Bitcoin academic articles free
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Velde , in his capacity as senior economist of the Chicago Federal Reserve, concluded his discussion of bitcoins with an intriguing possible scenario that bitcoins could eventually form the basis of a new monetary system. In a throwback to the gold standard era, money will not be based on a fiat currency and central banks will have only limited flexibility in its creation. However, the quantity of money would not be affected by the geological and political uncertainties associated with physical gold.
In particular, this study examines whether bitcoin has the three main attributes of a currency: This paper also investigates the value of bitcoin as an investable financial asset by incorporating it in portfolios that include major world currencies, U. This study assesses whether the inclusion of bitcoins in such an investment portfolio enhances its efficiency. Daily closing prices and trading volume of bitcoins was taken from bitcoincharts.
Gold is often considered a good hedge and a safe haven currency during times of extreme financial distress Bauer and Lucey Hence, in addition to major world currencies, we also compared bitcoin price returns to gold price returns.
We obtained daily price quotes for gold from the Bloomberg Professional database. To assess portfolio performance of bitcoins when this asset is included in a diversified portfolio with major asset classes, we needed proxy portfolios of various asset classes.
For this part of the study, we included major indexes representing each asset class. This index evaluates the U. For the final part of the analysis, we needed investable assets representing these asset classes in order to capture market capitalization data.
Exchange traded funds ETFs provided the solution. In an attempt to examine if bitcoin behaves like a currency, we examined the distributional properties of its returns along with those of other major currencies and gold. Another objective of the analysis presented here was to examine whether bitcoins can serve to enhance portfolio performance metrics. As mentioned earlier, we examined portfolios formed with indices representing currencies, stocks, bonds, real estate, commodities, and the fear index with and without the addition of bitcoins to these portfolios.
Optimum portfolios were examined after a simulation of 1, trials in which random weights for each asset class were drawn in each trial; then the portfolio that optimized each examined measure was selected for illustration. Consistent with most investor preferences, we chose to examine long-only portfolios. At first, we examined portfolios that minimized the total variance.
This objective was defined as: The optimization of asset weights w pi in each portfolio p was conducted subject to the constraints that the portfolio is fully invested all asset weights sum up to 1 and that each asset weight is greater than or equal to zero long-only portfolios.
We examined two versions of the minimum variance portfolio: The latter optimization procedure minimized the negative variance of long-only portfolios under the assumption that investors choose to ignore positive deviation and are only concerned about minimizing the negative deviation in portfolio returns. We also measured portfolio efficiency with the Sharpe ratio and the Sortino ratio. These measures maximize risk- adjusted excess returns.
The optimization process may be defined as: We used the risk-free rate RFR as the target return in the optimization process. Next, following post-modern portfolio theory, we examined the portfolios that maximize the measured Omega ratio. The Omega ratio is based on the proportional distribution of returns above and below a specified target. Among its many advantages see Shadwick and Keating , the one that pertains most to an investor is that it minimizes the potential for extreme losses.
In the portfolio optimization process using the Omega ratio, we used zero as the target to differentiate positive from negative returns. The optimization process can be defined as: Where, F x dx represents the respective cumulative distribution functions. As mentioned earlier, 1, trials were conducted for each portfolio over the sample period in order to select the optimal portfolio weights.
The probability of loss is simply the relative frequency with which a negative return is observed in the 1, trials. The final part of the analysis used the Black-Litterman approach to examine whether bitcoins remain in an investment portfolio even after incorporating various pessimistic views regarding the performance of bitcoins.
Data enters the Black-Litterman model from two sources: Historical returns and co-variances were used to produce baseline forecasts that were supplemented with quantified views. A vector of revised expectations, conditioned on these views, was then entered into the model to produce optimum portfolio weights.
To begin with, one must start with a neutral portfolio. Black and Litterman suggested starting with the equilibrium market capitalization weighted portfolio. We used ETFs rather than indices for this part of the analysis. Also, lacking a suitable ETF to represent the value of the U.
Starting with the neutral market capitalization weighted portfolio, we employed the Black-Litterman approach to tilt the portfolios reflecting various pessimistic views regarding bitcoin returns over the next period. We focused on these pessimistic views regarding bitcoins because we were interested in observing whether bitcoins are still a consequential part of an optimal portfolio after model expectations have been revised to incorporate these pessimistic outcomes.
The limited number of daily bitcoin transactions, although growing rapidly, suggests that this asset fails in serving as a medium of exchange capable of being traded for a wide variety of goods and services. Nor are bitcoins used much as a unit of account. In fact, even merchants that accept bitcoins still price their goods and services in sovereign fiat currencies, such as the U. So, bitcoin does not have the key attributes of a currency and instead should be regarded as a very illiquid financial asset.
All other examined currencies, as well as gold, provided U. The Japanese yen is unique in its negative average daily return, attributable to the weakness of the Japanese currency relative to the U. The large daily returns for bitcoin were also accompanied by larger risk, as evidenced by its standard deviation, which was the highest of all currencies examined, as well as that of gold.
While bitcoin returns do not suffer from skewness, it is highly leptokurtic, indicating a fat tailed distribution. This underscores the risk inherent in bitcoins, which was not evidenced in most of the examined currencies, except for the Swiss franc. Gold returns also demonstrate high kurtosis.
