I made a model to simulate what would happen to BTC and ETH if something happened to Binance and BNB went to $0. According to my model, BTC would drop to $7900 and ETH would drop to $217.

\Disclaimer: This post is for entertainment purposes only. The factors that determine crypto prices and market behaviour are too complex for any model to forecast accurately.*

I know we've all probably wondered what would become of crypto if something was to happen to Binance; let's say, something similar to FTX. So I decided to create a simple model to simulate what would happen to the price of BTC and ETH in the unlikely event that BNB dropped to $0. I used R for my data analysis. My dataset consisted of daily prices of BTC, ETH, and BNB from 9th November 2017 to 30th August 2023 (you can find these datasets everywhere on the internet).

Once I had loaded my data and did all the cleaning, I began by calculating the cross-correlation among the 3 prices.

# Calculate the correlation between BNB, BTC, and ETH (CROSS CORRELATION)

cor_matrix <- cor(data[,c("BNB", "BTC", "ETH")])

cat("Correlation matrix:\n")

print(cor_matrix)

I got the following result (correlation matrix):

The closer the correlation number is to 1, the more closely related the prices are. So, as you can see from the correlation matrix above, the movement of BNB prices is closely related to that of ETH than BTC. BNB price movement if 94.7% similar to ETH and 88.28% similar to BTC. That's why my simulation model showed a higher drop in the price of ETH than BTC if BNB falls to $0.

Based on the high correlation in the prices, I assumed that a linear model would not be that far from the truth. So I fitted a linear regression to show the movement of BTC and ETH based on BNB:

# Fit a linear regression model to predict BTC and ETH prices based on BNB price

fit <- lm(cbind(BTC, ETH) ~ BNB, data=data)

cat("\nRegression coefficients:\n")

print(coef(fit))

The fitted linear regression showed that 1 unit change in BTC changes BNB price by 79 BNB, while a 1 unit change in ETH leads to a change in the price of BNB by 6 BNB. Normally, people use ARIMA models to predict future values based on past values. ARIMA stands for Autoregressive Integrated Moving Average and it is a method used to forecast time series data. However, I assumed that the impact of BNB going to $0 would be immediate, not slow and gradual. If anyone has the time to use ARIMA to replicate my model, I would love to see how it goes.

Next, I simulated a price drop of BNB to $0:

# Predict BTC and ETH prices when BNB is 0

pred <- predict(fit, newdata=data.frame(BNB=0))

cat("\nPredicted BTC and ETH prices when BNB is 0:\n")

print(pred)

My output showed that BTC would immediately go to $7934.371, while ETH would drop to $217.9605. Based on the prices of BTC and ETH on 30th August 2023, that represents a percentage drop of 69% (nice!) and 86%, respectively.

TLDR; If something similar to FTX happened to CZ/Binance and BNB dropped to $0, BTC would drop to $7934.37 and ETH would drop to $217.96. It's not facts. That's just according to my simulation, which is wrong in so many ways and the odds of this happening are very close to 0.