David Zou, The Chief Economist from Bitmain Shares an Inspiring Talk on Robustness of Incentive Mechanisms with Volatile Tokens at Mars Blockchain Summit NYC

David Zou, The Chief Economist from Bitmain Shares an Inspiring Talk on Robustness of Incentive Mechanisms with Volatile Tokens at Mars Blockchain Summit NYC

Oct 20th 2018| Investment By:Mars Finance
As more and more stable tokens come onto the market, a competition among stable tokens has started.

Mars Blockchain Summit NYC 2018 was successfully held in New York on October 18, EST. Joint by well-known financial institutions and industry leaders from both China and Wall Street, guests engaged in deep dialogues to explore what’s next in blockchain.

Six panel topics were tackled at the summit, including the Trend of Global Regulations, the Future and Implementation of STO, Stablecoin, Secondary Market and Trading Platforms, Public Blockchain and Application, and Investment Strategy against Market Cycle. 

David Zou, Chief Economist from Bitmain shares an inspiring talk on Robustness of Incentive Mechanisms with Volatile Tokens. To recap the main points of Zous speech:

1. Fluctuations in token price may make some incentive mechanims hard to enforce and introduce economic instability into some POS algorithms.

2. Tokens with equity features in blockchain platforms may cause unmanageable procyclicality problems. Bancor algorithm offers an approach to anchor token price to some stable reserve.

3. As more stable tokens compete with each other, stable tokens with higher compliance and transparency standards will gain market share from less qualified ones. However, we should pay attention to portfolio rebalance and liquidit squeeze effects in the transition period.

Below is the full text of the speech, recorded and edited by Mars Finance, unconfirmed by the speaker:

I will divide my speech into 3 parts. First, I will discuss why we need stable tokens. Then, I will briefly compare stable tokens with other tokens designed to be unstable. Lastly, I will discuss Bancor algorithm and reserve-backed tokens.

Let’s start with a tale of Crypto Land. Robinson Crusoe and Friday live happily in Crypto Land. They use crypto coins in their daily life. One day, Robinson traveled to the mainland and set up an internet link. Now they can trade crypto coins for USD. At the beginning of one year, Robinson borrowed one crypto coin from Friday for one year. At that time, one crypto coin was worth only 8 USD. However, at the year end, one crypto coin was worth more than 700 USD. Now Robinson had a strong incentive to default on his loan. This was unimaginable before the introduction of the crypto market.

In fact, this is not just fiction. Many blockchain projects face similar challenges. That is, how to make incentive mechanisms work when tokens are volatile. For example, in many POS algorithms, validators or miners need to stake their tokens. They are entitled to pro-rata share of new token issuance. But they are subject to withdrawal delay. If they opt out of the validator program, their tokens are locked up for a certain period of time. For example, under Casper FFG, validators may earn an annual interest of 5% but faces a 4-month withdrawal delay. The question is, will the 5% interest big enough to compensate for the withdrawal delay?

To study this problem, we need to borrow some tools from financial engineers (figure 1). Investors give up the liquidity of their tokens in the lockup period. Otherwise, they can sell tokens whenever they want. Assume investors have perfect market timing skills and can sell tokens at the highest price, S_max. The cost of token lockup is simply the difference between S_max and end of period price, S_T. It turns out that token lockup is equivalent to a lookback put option with floating strike.


Figure 1: Token Lockup Problem

Fortunately, this option has a simple valuation formula. Token lockup cost is (in term of initial token price): 

Token lock cost is an increasing function of token volatility (σ) and the length of lockup period (T). If you lock up a volatile token for a long time, you will incur a high cost. For example (figure 2), with ETH volatility of 108.5%, a 4-month withdrawal delay means a lockup cost of 60%, much higher than the 5% interest. On the other hand, given the 5% interest, ordinary ETH investors only want to lock up ETH for 1 day. Then, who are willing to become validators?


Figure 2: Token Lockup Cost

Of course, I am not implying Casper FFG is infeasible in the real world. To better study this problem, we can use base currency in crypto world to summarize investors’ risk appetite (table 1). Base currency is something that investors consider to be risk free. For example, ordinary investors use USD as base currency and treat tokens as risky investment. Meanwhile, hodlers use tokens as base currency. Intermediate investors lie between ordinary investors and hodlers. The token component in their base currencies has a weight between 0 and 1. 

