(365zg.vip):BM對穩定幣的新想法,是社區受益還是曇花一現(附BM原文)

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  本文來源:區塊鏈新金融 (ID:shhl_qukuailian),作者:Daniel Larimer

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  摘要:BM提出的新型穩定幣,則是要根據原BitUSD的必發88算法,再與“嚴重超額抵押的空頭倉位”相結合,加上EOS的激勵措施,繼而提出一種能夠保證流動性的運行在EOS鏈上的穩定幣。

  昨日,BM發表了有關于新的穩定幣設計的想法。

  隨著Bitfinex與Tether事件的爆雷,USDT再度被推到了風口浪尖。與此同時,暫緩增發腳步的USDT為其它穩定幣打開了市場缺口,

  在此過程中,伴隨著DeFi(去中心化金融)這一概念的愈發火熱,人們對包括MakerDao、Gemini Dollar、USD (365zg.vip) Coin等在內的穩定幣的算法、保證流通性等手段等做出了探索。而BM提出的新型穩定幣,則是要根據原BitUSD的算法,再與“嚴重超額抵押的空頭倉位”相結合,加上EOS的激勵措施,繼而提出一種能夠保證流動性的運行在EOS鏈上的穩定幣。

  特點:嚴重超額抵押的空頭倉位結合Bancor算法,為多頭和空頭頭寸提供流動性,從而提供高流動性,并且讓抵押物在時間推移、價值損失的情況下,還能夠保證平穩運行,從而保護市商。

  背景

  2013年Bitshares(注:比特股,BM之前的區塊鏈創業項目)提出了比特美元(BitUSD)的概念,其旨在提供一個去中心化交易所的解決方案(人人可發行、人人持債倉)。成為錨定美元的穩定幣,從而提供法幣與比特幣的置換與穩定。

  BitUSD通過在想要利用BitShares代幣(BTS)的人和想要價格穩定的人之間創建訂單來運營。為了向購買BitUSD的人提供流動性,BitUSD持有人被允許在多日延遲后強制平倉至少抵押的空頭頭寸。這創造了一個有效的追加保證金,并確保BitUSD的購買者,他們的代幣總是價值約1美元的BTS。為防止違約,如果價格反饋低于最低保證金要求,最低抵押空頭頭寸也可能被強制平倉。

  注:如同當今銀行體系利用抵押品以借貸的方式鑄造美元一樣,比特股X也是可以利用抵押品鑄造出BitUSD(比特美元)。而不同于傳統銀行利用房產作為抵押品借出美元,比特股X會利用BTSX(股份)作為抵押品借出BitUSD。如果抵押品的價值相較于BitUSD的價值有所下跌,比特股將會自動售出部分抵押的BTSX以彌補虧空(強制平倉),并返還剩余的BTSX給抵押人。

  BitUSD的主要問題是缺乏流動性:可用的BitUSD供應有限、市場價差傳播、做市商必須經營交易機器人,以便在內部訂單簿上移動訂單。

  (注:BM認為,這種情況下,BitUSD缺乏流動性,但其算法仍能夠提供高流動性。)

  顯而易見,BitUSD的機制,雖然讓BitUSD的持有者獲得了流動性,但是,空頭頭寸并沒有得到流動性保證。最后,當無法涵蓋最少抵押的頭寸時,整個市場就會發生事件。當發生這種問題時,掛鉤會永久損壞,并且在BitUSD和BTS之間建立固定的匯率。

  缺乏激勵創造了BitUSD,低流動性的風險(沒有不合理的下滑就無法彌補),意想不到的追加保證金通知,以及安全市場制造機器人的困難,意味著BitUSD的供應量很小,息差很高。

  還有許多其他項目利用了過度擔保頭寸和保證金要求的變化,他們都面臨著與BitUSD類似的問題。

  (注:BitUSD的問題:1、缺乏流動性 2、只為一方提供強制結算)

