Cryptocurrencies: return, risk, perfomance, relationship with other assets and composition of investment portfolios: Covid challenges
Notice bibliographique
Résumé
Cryptocurrencies have been increasing their relevance in academic life and, especially, among investors. This market is still not mature, as it has now only reached a decade of existence. However, cryptos performed impressively in terms of returns, despite having strong volatility. As an interesting asset in terms of investment, it is important to understand its risk-return performance, the relationship with other financial assets (e.g.: forex and stocks), and the impact of its inclusion in investment portfolios. Therefore, we use Pearson's correlation to test the relationship between variables of the same asset class, and to see those that are more similar, we study the Impulse Response Function (IRF) to understand the impact that a shock on one asset generates on the other, and finally, we apply the multivariate GARCH to evaluate the existing connections in terms of volatility. To estimate the optimal investment portfolios we use the Markovitz model and the Sharpe Ratio. All these phases were carried out for the period from 2015 to 2021, and the period of COVID-19 was also analyzed separately. The cryptocurrencies that will be studied are Bitcoin, Ethereum, Ripple, Litecoin, Dash, Stellar, Monero, Dogecoin, Verge, NXT. The fiat currencies in the analysis are the American dollar, euro, British pound, Japanese yen, Australian dollar, swiss-franc, Canadian dollar, and New Zealand dollar. In terms of stock indexes we use S&P500, STOXX 50, FTSE 100, NIKKEI 225, ASX 200, SMI, TSX e NZX 50 During the phases of this research work, we concluded that the forex market and stock indexes still do not have great relevance in the cryptocurrency price trend and vice versa. It was also found that the cryptocurrency market is more interconnected than other asset classes, with the impacts of shocks occurring in digital assets is more accentuated than in all others. The same happens for volatility. Regarding the optimal portfolio, we can note that, including the American S&P500 index and gold in a portfolio, the best solution is to hold 20% of Bitcoin and 7% of Ethereum as well. With the arrival of the pandemic, all the previous points became even more salient and the presence of cryptocurrencies in the optimal portfolio is also greater. This study will allow investors to have more information in the decision-making process for their investments and will also allow policy makers to better understand the evolutionary trends of cryptocurrencies, considering its future regulation and eventual adoption for the monetary system.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».