Table 1: Base Currencies of Different Investors


Now the same token will appear different to different investors (figure 3). As the token component in their base currencies becomes larger, they view tokens to be less volatile. Therefore, they are more likely to accept lockup arrangements. Under Casper FFG, only investors with a token component larger than 90% are willing to lock up ETH for 4 months in exchange for the 5% interest (figure 4).


Figure 3: Token Volatility and Base Currency


Figure 4: Potential Validators under Casper FFG

In summary, token volatility limits the pool of potential validators. Only investors who are risk-taking enough or crypto enough will become validators. Besides, fluctuations in token price may introduce economic instability into POS algorithms. That’s why we need stable tokens. They create a stable environment to enforce incentive mechanisms. It is like putting a ship into a bottle so that it won’t be teared apart by wind and wave.

But not every token needs to be stable. First, tokens with dual functions in blockchain platforms. They are both payment instruments within the platforms and funding tools to kickstart the platforms. Second, equity tokens with voting and dividend rights (especially via token repurchase and burn). Many blockchain projects use those tokens to produce a positive feedback loop among user adoption, network effect, and token price (figure 5). 


Figure 5: Positive and Negative Procyclicality Effects

However, this strategy doesn’t guarantee success. On sunny days, everything moves to the right direction. But on rainy days, everything can go wrong. As a matter of fact, many projects end up in a procyclicality trap (figure 5). Users walk away, network effect disappears, token price falls, and more users want to leave. So on, and so forth. This summer, we witnessed the rise and fall of FCoin. It shows the brutal force of procyclicality. There is a time for procyclicality to build up. There is a time for procyclicality to break down. That’s why sometimes we want to anchor token price to something more stable.

I don’t have enough time to go over different stable token designs and discuss their economic rationales. I want to discuss how to use Bancor algorithm to create reserve-backed tokens and how token price is anchored to reserve.

Suppose you issue a certain number of tokens and raise some fund. You put aside all or a part of the fund as reserve. You can either keep the tokens and reserve in the balance sheet of a trusted entity or use a smart contract to manage them (figure 6). 

Figure 6: Bancor Algorithm

The trusted entity and the smart contract maintain the two-way exchange between tokens and reserve according to those formulas: 

Particularly, Bancor algorithm specifies the relationship between token price and token supply (figure 7):


Figure 7: Token Supply and Token Price

When tokens are traded in the crypto market, market price usually deviates from the level set by Bancor algorithm. However, arbitrage will drive market price and Bancor algorithm to converge.

Reserve level determines the strength of anchoring effect. With no reserve, Bancor algorithm has no anchoring effect. Tokens and reserve asset will trade independently. This is the case for most ERC20 tokens. With fractional reserve, the higher the reserve level, the larger the anchoring effect. With full reserve, token price will be pegged to the reserve. This is the case for mainstream tokens.

I don’t think stable tokens without reserve can succeed. In times of emergence, central banks need reserve to defend their currencies. Whenever central banks run out of reserve, their currencies will be forced to depreciate. This happens in many emerging countries, for example Thailand in 1998 (figure 8). This also holds true for the crypto market.


Figure 8: Thailand’s Official Reserves and Exchange Rate around 1998

As more and more stable tokens come onto the market, a competition among stable tokens has started. In the long run, stable tokens with higher compliance and transparency standards will gain market share from less qualified ones. However, the transition process can be quite tricky. We should be aware of two possible effects. The first is portfolio rebalance effect: Investors sell less qualified stable tokens and buy major cryptocurrencies as a safe harbor. This happened on this Monday. The second is liquidity squeeze effect: Investors withdraw USD from issuers of less qualified stable tokens. Particularly, if in the past, those issuers printed stable tokens out of thin air to buy cryptocurrencies, they may be forced to liquidate their investment position. This fire sale could have systematic impacts on the crypto market. Whether this will happen or not, I can’t say for sure. But if you notice black clouds gathering in the sky, you should probably prepare for the storm to come.