  值得一提的是,雖然傳統的衍生品市場實行“期貨交易”,允許人們提供抵押品,并在未來某個固定的時間以固定的價格進行結算,這些期貨合約是可替代的,可以在一定的時間內交易,但由于到期和需要結算/轉倉,所以這種方式不適合創建掛鉤令牌。

  Bancor算法提供了兩種資產間的自由流動,同時保護其不會流失給老練的交易員。無論針對Bancor算法執行的訂單數量和種類如何,當資產對的價格回到初始起點時,該算法總是會產生利潤。

(365zg.vip)

  一個典型的Bancor算法有兩個“連接器”,代表市場價值相等的余額。該算法成功的在EOS RAM等市場提供了自動流動性。(注:BM認為,雖然BitUSD具備不足,但其算法具備成功案例)

  Pegging算法

  這種穩定幣的算法基于這樣一個概念:穩定幣是空頭對多頭提供的服務。這項服務要求賣空者為穩定幣提供流動性,如果對穩定幣有需求,那么市場相對穩定,做市商的服務提供商也會有利可圖。

  其他釘住匯率的算法讓賣空者相互競爭,而不是促進它們的合作,目的是將釘住匯率的服務推向市場。在這些其他算法中,空頭競相回補和抵押其頭寸,并擔心空頭擠壓。

  我們的算法的前提是那些愿意在抵押資產(如EOS)中略微杠桿化的人可以通過促進EOS和可互換掛鉤資產(例如美元)之間的市場交易來賺錢,其價值的設計,是在允許的偏差范圍內跟蹤價格。

  我們的目的不是用戶創建獨立的空頭頭寸,而是創建一個全局空頭頭寸。賣空頭寸是一個中性且可逆的過程(減去交易費用),前提是在此期間不發生其他交易。交易費用不是杠桿的主要動機,而是買入頭寸股份的動機。

  為此,BM認為,這意味著400%的超額擔保或更高的目標是可行的。允許賣空者在擔保資產中只是用少量杠桿,這樣他們就有機會賺取交易費用。

  做市商最初是通過在合約中存入抵押品創建的,該合約將在做市商(MMS)中創建代幣,并將代幣交給最初的存款人。

  舉個例子,假設抵押品是EOS,而掛鉤的是美元,其設計目的就是跟蹤EOS美元的24小時價格中位數。

  目標準備金率是確定的,例如400%,其中,75%的EOS存款將作為多余的抵押品,其余的25%將放在Bancor連接器中。此時,自動做市商的合約將創建許多美元代幣,使EOS和美元連接器余額的市場價值與Price feed(價格供應)的初始價值相同。

  (注:BM想通過為降低大部分市場流動性風險的空頭方提供資產創建、提供流動性等方式進行激勵,同時利用Price feed來引導市場)

  在初始條件下,做市商擁有連接器中的100%,因此沒有凈負債(必須回購美元),市商的賬面價值等于連接器中持有的EOS價值加上多余的抵押品EOS,再減去從連接器中出售的任何流通美元。在這個初始設置之后,任何人都可以從市商那里購買美元,這就為市商建立了未來的回購債務。

  這意味著,當用戶從市商那里購買美元時,報價將會上升。如果每個購買美元的人都將其賣出,那么連接器最終處于初始狀態,對交易收取的任何費用,都將導致EOS連接器余額中EOS的凈增加。

  考慮到這一設置和400%的準備金目標,我們可以證明實際準備金與賣出的美元之比將遠遠超出400%。為了降到400%(沒有市場價格變化),整個美元連接器將不得不買斷,這將推動美元價格無限。實際上,做市商會自動提高買入美元多頭的成本,因為可用的美元多頭供應減少。

  做市商提供的現貨價格等于連接器余額中EOS與美元的比值。當現貨價格與Price feed偏離超過可接受的幅度(如+/- 2%)或超過允許的時間(24小時),做市商將更具市場情況采取下列行動:

  該算法的目標是,始終將狀態移動到更接近初始狀態,即比Bancor多3倍的額外抵押品和美元連接器中100%的美元。顯然,在有流通美元的情況下,連接器不可能達到100%的美元,但當EOS相比于美元的價值明顯上升時,流通供應量的百分比會下降。

