Crypto-Assets in Managed Portfolios

Christoph Wechsler, August 2018

Crypto-assets pair great risk with mindboggling return potential. Last year’s increasing public awareness of Bitcoin’s astronomic rise sparked many questions of how to benefit from this opportunity. Since then we’ve seen increasing numbers of often particularly wealthy clients who demand to include crypto-assets in their wealth management or portfolio advice. There is a feeling that, for some reason, they are being kept out by their bank or advisor.

There are good reasons for such hesitation: the new asset class is cumbersome to invest in, highly unregulated and volatile like leveraged options; but it can also boost performance, diversify a portfolio and tickle one’s fancy.

Persephone has – for test purposes and to gain experience – incorporated the crypto currency Bitcoin (which is the largest of many and tradeable via certificates outside of complicated crypto-markets) as an asset class in its automated asset management engines. In this article we want to share some insights.


Single asset risks

Whereas the extreme volatility may be comparable to leveraged options, the attractive feature of crypto assets is their largely capital market-independent volatility. Without suffering from losses in the rest of the portfolio, a crypto asset may quintuple in value in the same time period. In contrast, most regular options are much more market dependent and thus in line with your portfolio performance overall. Crypto assets are in the current stage entirely unsuitable for hedging and should be viewed as sources of pure risk.

Modelling the individual crypto asset’s risk over a relatively short period up to one year using financial risk models of the generalized heteroskedasticity class (GARCH types) is possible with caution. Volatility clustering is equally present but possibly large asymmetries and a well calibrated News impact curve (how strongly the asset reacts to positive or negative news) are crucial.


The News Impact asymmetries of crypto assets are a constant reminder of the importance of active expert judgement and continuous evaluation for new asset classes. The highly unregulated and controversial asset class should only be included in a client portfolio if the asset management team has sufficient expertise in evaluating its prospects and the flexibility to respond to changing environs such as in delegated portfolio management. For advice-only portfolios the delay between relevant news and achieved trading may be a relevant obstacle for now.


Dependency structure

Modelling crypto currencies using standard portfolio analysis techniques indicates very quickly shifting dependencies and a very high impact on model parameterization for this new asset class. We illustrate the impact of only one parameter, the time frame, on the well-known Pearson correlation measure (underlying Markovitz theories) to highlight the difficulty of measuring risk using assumptions such as normal distributions and symmetry.

Within a few weeks assets can appear moderately positively or negatively correlated, the speed of the change of the correlation between Bitcoin and other asset classes (S&P 500 shown here) is remarkable.



Measuring dependency with straightforward correlations ignores fat tails and imposes symmetry. However, crypto currencies and Bitcoins are asset classes which can realistically change +250% or -70% in just one month. Therefore, their payoff structures differ significantly from standard models which rely on symmetric gaussian correlation. Managing crypto-currencies as an additional asset class therefore requires robust models to capture the dependency in the tail risks and needs a daily monitoring to understand changes and reasonably react to them.

A look at the return pattern of daily returns for the broad US equity index S&P 500 and Bitcoin (in USD) indicates an asymmetric and nearly independent shape which is clearly non-Gaussian. Only on one day the two assets share an extreme tail value (-24% for BTC and -4.2% for SP500 on 05.02.2018) which immediately has an outsized effect on standard dependency measures.


This shows that dependency measures without superimposed distributional assumptions are necessary to adequately capture the dependency structure of traditional and crypto assets in one portfolio and react to changing market realities. Copula-based models which dynamically calibrate to real data, select and parameterize distributions based on observed structures are an advantage here.



The extremely high return potential and largely independent nature of crypto assets makes them a valuable addition to portfolios. In order to maintain the agreed risk structure of the managed portfolios, the allocation should remain small and monitoring should occur frequently. For our robotIQ portfolios (see article here) we decided to allow small allocations in Bitcoin for portfolios of increased risk target.

As the investment procedures for real crypto assets are currently cumbersome and the anonymity of this process may pose significant institutional compliance problems, derivative products based on crypto indices but traded on organized exchanges may be the best entry point for traditional asset managers.

An overview of the requirements and our implementation:


Crypto assets can be a distinguishing feature and great performance contributor for portfolio managers. The inadequacy of traditional asset allocation models can pose a model risk but should not deter managers from incorporating this exciting opportunity in principle.

Clients who are adequately educated about the risks associated with such investments can benefit from small allocations to “pure risk” assets with astonishing return potential.

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