However, given that gold returns have a much smaller standard deviation, bitcoin was the most risky currency or pseudo currency examined. The correlation of daily returns between bitcoins and other assets are shown in Tables 2A and 2B.
From Table 2A, it is worth noting that daily bitcoin returns have very low or insignificant correlations with other world currencies. Correlations among the other currencies examined were quite large and statistically significant. Returns from the Japanese yen had the lowest correlation with other currencies, but these correlations were still larger than those of bitcoins and all were statistically significant.
As shown in Table 2B, bitcoin returns also had a very low correlation with gold returns. These results reinforce the conclusion that bitcoin does not behave much like a currency. Table 2B also demonstrates that bitcoin had low or insignificant correlations with major investable asset classes, such as stocks, bonds, real estate, commodities, and the fear index. It is remarkable that almost all of the daily returns of major currencies and various asset classes in Tables 2A and 2B had a negligible impact on the daily returns of bitcoins.
This indicates that bitcoins could serve as a potent diversifier for an investment portfolio. Table 3A shows the metrics of optimal portfolios formed with major asset classes, excluding bitcoins. Table 3B shows optimal portfolios formed with the inclusion of bitcoins. To minimize daily noise, we examined weekly portfolio returns over the sample period July to December One thing that is startling to note is that the remarkable run-up of bitcoin values in the short sample period caused the optimal portfolios those with maximum Sharpe and Sortino ratios in Table 3B to be comprised entirely of bitcoins.
Table 3A demonstrates a similar trend with stocks when portfolios were formed without bitcoins. Together, optimization of the Sharpe and Sortino ratios are heavily influenced by high returns.
Both Tables 3A and 3B demonstrate that the probability of loss is minimal with portfolios that maximize Omega. Comparing Tables 3A and 3B also demonstrates that portfolio returns are higher, and the risk probability of incurring a loss is much lower, when bitcoins are added to an investment portfolio with every portfolio optimization measure examined. Figure 1 provides a visual depiction of portfolios that maximize the Omega ratio on a quarterly basis.
For this analysis, we used daily returns in order to obtain enough observations during each quarter. The short sample period yielded 14 quarters of results third quarter until fourth quarter As illustrated, bitcoin was an integral part of an optimal Omega maximizing portfolio in virtually every quarter with just a couple of exceptions.
Finally, we employed the Black-Litterman approach to incorporate some pessimistic views regarding bitcoins in an investment portfolio. As suggested by Black and Litterman , the neutral portfolio has a market capitalization weighted portfolio comprising bitcoins, stocks, bonds, real estate, and commodities. Given the relative market capitalization, bitcoins account for less than 3 percent of the neutral portfolio.
When pessimistic views were incorporated in absolute terms—based on the assumption that one expects bitcoins to lose 50 percent of their value over the next period—the composition of bitcoins in the optimal portfolio dropped by nearly 60 percent down to 1. However, bitcoins remained in the optimal portfolio even with the revised expectation. The remaining columns in Table 4 display optimal portfolios under pessimistic views regarding bitcoins, but in relative terms.
The remaining columns of Table 4 show optimal portfolios under the views that bitcoins will underperform stocks by 50 percent, bonds by 50 percent, real estate by 50 percent, and commodities by 50 percent, respectively. Once again, the Black-Litterman algorithm continued to provide positive allocations to bitcoins in the optimal portfolio.
Results presented in Table 4 demonstrate that bitcoins have the potential to enhance portfolio performance even under pessimistic views. In conclusion, the analysis reveals that, as a currency, bitcoins are in their infancy.
Currently, limited acceptability makes bitcoins a poor medium of exchange, while high volatility makes this asset a poor store of value. This could change over time. Therefore, it can be useful to hold bitcoins as a component within a diversified investment portfolio. Trading and investing in a virtual currency, such as bitcoins, is readily accessible to individual investors.
Uncertainty regarding taxation of bitcoin transactions has been ameliorated with new IRS tax guidance IR Additionally, competing exchanges have brought down the costs of trading bitcoins to a minimal level. Given these observations, and the conclusions from the empirical analysis, individual investors can benefit from holding a small amount of bitcoins in a diversified portfolio. Financial planners can open bitcoin trading accounts or digital wallets for their clients at a number of digital currency exchanges, such as coinbase.
While recommending virtual currencies as an investment asset, it is important to underscore the risk involved with investing in these assets.
Rizun and Christopher E. Wilmer on the bitcoin forum bitcointalk. A call for papers was issued on the 15th September with the deadline set at 31st December but following delays in formalising the review process the inaugural issue was not published until December The journal encourages authors to digitally sign a file hash of submitted papers, which will then be timestamped into the bitcoin blockchain.
Authors are also asked to include a personal bitcoin address in the first page of their papers. From Wikipedia, the free encyclopedia. Alternative currency Decentralized autonomous organization Digital gold currency Digital asset Private currency World currency. Archived from the original on 11 January Retrieved 11 January Archived from the original on 10 January Retrieved 10 January Archived from the original on 12 January Retrieved 12 January Ethereum Ethereum Classic KodakCoin.