  抵押物轉移或美元發行的速度應將目標價格調整到目標時間(如1小時)內可接受的范圍。理論上,交易者應該像美元價值1美元一樣買賣美元,因為他們有信心在不久的將來總能以1美元的價格賣出美元。

  這意味著,當EOS和美元的相對價格穩定時,做市商不必依賴于Price feed來糾正實時價格,而Bancor算法針對操縱者的安全性保護連接器余額不被交易員丟失。

  在配置參數時,關鍵是要盡量減少主動做市商的頻率,當需要干預時,干預應是緩慢而漸進的。市場參與者不應該搶在市商之前獲利。

  該算法的結果是一個固定資產,遵循24小時中值,而不是瞬時值。只有當做市商的24小時中位數顯著大于真實市場的24小時中位數時,它才會積極修正穩定匯率。

  當市場跳躍時,我們希望交易員與Bancor算法交互,以投機的方式領先于24小時的中位數。這種提前運行可以防止強制調整Bancor的連接器,除非在長時間的情況下,中位數價格走勢越慢,做市商的風險越小,但釘住美元與實際美元之間的偏差越大。使Bancor算法具有偏離較大百分比(例如5%)的能力也最小化了由于饋送導致的定價的人為交互。

  買賣MMS令牌

  (注:MMS資產的設計,是為了給穩定幣做出貢獻的人提供收益用的)

  任何人在任何時候都可以貢獻新的EOS來購買MMS和USD組合。這是通過在添加新的EOS后,保持MMS、EOS、Bancor連接器中的USD和流通中的USD的比例來實現的。個人收到的MMS,加上百分之一的新創造的美元比例的美元流通。美元和EOS也被添加到連接器和超額準備金中。然后用戶可以出售EOS的美元,并重復這個過程,或者只是持有多余的美元或EOS。

  銷售MMS令牌需要提供的美元數量等于usd_circulation * MMS_Sell / MMS_Supply。這與他們在購買帶有EOS的MMS時收到的美元數量相同(減去任何交易費用)。

  這個過程可以看作是分割和連接相同的空頭頭寸。一旦你控制了空頭頭寸的所有未償債務,你就可以解除它,收回所有抵押品。這個過程的關鍵是保持不變,即有人用/作為抵押品買賣做市商不會改變MMS、EOS、USD和USD的流通比率。如果你想出售莊家1%的股份,你還必須購買并覆蓋1%的流通美元。幸運的是,你可以從做市商那里購買流通的美元,所以總是有流動性的。

  市場交易費用

  市場交易費用會不斷對做市商進行資本重組,而無需任何集體杠桿作用的投入。無論市場條件如何,那些被杠桿化的人都不可能減少他們的抵押品或者不能逐漸加強抵押品。由于初始抵押品比例較高,例如4倍,抵押品的下跌速度必須比交易費用的累計速度快75%。

  黑天鵝

  黑天鵝是指做市商無法維持美元在價值附近的價格。當多余的抵押品消失時,就會發生這種情況。當這種情況發生時,做市商將繼續運作,但美元的價格將獨立于哺育浮動。精明的觀察者可能會考慮從連接器的一側刪除USD以維持價格,但這是不可取的,因為這會在連接器中的剩余EOS上創建一個運行。一旦多余的抵押品消失,通過讓價格浮動,那些想要提前退出的人將為流動性和正在進行的交易支付溢價,從而對其余各方進行資本重組。

  即使在黑天鵝交易期間,以交易費用為代表的收入流也會激勵交易各方提供抵押品,并為做市商提供資金。如果抵押品資產(例如EOS)沒有預期未來價值的恢復,那么掛鉤資產(例如美元)的持有者將以市場決定的價格在連接器中獲得剩余EOS的公平份額。

