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1/ Liquidity is definitely a factor but the fundamental reason for volatility autocorrelation is information propagation.
One mental model for markets is a viscous fluid. Shocks to the system play out as damped oscillations in the price discovery process.
One mental model for markets is a viscous fluid. Shocks to the system play out as damped oscillations in the price discovery process.
2/ In this analogy, the autocorrelation of volatility is the amplitude decaying over time at rate proportional to viscosity. It's intuitive if you think about dropping heavy objects into liquid.
The viscosity parameter is the efficiency of the market. What are its components?
The viscosity parameter is the efficiency of the market. What are its components?
3/ Markets are where self-interested actors come together in a dance of price discovery. A simple example, let's say CPI prints lower than expected.
First NLP strategies parse headlines and slam BTC books. Hard coded triggers, for example 6% inflation -> BTC +2%.
First NLP strategies parse headlines and slam BTC books. Hard coded triggers, for example 6% inflation -> BTC +2%.
The #BTC 200D MA (200-day moving average of price) is an important level for understanding #Bitcoin.
I think the $BTC 2Y MA and its 5x multiple are more valuable long-term, but mid-term the 200MA is worth paying attention to.
Here's my perspective π[THREAD]π
I think the $BTC 2Y MA and its 5x multiple are more valuable long-term, but mid-term the 200MA is worth paying attention to.
Here's my perspective π[THREAD]π
From #Bitcoin's introduction in 2009 until its first halving, $BTC stayed above the 200D MA period until its first bear market period. The 200D MA served as resistance, but breaking above it led to prices higher than the 200D MA breakdown the previous year, signaling new πtrend
In the next #Bitcoin era, the first after the halving at the end of 2012, $BTC rose rapidly and parabolically, blowing off early and testing the 200D MA for support to continue the bull run. (cont)
