Synthetic portfolio theory assets (SPTA) are priced using a primary data feed that pulls relevant information. For example, a volume-specific product utilizes volume data as a core starting point. The data is then manipulated to smooth curves, normalize volatility spikes within a trailing period range, and then fed to an oracle for pricing the asset. These calculations occur on a compute server, similar to how our option chain execution begins. We are working to decentralize and verify compute accuracy for further transparency in our asset pricing.
Depending on what the underlying asset is designed to hedge, multiple data streams could be used to pull specific pre-compute variables to model more nuanced products. However, in either case, the goal is to minimize volatility over longer durations for our hedge-oriented products. This also makes them less susceptible to manipulation because all of our assets are aggregated calculations; therefore, for manipulation to occur in the SPTA, sustained manipulation in each of the underlying base feeds would need to occur for a longer duration than our built-in smoothing windows. In some cases, this results in sustained manipulation attacks for more than 1 quarter.
Our trade-oriented asset list reduces this timeline of smoothing to allow for more significant intra-day moves. While more volatile, these specific assets are protected similarly by introducing random sampling into their windows that are pair matched to prior periods. In addition, just like our lower volatility products, SPTAs do not track singular entities but aggregated market pockets. Therefore, the capital required to manipulate even shorter windowed products would be larger than the profit that could be gained due to our mint limits.
Mint rates across our entire platform are scaled based on several key features, including supply/demand for the asset, the realized volatility of the asset class, and the available liquid POL on the platform under which redemptions would be fulfilled.