  與其他一些制度不同,黑天鵝事件并不是特例,市場有一個無縫的、自然的恢復方法來恢復掛鉤。

  如果沒有多余的抵押品,做市商可以配置為防止出售MMS代幣,同時允許以10%的折扣購買帶有EOS的MMS。這將對做市商進行資本重組,使新的MMS持有者受益于先前的持有者。一旦多余的抵押品被恢復,MMS就可以像上面描述的那樣再次出售。抵押品過度損耗的確切點和折價幅度是可以調整的變量,以使完全損耗的風險最小化,并使激勵機制最大化,從而在不過度懲罰先前MMS持有者的情況下,迅速對市場進行資本重組。

  價格

  我的建議是,掛鉤的目標是24小時的中間價格,而不是瞬時價格。這將減少與釘住匯率制背離的頻率和幅度,同時又不會削弱釘住美元作為美元替代品的價值。實際上,它將部分日內波動風險轉移給美元持有者(偏離平均值),同時仍在對沖美元持有者對長期波動的風險。

  結論

  與BitUSD等系統相比,我們的掛鉤方法通過向提供抵押品的空頭提供交易費用,同時有效消除空頭的大部分流動性風險,從而激勵資產創造和流動性。此外,該算法為市場雙方提供相同的流動性,其中BitUSD僅向市場的一方提供強制結算。交易手續費持續不斷地重新在市場中抵押,能夠隨著價格的不斷變化來恢復,這只需要交易手續費收入比資產貶值速度大就可以做到。其認為,這一措施將最大化使用者的實用性,同時最小風險化。

  -----------

  市場評價

  對于BM提出的新的高流動性穩定幣算法,有人提出了質疑的聲音。

  BTS理事會成員、YOYOW聯合創始人,GDEX去中心化交易所創始人劉嘉陵認為,BM此舉破壞了Bancor協議的最基本條件,這種基于EOS的穩定幣難以持續運行。

  其表示,Bancor協議的一個要點是,主體交易價格只由設定的抵押率和新資產供應量決定,不受其它因素影響。

  而在BM設想的邏輯里,不但沒有一個固定的的抵押率(Bancor意義的固定抵押率是指1穩定幣=CW*1EOS,CW固定),而且又引入了新的影響價格的因素,那就是Bancor連接器中的EOS資產還隨時可以增減,增減只為影響SPOT價格。

  其結果是,資產發行主體根本無法保證通過交易保持收支平衡。由于市場上攻擊者常有,虧損是高概率事件。還有,單獨一個全局債倉的設置也缺乏足夠緩沖,使得黑天鵝事件發生的概率變高。

  而在今日,BM又現身電報群與社區成員討論了穩定幣的相關問題。

  BM表示:1、B1沒有開發穩定幣,我只是說出了我想法,想讓社區受益; 2、B1將在6月份揭示未來的發展方向,MAS還需要某種穩定的記賬單位; 3、穩定幣不能空投,用戶必須通過購買的方式獲得,這是因為穩定幣是一種債務性資產,他需要實際資產作為支撐; 4、提問:USD沒有任何實際資產做支撐,他難道不是穩定幣嗎?BM:USD是相對穩定,但與黃金相比,USD是不穩定的。

  附:High Liquidity Price Pegged Token Algorithm

  Today I introduce a new token pegging algorithm that provides high liquidity and narrow spreads, while being robust against default in the event collateral loses value over time. The basis of our algorithm is a heavily over-collateralized short position combined with the Bancor algorithm to provide liquidity to both long and short positions. A price feed is utilized to guide the market, but its influence is limited to situations where there is a prolonged deviation so as to protect the market maker from abuse.

  Background

  In 2013 BitShares introduced the concept of BitUSD, a “smart coin”, backed by BTS tokens, which was designed to track the value of the dollar. BitUSD operated by creating an order book between those who wanted leverage on the BitShares token (BTS) and those who wanted price stability. To provide liquidity for those who purchased BitUSD, BitUSD holders were allowed to force-settle the least-collateralized short position at the price feed after a multi-day delay. This created an effective margin-call and assured buyers of BitUSD that their token was always worth about a dollar worth of BTS. To prevent default, the least collateralized short position could also be force closed if the price feed fell below the minimum margin requirements.

