A Quick Analysis of Historical Drawdowns for Bitcoin (BTCUSD)
Given the sudden influx of new money that has been chasing "cheap Altcoins", to a new investor Bitcoin's ~$15,000 price tag may appear unaffordable compared to an Altcoin priced at ~$1. So the aim is to remove the "unit bias" of each Altcoin by repricing them using the same current Total Coin Supply as Bitcoin.
Given the recent volatility in cryptos, the chart below gives a good snapshot and overview of the relative performance of the top 40 crypto-currencies/assets.
A Log-Periodic Power Law (LPPL) model is used to see whether the price action for Bitcoin follows a log-periodic oscillation model for a speculative bubble and predicting its subsequent "crash".
Mean Variance Optimization is performed on a test basket of 15 cryptocurrencies to create an optimal portfolio that lies on the Efficient Frontier. Several practical portfolio scenarios are examined.
Bitcoin (BTC/USD) price is modeled as a stochastic process following a fractional Brownian motion (fBm) demonstrated via a Hurst exponent (H) to try and measure the long term memory in the time series. Monte Carlo simulations were performed on this model to extend historical data and forecast Bitcoin price. Out of sample simulation results showed accuracy was to within ~10% of current prices. The 180 day (6 month) most probable (median) forward looking Bitcoin price prediction is ~USD14,211 by May 2018 and implying upside risk of ~95%. In addition, within this time frame Quantile risk/loss estimates show that there is only a 5% tail-end risk of a drop back to the ~$2000 price level (or a ~70% price drop).
Metcalfe's Law This puppy never gets old and I came across Metcalfe's law (that the value of a network is proportional to the square of the nodes/users in the network) being mentioned again regarding Bitcoin on CNBC with a Hedge Fund Guru Tom Lee forecasting $25,000 price for Bitcoin! (He used the Bitcoin adoption rate... Continue Reading →