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Book contents
- Frontmatter
- Contents
- Editorial Board Members
- Acknowledgements
- Part I Institutionalisation of Digital Assets
- 1 Institutionalisation of Digital Assets
- 2 How and When Are Cryptocurrency Predictable? Backtesting Their Portfolio Economic Value
- 3 DeFi versus TradFi Valuation Using Multiples and Discounted Cash Flows
- Part II Digital Assets and Decentralised Finance
- Part III Regulations and Compliance of Digital Assets
- Part IV Cryptocurrency Economics and Monetary Policies
- Abbreviations
- Index
- References
2 - How and When Are Cryptocurrency Predictable? Backtesting Their Portfolio Economic Value
from Part I - Institutionalisation of Digital Assets
Published online by Cambridge University Press: 06 March 2025
Book contents
- Frontmatter
- Contents
- Editorial Board Members
- Acknowledgements
- Part I Institutionalisation of Digital Assets
- 1 Institutionalisation of Digital Assets
- 2 How and When Are Cryptocurrency Predictable? Backtesting Their Portfolio Economic Value
- 3 DeFi versus TradFi Valuation Using Multiples and Discounted Cash Flows
- Part II Digital Assets and Decentralised Finance
- Part III Regulations and Compliance of Digital Assets
- Part IV Cryptocurrency Economics and Monetary Policies
- Abbreviations
- Index
- References
Summary
In the light of the growing interest in crypto-assets and the quest for their institutionalisation, we examine the role that they can play as investable assets useful in standard portfolio problems when asset returns are predictable. In particular, we study whether a mix of macroeconomic factors and crypto-specific predictors can be combined to produce accurate and economically valuable pooled forecasts. With reference to Bitcoin data, we uncover that crypto returns are predictable out-of-sample. Moreover, when this crypto-asset is made available to a mean-variance optimising investor, it generates large risk-adjusted realised performance gains irrespective of the assumed risk aversion. The results on the predictability of cryptocurrencies are robust to a generalisation to Litecoin and Ripple, although on a shorter 2015–2020 sample. However, results turn mixed and come to depend on the assumed risk aversion, when we investigate the power of forecast combinations to generate economic value from the entire pool of cryptocurrencies.
Information
- Type
- Chapter
- Information
- Digital AssetsPricing, Allocation and Regulation, pp. 17 - 43Publisher: Cambridge University PressPrint publication year: 2025
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