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Article
Publication date: 26 November 2024

Viktor Santiago, Michel Charifzadeh and Tim Alexander Herberger

This study aims to investigate the impact of the 2022 collapse of the Terra-Luna ecosystem on volatility correlations among digital assets, including U.S. Terra, Luna, Bitcoin…

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Abstract

Purpose

This study aims to investigate the impact of the 2022 collapse of the Terra-Luna ecosystem on volatility correlations among digital assets, including U.S. Terra, Luna, Bitcoin, Ether, a Decentralized Finance index and U.S.-sourced conventional assets stocks, bonds, oil, gold and the dollar index. The primary research question addresses whether correlations increased between digital and conventional assets during the collapse.

Design/methodology/approach

A dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model was used to examine changes in volatility correlations during the market crash. Specifically, a data set of 1,442 close prices from 30-minute interval candles of digital and conventional asset prices are considered to provide a granular view of market dynamics during the sample period from January 3rd, 2022, to May 31st, 2022, including the crash event.

Findings

While the dynamic conditional correlation plots of the model indicate increased volatility, the results do not offer sufficient evidence to confirm an increase in correlations between digital and conventional assets during the Terra-Luna downfall. Furthermore, the authors confirm Bitcoin’s role as a diversifier with oil and observe the dollar index maintaining a negative correlation with Bitcoin during the crash, supporting Bitcoin’s function as a hedge against the U.S. dollar. However, the findings during the crash diverge from previous studies, reflecting shifts in correlation patterns in broader market downturns. Specifically, the authors identify the need for adaptive capital allocation strategies, as gold’s oscillation during the period suggests it may not serve as an effective hedge during black swan events.

Practical implications

The findings provide insights for investors, financial institutions and regulators to improve risk management, portfolio diversification, trading strategies and the formulation of consumer protection regulations. In addition, the results underscore the challenges of mitigating risks beyond regulatory measures and emphasize the importance of exercising caution for investors.

Originality/value

This study addresses the research gap in changes between conventional and digital asset volatility correlations during collapses in the digital asset space.

Details

Studies in Economics and Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1086-7376

Keywords

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Article
Publication date: 23 April 2020

Tim Alexander Herberger and Felix Reinle

The purpose of this paper is to outline and demonstrate a method for screening and selection of potential portfolio companies (PCs) during the screening phase in corporate venture…

556

Abstract

Purpose

The purpose of this paper is to outline and demonstrate a method for screening and selection of potential portfolio companies (PCs) during the screening phase in corporate venture capital.

Design/methodology/approach

The use of the data envelopment analysis (DEA) enables the consideration of individual, heterogeneous and multidimensional decision criteria in portfolio selection and the preceding screening process by the investor.

Findings

The result of this method is a relative ranking of the PCs, with all the PCs considered serving as peer group. A weighting of individual criteria is not necessary because it is part of the functionality of DEA. The authors validate the proposed approach in a case study and show that it can be well combined with other models and theoretical frameworks.

Practical implications

The method is particularly useful in two cases. First, if a highly specialized investor wishes to use a variety of individual selection criteria for portfolio selection. Second, if an investor only has insufficient (financial) data on potential PCs, but still wants to make a (pre-) selection based on observable (qualitative) characteristics. This model helps to make consistent, intersubjectively comprehensible decisions based on valid decision criteria and helps to optimize the decision-making process in the context of portfolio selection in CVC.

Originality/value

This method allows the systematic selection of an attractive group from a large number of potential PCs, based on observable characteristics and taking into account individual strategic investment objectives, without having to make assumptions about underlying distributions or weights of decision criteria.

Details

Studies in Economics and Finance, vol. 37 no. 3
Type: Research Article
ISSN: 1086-7376

Keywords

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