Cryptocurrencies: return, risk, perfomance, relationship with other assets and composition of investment portfolios: Covid challenges
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".