These are volatile times, and we must prepare for the bear market. But how bear could it be? Even if we know something is coming after us, we cannot adeptly prepare ourselves if we know what is coming after us.
TaschaLabs, in her brilliant article, pointed out that the dollar index (DXY) was the strongest indicator of the bitcoin price (BTC).
As we can observe, the “inverse of DXY” is strongly correlated to the price of BTC. To be more precise, the YoY change in DXY is negatively correlated to the YoY change in BTC price. So, if we can somehow predict the dollar index, we would know the vague idea on the BTC price path.
Now, let’s bring us another interesting plot.
The graph above shows the impact of QT on DXY, and the impact is fairly straightforward: QT strengthens DXY.
We know two relationships.
- High DXY = Low BTC
- QT = High DXY
Then if we know the magnitude of the impact QT has on DXY, maybe we can derive a theoretical price path for BTC.
The graph might be a bit misleading in terms of magnitude since the numbers are normalized, but the trigger of QT in 2014 caused DXY to increase ~21% over 2 years.
The DXY Model
It is a bit embarrassing to call this a ‘model,’ but I will cope with it.
By looking at the data, we can establish that
- QT would cause DXY to increase by roughly 21% over 2 years, so g = 0.21/730
- DXY volatility last year was 0.014, so s = 0.014
- We know that the Fed would trigger QT this June
Now we will run 100 Monte Carlo simulations to see the possible paths of DXY at g = 0.21/730, s = 0.014.
We will take the median value for each time period (index) and use it as the base DXY path.
So the blue line will be our DXY value forecast.
If we run a simple regression on YoY change in BTC to YoY change in DXY, we have -6.5, which means 1% annual increase in DXY leads to 6.5% annual decrease in BTC.
We have simulated the daily data for DXY, so we will assume BTC price would change by the percentage change in DXY * -6.5%/365.
We will make another assumption that the long term trend on BTC is bullish, so BTC would “grow by 20% annually” if ceteris paribus.
Now we can calculate the price path for BTC.
The smooth line from index 2700-ish is our simulated BTC price over 2 years.
Nooooow we will bring our LUNA model from our previous post.
With the ‘Alabi’ case, we will adjust the numerator (LUNA market cap) by decrease in BTC price, and the denominator (LUNA circulating supply) by increase in the stablecoin dominance during the bear market.
For the numerator, we will tone it down by changes in BTC * 0.6. LUNA lately has been decoupled from the prices of BTC, but still it is correlated to BTC yet.
The method is simple. We just bring our Alabi model as our base case, and scale the numbers down, and we have our bear market LUNA market cap.
There are 1M daily LUNA burns right now, but we will safely assume its burn rate goes down to 500K daily throughout this year.
Now we have both our numerator and denominator, we can draw our LUNA price paths.
So if LUNA supply stays fixed, LUNA wanes with BTC. If TFL can hold the growing demand for UST, maybe LUNA still has a chance to outperform the bear market.
The numbers in this article are really vague and lacks theoretical backgrounds, so no one should take this article as an investment advice.
But still, we know that QT = DXY up = BTC down, and the impact of QT on DXY lasts for ~2 years, we need to prepare ourselves for the prolonged bear market.
LUNA, with its excellent stablecoin design, might have a chance to survive the bear market IFF the demand for UST continually grows; if not, it will wane just like any other assets.
Thank you for reading this article, and feel free to respond here if you want to suggest any specific topics/ecosystem/tokens for future writings.
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