Spectral risk for digital assets
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | REVIEW OF QUANTITATIVE FINANCE AND ACCOUNTING |
| Citation | |
| Doi | http://dx.doi.org/10.1007/s11156-024-01313-0 |
| Keywords | Spectral risk measure; Value-at-risk; Expected shortfall; Digital assets; CRIX; Portfolio; G11; G32 |
| Description | Digital assets (DAs) are a unique asset class that presents investors with opportunities and risks that are contingent upon their particular characteristics such as volatility, type, and profile, among other factors. Among DAs, cryptocurrencies (CCs) have emerged as the most liquid asset class, holding this distinction for almost a decade. However, while CCs offer a high level of liquidity, investors must be aware of the potential risks and rewards associated with investing in this asset class, and should conduct a thorough evaluation before making any investment decisions. Our study examines the risk profile of CCs through portfolio analysis, utilizing Spectral Risk Measures (SRMs) as the commonly applied method. In this study, we investigate the application of SRMs in assessing the risk structure of CC portfolios, and their alignment with investors' risk preferences. We employ SRMs to evaluate the CC index CRIX and portfolios constructed from the most liquid 10 CCs from the Blockchain Research Center (BRC), optimizing different SRMs.Our empirical findings suggest that various optimal portfolio allocations can be formulated to meet the unique risk appetites of individual investors. All Quantlets (macros, code snippets) are available via quantlet.com and instructive educational element are available on quantinar.com. |
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