Chiara Longo, PhD

For a long time, models have been built on the assumption that financial relationships are linear. However, events like Black Monday (and many studies motivated by them, e.g. “Conditional Heteroskedasticity in Asset Returns: A New Approach”, D.B. Nelson, 1991) have proven that, as a matter of fact, most of the relationships in finance are intrinsically non-linear.

This is one of the main reasons why linear models have become less and less popular and have been slowly replaced by non-linear ones. Indeed it is the nonlinear models that explain some important features common to most of financial data:

· Leptokurtosis, i.e. the tendency of financial asset returns to display a distribution with fat tails and a high peak around the mean;

· Volatility clustering, i.e. the tendency for volatility of asset returns to appear in bubbles

· Leverage effects, i.e. the tendency for a negative shock (e.g. price fall) to have a bigger impact than a positive one in terms of volatility.

Now, compared to conventional equities, crypto assets have been treated and described as a different beast. But, are they really so different?

Let’s just take a look.

· Leptokurtosis: here is the estimated distribution of Ethereum returns (blue line), compared to a Normal distribution (red line).

A simple kernel density-based analysis reveals that the returns series display critical deviations (of the leptokurtic type) from a Gaussian benchmark, and in particular fat tails — that may be induced by the presence of volatility clustering.

· Volatility clustering: here is the simple time series of Bitcoin daily returns on the past year. It’s easy to see how the volatility appears in bubbles; and this feature would be confirmed by a simple distribution analysis as above.

· Leverage effects: few recent studies (e.g. “Ether: Bitcoin’s competitor or ally?” J.Bouoiyour and R. Selmi) have showed an asymmetric response of Bitcoin returns volatility to positive and negative shocks, with larger effect coming from the latters.

So, after a closer look they don’t seem so different after all! Yes, the crypto market is highly volatile; but the stock market is quite risky too, if you don’t know how to navigate it. And the need to account for this risk, to use it and to make the best out of it, has the fueled a great deal of research; it has been the engine of quantitative finance evolution and today financial analysts have access to tools that were unconceivable few years ago.

So, after assessing the similarities between traditional and crypto assets, the question naturally arises: when we enter the parallel universe of cryptos, do we have the right tools, do we benefit from all the effort made to build powerful models that can understand and “exploit” volatility in more traditional markets, or do we simply go back at square one, toss a coin and hope for the best?

Well, the answer to this question is not an easy one. We are just taking the first steps in this new environment; we are grasping in the dark, as the socio-economic system struggles to accept and integrate cryptocurrencies and the decentralized models they enable. The effect the overall environment plays upon the returns of crypto assets is still difficult to parameterize.

And yet, as you could see above, the similarities between traditional and crypto assets entitle us to use the most complex and sophisticated tools available today to account for this high volatility that scares everyone. There is a beautiful, rich toolbox there that can help us understand the crypto market, navigate it and ultimately optimize our investments.

In coming posts we will continue to explore this analytic frontier. You’ll learn what these models may look like and how they can be used to estimate and predict the risks and rewards around crypto asset returns.

Related Posts

Crypto Investing: Can we have the cake and eat it too?

Crypto Portfolio Investing

Modeling Volatility in Cryptocurrencies

About the Author

Dr. Chiara Longo is Chief Economist at Pareto Network where she leads the Predictive Analytics and Economic Research department.