An adaptive boundary model for defining regimes on prediction markets

Volatility greatly increase risk for the trader, the paper seeks to find a way of understanding the current regimes expected oscillation, this in turn gives a better insight into the current market conditions, thus gaining better insight into the associated risk. Prediction markets are still in their infant phase compared to traditional exchanges, thus making research about risk management limited. The paper introduces an adaptive boundary model for defining regimes, which is found to be useful across not only prediction markets, however it also employs useful across traditional and crypto exchanges, as well as a general analytics tool.