Research

We decompose the realized variance into four components: downside tail, downside core, upside core and upside tail. This approach yields better prediction than established predictors such as VIX and the price-dividend ratio.

This paper investigates portfolio strategies involving only crypto-assets. We analyse optimal investment strategies for various types of investors. A concluding cluster analysis reveals which strategy performs best across various risk metrics.

This study delves into the momentum effects and investor risk aversion in the dynamic crypto-asset market. Utilizing the largest dataset yet in this context, we aim to illuminate the phenomena of momentum and reversal, addressing potential biases in cryptocurrency data. Our analysis allows us to explore the relationship between initial and subsequent returns and the impact of investor risk aversion. Significant momentum effects are observed for small coins in five out of six strategies, while mid-sized coins displayed consistent reversal effects. Large coins yielded mixed outcomes, implying varied risk aversion levels across coin sizes. The study underscores that constant relative risk aversion assumptions may not always correspond to actual investor behaviors, possibly influencing results.

Crypto-assets constitute a large and heterogenous asset class. Only a minority of crypto-assets qualify as crypto-currencies, while the large majority provide services that go beyond Bitcoin’s peer-to-peer payment system pioneered by Nakamoto (2008). This paper introduces a comprehensive taxonomy spanning crypto-currencies, service crypto-assets, smart-contract platforms, distributed applications and tokenized assets (including both fungible and non-fungible tokens). To illustrate stylized facts and capture various risks of the cross-section of crypto-assets this paper relies on a novel dataset covering more than 14,000 crypto-assets.

How does uncertainty of crypto-assets affect traditional asset classes? Using a vector autoregression (VAR) methodology, we answer this question by analyzing volatility spillovers between five asset classes (crypto-assets, stocks, bonds, fiat-currencies, and commodities). Given the vast heterogeneity within each asset class, our VAR specification accounts for cross-sectional variation across and within each asset class. By transforming the VAR residuals into sectoral shocks, we are able to distinguish between volatility spillovers across, and volatility co-movements within asset classes. We find that on average volatility of crypto-assets accounts for 15% of the volatility contagion received by traditional asset classes. The directional spillovers from crypto-assets to bonds and to fiat-currencies are particularly strong, capturing the wealth channel and the remittance channel, respectively.

As economies grow closer together it becomes increasingly important to understand linkages, both across and within different markets. To study these spillovers in a non parametric way the literature relies on the orthogonalized impulse responses functions (Sims, 1980) and the generalized impulse response functions (Koop et al., 1996). The present paper proposes an overarching theory, coined sectoral impulse responses functions, which not only includes orthogonalized and generalized impulse response functions as corner solution but also contains a wide a range of alternative specifications that allow to capture heterogeneous dynamics, both within and across markets.