, anon?

@malleshpai, @ks_kulk, @theo_diamandis and I show you that if solvers have to do more work to deliver the item to you, they're not going to show up to the auction https://arxiv.org/abs/2403.02525 There’s been a lot of talk about `intents’. What are they?

Simply put: they're markets for transaction execution where third parties called solvers compete to satisfy user orders (and any constraints those orders come with)

Question: What are the principal-agent problems here? Image
Jan 16, 2023 • 27 tweets • 12 min read
While @artgobblers isn't exactly my cup of tea, the novel NFT auction mechanism ford is cool from an auction theory perspective— but is it incentive compatible (IC) for both buyers and sellers?

tl;dr: It is *not* IC but can be modified to be IC!

people.eecs.berkeley.edu/~ksk/files/GDA… Quick recap: A gradual dutch auction (GDA) is a sequence of n auctions whose initial prices a_1 < ... < a_n are increasing but where the price of an auction decays as a_i * p(t) where p is non-increasing (e.g. p(t) = exp(-t) in the original paper)

paradigm.xyz/2022/04/gda
Nov 16, 2022 • 14 tweets • 3 min read
As someone way less cynical I’d counter with:

1. Winning mechanisms in DeFi come from where you don’t expect (e.g. Uniswap vs. bad copies of TradFi products badly)
2. MEV-aware designs take advantage of FHE/ZK improve efficiency/costs
3. Treasury management isn’t just a meme Simplified definition of capital efficiency:

max amt of capital used for risk bearing ops / amount of locked capital needed to support those operations

DeFi: denominator is larger than it is in TradFi due to permissionless/censorship resistance

What’s missing to close the gap?
Nov 11, 2022 • 6 tweets • 2 min read
The most amazing thing in the filings is how abysmal centralized lenders (incl. exchanges) were at 2 critical functions:
1. Dynamically setting loan-to-values
2. Liquidating bad loans

It’s embarrassing that @gauntletnetwork’s weekly DeFi governance proposals are faster than CeFi The fact that Voyager never liquidated FTT or SRM and that exchanges were sitting on so much GBTC and *still* didn’t decrease loan-to-values for their largest customers is insanely irresponsible risk management — and it is 100x easier to adjust this as a CeFi lender than in DeFi
Oct 12, 2022 • 10 tweets • 6 min read
One funny thing about DAO governance is that it makes it hard to manage assets held by a collective (why vote to sell the asset you're voting with?) — but for many collectives, asset management is more than pure yield optimization

Can we do better with a return to futarchy? The goal of Aera is to make it possible for decentralized, censorship resistant treasury management to make it possible for DAOs to hedge their portfolios according to their own KPIs, goals, and objectives while also seeding new protocols with liquidity in a positive sum manner Image
Aug 18, 2022 • 12 tweets • 7 min read
🚨New Paper Alert🚨

There have been a number of proposals for sharing MEV revenue between particular block producers and the rest of the (staked) network. But is it safe?

@ks_kulk and I show there can be *positive externalities* from MEV redistribution!

people.eecs.berkeley.edu/~ksk/files/MEV… What is MEV redistribution (also called MEV smoothing)?

💡: MEV profits split w/ x% go to all stakers and 100-x% to the validator themselves

Impossible to do in PoW, possible to do in PoS via Proposer-Builder Separation (see @ObadiaAlex/@taarushv's post) hackmd.io/@flashbots/mev…
Jul 18, 2022 • 23 tweets • 11 min read
⏰⏰⏰ New Paper Alert ⚠️⚠️⚠️
MEV is always bad for users… right?

Not always! @ks_kulk, @theo_diamandis, & I formalize the loss from sandwich attacks and show that sandwichers act as decentralized highway traffic controllers & *improve* network welfare

people.eecs.berkeley.edu/~ksk/files/MEV… Prior work on MEV has focused on:
1. Fair ordering
2. Formal verification to estimate MEV profit
3. Single sandwiches on Uniswap
4. Empirical data
But so far, no one has talked about the theoretical costs to the entire network from MEV

This paper solves this (at least for CFMMs)
Apr 14, 2022 • 12 tweets • 4 min read
🌬️💧How should we think about the implied value of a retroactive airdrop?

In this tiny note, I claim that:
a) They are exotic options
b) You can price them in some cases

But there are a lot of open problems that I'll leave for the end of the thread

gauntlet.network/reports/retroa… Image Think of retroactive airdrops in terms of capital optionality:

1. Provide capital (TVL, fees, volume) to a protocol
2. Increases a topline metric h that the snapshot uses
3. At an unknown time in the future you get an airdrop proportional to how much you increased the metric
Feb 21, 2022 • 20 tweets • 9 min read
⚠️ Paper Alert ⚠️
a. (3,3) is a meme, not game theory (sorry @TheStalwart)
b. Ohm isn't a stablecoin

BUT

@OlympusDAO’s protocol is a vol reducing liquidity mgmt strategy

👀 new work from me, @ks_kulk, @GuilleAngeris, @alexhevans, and Victor Xu

people.eecs.berkeley.edu/~ksk/files/Ohm… It is clear that algo ‘stablecoins’ involve control theory — but prior to Ohm, most failed attempts (e.g. Basis, ESD) relied on PIDish controllers