  The primary problem with the BitUSD approach is the lack of liquidity, the limited supply of BitUSD available, and the market spread. Market makers were required to operate trading bots that move orders on the internal order book. While the BitUSD holders were provided liquidity, the short positions were not guaranteed liquidity. Lastly, the entire market is subject to a black-swan event when the least collateralized position is unable to be covered. When this happens the peg is permanently broken and a fixed exchange rate is established between BitUSD and BTS.

  The lack of incentive create BitUSD, the risks of low liquidity (unable to cover without unreasonable slippage), unexpected margin calls, and difficulty in running safe market making bots meant the supply of BitUSD was small and the spreads were high.

  There have been many other projects that utilize variations of over-collateralized positions and margin requirements. All of them suffer similar problems to BitUSD.

  Traditional derivative markets implement “futures trading” which allows people to post collateral and settle at a price feed at a fixed point in the future. These futures contracts are fungible and can be traded for a certain time, but due to the expiration and required settlement / rollover they are not suitable for creating a pegged token.

  The Bancor algorithm provides automated liquidity between two assets while protecting its reserves from being lost to sophisticated traders. Regardless of the number and kind of orders executed against the Bancor algorithm, the algorithm always generates a profit when the price of the asset pair returns to its original starting point. A typical Bancor market maker is known as a relay which has two “connectors” which represent balances of equal market value. This algorithm has been successful at providing automated liquidity in markets such as the EOS RAM market.

  The Pegging Algorithm

  This pegging algorithm is based upon the concept that a pegged token is a service provided by the shorts to the longs. This service requires the shorts to provide liquidity for the pegged token. If there is demand for the pegged token then trading fees earned by the market maker should be profitable for the service provider even if the market is relatively flat.

  Other pegging algorithms pit shorts against each other rather than facilitate their cooperation for the purpose of bringing a pegged currency service to the market. In these other algorithms shorts compete to cover and collateralize their positions and are worried about short squeezes.

  The premise of our algorithm is that those willing to be slightly leverage-long in a collateral asset (such as EOS), can make money by facilitating market making activities between EOS and a fungible pegged asset, (e.g. USD), whose value is designed to track a price feed within an allowed deviation range.

  Instead of users creating independent short positions, our algorithm creates one global short position and allows users to buy and sell stake in this global position. Buying and selling stake in the short position is a neutral and reversible process (minus trading fees) provided no other trades occur in between. Instead of leverage being the primary motive, trading fees are the motive for buying stake in the position. This means that a target of 400% over collateralization or higher is viable allowing shorts to be only slightly-leveraged in the collateral asset so that they have the opportunity to earn trading fees.

  The market maker is initially created by depositing collateral into the contract. The contract will create tokens in the market maker (MMS) and give them to the initial depositor. For the sake of example, we will assume the collateral is EOS and that the pegged token is USD and is designed to track the 24hr median EOS dollar price.

  A target reserve ratio is decided upon, for example 400%. Based upon a 4x reserve ratio, 75% of the EOS deposited would be set aside as excess collateral, and 25% would be placed into a Bancor Relay connector with weight of 50%. At this time the automated market maker contract will create a number of USD tokens to fund a second Bancor Relay connector such that the market value of EOS and USD connector balances are the same at the initial value of the price feed.

  In the initial condition the market maker owns 100% of the USD in its connector and therefore has no net liability (USD it must buy back). The book value of the MMS is equal to the value of the EOS held in the connector plus the excess collateral EOS and minus any circulating USD sold from the connector. After this initial setup anyone can buy USD from the market maker which establishes a future buy-back liability on the MMS holders. The mathematical properties of the Bancor Relay algorithm mean that as users buy USD from the market maker the offer-price will increase. If everyone who bought USD then sold it back then the connectors would end up in their initial condition. Any fees charged on the trades would result in a net increase in EOS held in the connector balance.

  Given this setup and a 400% reserve target, we can demonstrate that the actual reserves vs sold USD will be far in excess of 400%. In order to get down to 400% (without market price changes) the entire USD connector would have to be bought out which would push the USD price to infinity. In effect, the market makers automatically raise the cost of buying a long USD position as the supply of available long USD positions decreases.

