On the Return Distributions of a Basket of Cryptocurrencies and Subsequent Implications

Börner, Christoph J.; Hoffmann, Ingo; Krettek, Jonas; Kuerzinger, Lars; Schmitz, Tim

doi:10.2139/ssrn.3851563

Cited by 3 publications

(5 citation statements)

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“…Table 2 shows the selected time periods for each cryptoasset and the amount of available prices during that period. To cope with missing prices, the Last Observation Carried Forward approach, as in Schmitz and Hoffmann (2020); Trimborn et al (2020), andBörner et al (2021) has been utilised.…”

Section: Datamentioning

confidence: 99%

“…They note that they are extremely speculative and risky assets. To model their extreme tail behaviour and derive overall risk characteristics, Börner et al (2021); ; Omari and Ngunyi (2021), and Osterrieder and Lorenz (2017) applied Extreme Value Theory (EVT). All found that cryptocurrencies show fat-tailed behaviour with higher risk characteristics than other asset classes.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Risk Characteristics of the Cryptocurrency Market: A GARCH-EVT-Copula Approach

Bruhn

Ernst

2022

JRFM

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The cryptocurrency market offers significant investment opportunities but also entails higher risks as compared to other asset classes. This article aims to analyse the financial risk characteristics of individual cryptocurrencies and of a broad cryptocurrency market portfolio. We construct a portfolio comprising the 20 largest cryptocurrencies, which cover 82.1% of the total cryptocurrency market. The returns are examined for extreme tail risks by the application of Extreme Value Theory. We utilise the GARCH-EVT approach in combination with a novel algorithm to automatically determine the optimal threshold to model the tail distribution. Furthermore, we aggregate the individual market risks with a t-Student Copula to investigate possible diversification effects on a portfolio level. The empirical analysis indicates that all examined cryptocurrencies show high volatility in their price movements, whereby Bitcoin acts as the most stable cryptocurrency. All return distributions are heavy-tailed and subject to extreme tail risks. We find strong, positive intra-market correlations, in particular with the two largest cryptocurrencies Bitcoin and Ethereum. No diversification effect can be achieved by aggregating market risks. On the contrary, a negligibly lower expected return and higher joint extreme returns can be observed. From this analysis, it can be concluded that investments in individual cryptocurrencies as well as in a portfolio show extreme risks of losses. From the investor’s point of view, a possible strategy of risk reduction through portfolio formation within cryptocurrencies is only promising to a limited extent and does not offer a satisfactory solution to significantly reduce the risk within this asset class.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Risk Characteristics of the Cryptocurrency Market: A GARCH-EVT-Copula Approach

Bruhn

Ernst

2022

JRFM

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“…As data gaps appear in the time series of most CCs, we exclude all CCs with five or more consecutive missing observations. By utilizing the Last Observation Carried Forward (LOCF) approach, as previously done in Schmitz and Hoffmann (2020), Trimborn et al (2020), Börner et al (2021), we are able to include all CCs with smaller data gaps. Hence, N = 27 CCs remain in our data set, as depicted in Tab.…”

Section: Datamentioning

confidence: 99%

“…The analyses in Börner et al (2021) showed that the family of SDIs is the most promising for modeling the distribution of the returns of the CCs. Therefore, this family of functions is also used in the present study and will be introduced here in detail.…”

Section: Appendix a Stable Distribution -The Tail Parameter αmentioning

confidence: 99%

“…One general problem is that CCs are different from traditional asset classes, especially in terms of extreme tails and corresponding tail risk. Against this background, Majoros and Zempléni (2018) and Börner et al (2021) show that the stable distribution (SDI) is well suited to statistically model the returns of CCs overall and especially in the tail area. Thus, we extend our data base by including the tail parameter α of the SDI to specifically consider the tail risk.…”

Section: Introductionmentioning

confidence: 99%

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Bitcoin: Like a Satellite or Always Hardcore? A Core-Satellite Identification in the Cryptocurrency Market

Börner,

Hoffmann,

Krettek

et al. 2021

Preprint

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Cryptocurrencies (CCs) become more interesting for institutional investors' strategic asset allocation and will be a fixed component of professional portfolios in future. This asset class differs from established assets especially in terms of the severe manifestation of statistical parameters. The question arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components, based on modern methods from image / pattern recognition.

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Bitcoin: like a satellite or always hardcore? A core–satellite identification in the cryptocurrency market

et al. 2022

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Cryptocurrencies (CCs) have become increasingly interesting for institutional investors’ strategic asset allocation and will therefore be a fixed component of professional portfolios in the future. However, this asset class differs from established assets primarily in that it has a higher standard deviation and tail risk. The question then arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, with the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components based on modern methods from image/pattern recognition.

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On the Return Distributions of a Basket of Cryptocurrencies and Subsequent Implications

Cited by 3 publications

References 0 publications

Assessing the Risk Characteristics of the Cryptocurrency Market: A GARCH-EVT-Copula Approach

Assessing the Risk Characteristics of the Cryptocurrency Market: A GARCH-EVT-Copula Approach

Bitcoin: Like a Satellite or Always Hardcore? A Core-Satellite Identification in the Cryptocurrency Market

Bitcoin: like a satellite or always hardcore? A core–satellite identification in the cryptocurrency market

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