Mitchellpkt
commented
4 years ago Impacts of exchange rate volatility for Monero ecosystem contributors
Just completed a study quantifying the impact of exchange rate volatility for Monero-denominated delayed payouts. (Twitter thread, code)
Motivation
At the time of writing, most contributors need to pay their regular rent/tuition/food/etc bills in fiat currency. Exchange rate volatility resulting in loss of monero's buying power effectively reduces contributor compensation. To protect against this, proposals often include a volatility buffer. Rather than relying on gut intuition or short term market trends, this notebook analyzes exchange rate volatility over the last few years to suggest a statistically-appropriate buffer.
This method utilizes sliding window statistics over the monero/fiat exchange rate time series. The window width is determined by the time between quote issuance and payout. The length of the time series should be long enough to avoid recency bias, and short enough to remain representative (conditions in 2020 vary significantly from 2015). Here, we use 4 months for the window width (1 month to fundraise + 3 months to complete work), and initially select 2 years for the timeseries length.
2-year results
We observe an unfortunate asymmetry over the past two years, for example: contributors to projects with a 4-month timeline were more than twice as likely to experience a 50% decrease in compensation value (red line) than a 50% increase in compensation value (green line).
The red cross highlights that USD/XMR decreased over 65% of sliding 4-month windows (i.e. only 35% of contributors would receive payouts worth the quote price). The orange cross shows that an 80% likelihood of receiving a sufficient payout would be achieved with a +35% buffer.
An observation on methods
The last point highlights how this framework is formalized as a model:
[INPUTS:] X% confidence that a Y-month payout will cover the quote (based on the last Z months of data) can be achieved with an [OUTPUT:] V% volatility buffer.
Rephrasing the (orange cross) example above: 80% confidence that a 4-month payout will cover the quote (based on the last 24 months of data) can be achieved with a 35% volatility buffer.
4-year results
Now, let's consider what happens if we double the timeframe (so that the data include a bull market as well as cryptowinter). The previous plots spanned the last 2 years (blue dots); now let's extend this to 4 years (blue & green dots).
Now that our we include both the bull run leading up to the all time high and the subsequent decay, the extended data set contains new outcomes on both sides of the red breakeven line (+ a few outliers omitted on this plot that are shown in the next).
Over the 4 year history, we see that about 60% of the windows receive a payout covering the quoted value (red cross). Since the data set now includes an 8x bubble in its entirety, the volatility insurance rate for 80% confidence rises to a 170% buffer (orange cross).
Comments
I'm hoping that having a volatility risk management framework like this will increase funding accessibility for people/businesses that want to contribute but cannot afford speculate on income.
NOTE: Past performance is no guarantee of future results. This framework does not predict prices; it merely estimates uncertainty.
SarangNoether
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