  The spot-price offered by the market maker is equal to the ratio of EOS to USD held in the connector balances. When the spot price deviates from the feed price by more than an acceptable margin (e.g. +/- 2%) for more than an allowed period of time (e.g. 24 hours), then the market maker will do one of the following actions depending upon market conditions:

  SPOT=price of USD according to Bancor

  FEED_USD=price of a federal reserve note in EOS

  if( SPOT > FEED_USD )

  if( Excess Collateral < 3x EOS Connector )

  then slowly move EOS Connector to Excess (lowering SPOT)

  else

  then slowly add new USD to Connector (lowering SPOT)

  else if( SPOT < FEED_USD )

  if( excess collateral )

  then gradually buy USD using excess collateral

  and destroy it (raising SPOT)

  else do nothing

  The goal of this algorithm is to always move the state closer to the initial condition of having 3x more excess collateral than Bancor balance and 100% of USD in the USD connector. It is obviously impossible to achieve 100% USD in the connector while there is circulating USD, but the circulating supply can decrease in percentage terms when the value of EOS rises significantly relative to USD.

  The rate at which collateral is moved or USD issued should target a price correction to the within the acceptable range within a targeted period of time (e.g. 1 hour). In theory traders should buy and sell USD as if it were worth about 1 dollar because they have confidence that they will always be able to sell it for about 1 dollar in the near future. This means that when relative price for EOS and USD is stable that the market maker will not have to rely on the price feed to correct the real time price and the security of the Bancor algorithm against manipulators protects the connector balances from being lost to traders.

  When configuring parameters such as allowed deviation from feed, rate of correction, and delay until start of price correction it is critical to minimize the frequency of needing active intervention into the market maker and when intervention is needed, that intervention is slow and gradual. Market players shouldn’t be able to front-run the market maker’s moves for profit.

  The result of this algorithm is a pegged asset which follows an 24-hr median value instead of an instantaneous value. It only actively corrects the peg when 24-hr median of the market maker is significantly greater than the 24-hr median of the real market. When the real market jumps we want traders to interact with the Bancor algorithm to speculatively front-run the 24-hr median. This front-running prevents the forced adjustment of the Bancor connectors from occurring except in prolonged cases. The slower the median-price moves the less risk to the market maker, but the greater deviation between pegged USD and real USD. Giving the Bancor algorithm the ability to deviate by a larger percent (say 5%) also minimizes artificial interaction with pricing due to feeds. The less artificial interaction the less relevant the instantaneous accuracy of the price feeds become.

  Buying and Selling MMSTokens

  Anyone at any time may contribute new EOS to buy a combination of MMS and USD. This works by maintaining the ratio of MMS, EOS, USD in Bancor connector and USD in circulation after adding new EOS. The individual receives MMS plus a percent of the newly created USD proportional to the USD in circulation. USD and EOS are also added to connectors and excess reserves. Users can then sell the USD for EOS and repeat the process or simply hold excess USD or EOS.

  Selling MMS tokens requires providing a number of USD equal to USD_circulating * MMS_Sell / MMS_Supply. This is the same quantity of USD they would have received when buying MMS with EOS in the first place (minus any trading fees charged).

  This process can be viewed as splitting and joining identical short positions. Once you control all outstanding debt of your short position you can unwind it and get all the collateral back. The key to the process is maintaining the invariant that someone buying or selling the market maker with/for collateral does not change the ratio of MMS, EOS, USD, and USD circulating. If you want to sell 1% stake in the market maker (MMS) then you must also buy and cover 1% of circulating USD. Fortunately, you can buy circulating USD from the market maker itself so there is always liquidity.

  Market TradingFees

  As users buy and sell USD, EOS, and MMS a trading fee is charged. This fee represents a revenue stream that results in capital gains to those holding MMS. The higher the volatility the more trading occurs and the more fees are generated. These fees continuously recapitalize the market maker without requiring any input from those who are collectively leveraged. Regardless of market conditions it isn’t possible for those who are leveraged to reduce their collateral or fail to gradually top it up. With a high initial collateral ratio, eg 4x, the collateral would have to fall by 75% faster than trading fees accumulate.