Mechanisms had ‘control variables’ (e.g. bonds/locked assets) which couldn't reduce price variance/drift

Dec 20, 2021 • 13 tweets • 6 min read
🧙🏾‍♀️New Paper Alert 🧙🏾‍♀️

Our [Stephen Boyd, @GuilleAngeris, @alexhevans] Christmas Gift to you is a look at routing in CFMMs like Uniswap, Balancer, and Curve

People have talked about *heuristics* for routing, but what about an *optimal* form of routing? 🔮

web.stanford.edu/~boyd/papers/c… The growth of the CFMM markets in 2020 and 2021 was driven in large part by routing services such as @1inch, @SlingshotCrypto, and @matchaxyz, which allowed users to get better prices when going trading assets A to B by
a) allocating A/B trade to diff. CFMMs
b) paths: A -> C -> B
Nov 1, 2021 • 14 tweets • 6 min read
⚠️ October Paper Alert 🚨

Conventional wisdom: Order Books should beat AMMs on-chain

Is this *always* true? 🤔

In light of the @solana liveness issue last month, we decided to try to analyze this theoretically and... the results might shock you! 🌩️

stanford.edu/~guillean/pape… This tweet led us down this 🐇🕳️ to answer the following question:

Is it better for DeFi, when liveness is lost, to have the majority of on-chain liquidity in an order book or an AMM for liquidations?

Two ways to study this: LP losses, # of transactions

Aug 26, 2021 • 19 tweets • 9 min read
⚠️ 📰💧🚨
Tired but true: Private Uniswap can’t exist
Wired: *Differentially Private* Uniswap does exist

This month’s paper (9th in a row!): can’t get the mechanism we want, but there exists one that we need

eprint.iacr.org/2021/1101

(Storytime! 📓)

On 3/1 @GuilleAngeris, @alexhevans & I wrote a paper showing that you can infer trade sizes in CFMMs like Uniswap strictly from price — convexity: blessing for arbitrage, curse for privacy (even w/ ZKPs)

But we left two opportunities for partial privacy (arxiv.org/abs/2103.01193)
Jul 22, 2021 • 13 tweets • 7 min read
⚠️ It’s 📰💧 time of the month again 🚨

Don’t want to read 100s of pages of CFMM literature? 

You’re in luck! We review the known theory of CFMMs (plus some new goodies!) for an upcoming *textbook* chapter on crypto + DeFi w/ two new authors:

(Paper: stanford.edu/~guillean/pape…) First: Who are they?

Stephen Boyd is a renowned @Stanford researcher known for his oft cited textbook, multitude of INFORMS/IEEE awards, and advising BlackRock on convex analysis for manage trillions of dollars

He’s also @guilleangeris’s PhD advisor!

en.wikipedia.org/wiki/Stephen_P…
May 2, 2021 • 14 tweets • 6 min read
Wow, y'all really aped into this paper, thanks for all the memes (@0xtuba) & thoughtful comments

I figured I'd go a little deeper into the connection between lending protocols and options selling but first, don't forget to follow @htkao, @GuilleAngeris, and @alexhevans ☺️ As @cuckqueeen points out, numerical methods for simulating barrier options are hard to execute correctly (one of the reasons we run all such simulations @gauntletnetwork against the contract execution state in EVM, otherwise you will mess up liq barrier)
Apr 30, 2021 • 13 tweets • 6 min read
One mechanism for improving capital efficiency in CFMM trading is borrowing against LP shares (e.g. @AaveAave, @AlphaFinanceLab, @MakerDAO, @SushiSwap)

How safe is it compared to normal lending?

New 📝💧 from @htkao, @GuilleAngeris, @alexhevans y moi

stanford.edu/~guillean/pape… We first show how to compare Loan-to-Value (LTV) / collateral factors between borrowing A with B as a collateral vs. borrowing A against an A/B LP pool share

Turns out, with dynamically adjusted LTVs you can make LP share lending *more* safe than lending of the underlying Image
Apr 9, 2021 • 12 tweets • 4 min read
This is a good thread about the MEV War of 2021™️

One thing I will say is that most of the fair methodologies have a downsides themselves:
1. Added latency
2. Lack of guarantees about economic price ordering
3. Extremely unproven in production (similar to ZKPs in 2012) Why added latency?

Theoretical (@vegaprotocol’s Wendy) and practical protocols (@valardragon) add a >= 1 block commit-reveal from validators OR added rounds of BFT-style message passing. Griefing vectors (DDoS-esque) are abundant + provable models have weak synchrony guarantees
Jan 18, 2021 • 11 tweets • 6 min read
⚠️ Paper Alert ⚠️

Remember the Twitter argument between @danrobinson and @SBF_Alameda?

Recall how it hinged on logarithmic vs. linear utility functions?

Using optimal control, we show utilities are a red herring

joint w/ @alexhevans @GuilleAngeris

stanford.edu/~guillean/pape… Flip the problem upside down: LP returns are a function of how close the weights w (@BalancerLabs portfolio weights) are to the 'optimum' weight w*

Arbitrageurs can be viewed as a stochastic control mechanism that moves w around w*

Can you control |w-w*| as a function of fees?