Daily Crypto Index


NilssonHedge provides a Daily Crypto Strategy index, based on the average returns for managers providing daily return data.

The managers included in the index are based on strategies that we have identified as consisting of Crypto Funds funds (or using similar strategies) reporting real-time daily numbers. Most of the Crypto Funds in the index are long-biased, and can be viewed as asset allocators between the various Digital Assets that exists in the Crypto Space. Several of them try to time the market through cash allocation via stable coins or USD, depending on the platform they are trading on. You should expect that this is index will exhibit high volatility due to the high volatility of most of the Digital Assets that represent the underlying assets for the index constituents.

Index Constituents for 2021 will be disclosed on or around Jan 1st 2021.

We do not impose minimum requirements on track-records or aum for this subset. Managers that drop out of the index are replaced with the average return of the index. NilssonHedge’s daily Crypto Strategy Index is believed to be the first comprehensive index for active managers in the Crypto universe. Please keep in mind that these calculations were done based on third party data and is subject to errors and omissions.

Methodology

In line with our method to build the database, we collect data from a large number of sources. A difference to monthly data is that we need to process daily returns much more carefully, apply filters and aggregate differently.

  • Data is collected daily. One of the many problems with daily data is that is not cleaned in the same manner and may contain noise.
  • To remove noise, for instance, driven by dividend payments that are not properly incorporated into the return stream, we take the median return over many share classes. This removes some of the spikes, but not all of them.
  • Moreover, we apply a statistical filter to remove outliers. Here, we control for market movements that cause the filter to remove true market returns. An example of this is, for instance, the CHF intervention in 2015, which caused large losses for several currency managers.
  • As we aggregate over share classes and most managers only show the “cheapest” share class in their official track-record, our returns tend to show a lower rate of return and potentially more volatility.
  • As part of our final statistical test, we correlate the equivalent monthly returns, from daily compounded returns, with monthly returns streams that already exist in the monthly database.
  • Entry and Exit fees are ignored.