  Black Swans

  A black swan is any event where the market maker is unable to maintain the value of USD near the price feed. This occurs when the excess collateral is gone. When this happens the market maker will continue to function but the price of USD will float independent of the feed. Astute observers will likely consider removing USD from one side of the connector to maintain the price, but this is unadvisable as it would create a run on the remaining EOS in the connector. By letting the price float once the excess collateral is gone, those who want to exit early pay a premium for the liquidity and ongoing trading will recapitalize the remaining parties.

  Even during a black swan, the revenue stream represented by trading fees incentivizes parties to provide collateral and fund the market maker. In the event the collateral asset, (e.g. EOS), has no expectation of future recovery of value, then holders of the pegged asset (e.g. USD) will get a fair share of remaining EOS in the connector at market determined prices.

  Unlike some other systems, a black swan event is not a special case and the market has a seamless and natural recovery method to restore the peg.

  In the event excess collateral is gone, the market maker could be configured to prevent selling of MMS tokens while simultaneously allowing the buying of MMS with EOS for a 10% discount. This recapitalizes the market maker giving benefit to the new MMS holders over the prior holders. Once excess collateral is restored MMS could be sold again as described above. The exact point of excess collateral depletion and the magnitude of the discount are variables that can be adjusted to minimize the risk of complete depletion and maximize the incentive rapidly recapitalize the market without overly punishing prior holders of MMS.

  Price Feeds

  There are many different ways to produce a trusted price feed; however, there are some recommendations for better results. A pegged token can track any price feed, including artificial feeds such as a 30-day moving average. Typical price feeds attempt to track instantaneous spot prices, but this expectation is unrealistic for safe market makers. The slower prices change the easier it is for a peg to be maintained because market participants have more time to adjust.

  It is my recommendation that pegs target the 24hr median price rather than the instantaneous price. This will reduce the frequency and magnitude of deviations from the peg without undermining the value of the pegged USD as a dollar alternative. In effect, it transfers some of the intraday volatility risk to the USD holder (deviation from the mean) while still hedging the USD holder against long-term volatility.

  Some experimentation in the market will be required to determine the proper balance between reactivity of price feed and profitability of the market maker due to volatility.

  Alternative Price Correction Measures

  When traders interact with the algorithm they are either pushing the bancor price further from the feed, or closer to the feed. It should be possible to have a dynamic fee on trades that grows higher the further the trade would push the bancor price from the feed. This allows MMS holders to increase profits and discourages manipulators from causing excessive deviation from the price feed.

  Conclusion

  Compared to systems like BitUSD, our pegging approach incentivizes asset creation and liquidity by providing trading fees to the shorts who post collateral while effectively eliminating the majority of liquidity risks of the shorts. Furthermore, this algorithm provides equal liquidity to both sides of the market where BitUSD only provided forced-settlement to one side of the market. Trading fees continuously re-collateralize the market and enable it to recover from losses due to changing prices. So long as income from trading fees is greater than the average fall in the value of the collateral asset the system can remain solvent and liquid. We believe this approach maximizes the utility to all participants while minimizing risks.

  Note: The economic arrangements described in this post may or may not be subject to regulations in your jurisdiction. Please seek professional legal opinions before engaging in economic arrangements described. Actual performance will depend upon many factors including implementation and selected configuration parameters. The ideas and opinions expressed above are my own and not those of my employer.

  編譯:共享財經Neo 責任編輯:Alian

  (本文系共享財經原創,轉載請注明出處及作者)

  【重磅】改白皮書,推幣安鏈:幣安的“操盤”之路

  【海外】說出來你可能不信,澳本聰準備起訴V神

  【行業】G20將近,日本交提案,欲做全球監管“老大”

  【評級】數鏈評級 | 牛頓Newton——電商與區塊鏈的聯姻:鏈商的未來


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