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Global portfolio diversification with cryptocurrencies


par Salma Ouali
Université de Neuchâtel  - Master of science in finance 2019
  

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Bitcoin is a swarm of cyber hornets serving the goddess of wisdom, feeding on the fire of truth, exponentially growing ever smarter, faster, and stronger behind a wall of encrypted energy

4.3. Performance measurement

In order to measure the portfolios performance, I rely on Sharpe ratio, which is a performance criteria widely used by practitioners and in literature.

u,,

=

^ - Rf

o-^,,

Sharpe ratio is defined as follow:

^ SR,,

With û,, the portfolio sample mean returns and o-^,, its sample standard deviation.

In addition, I compute the cumulative returns and the maximum drawdown of the investment strategies for each of the optimal portfolios.

5. Empirical results

5.1. Sample Characteristics

Table 1 and table 2 display summary statistics of daily log returns for cryptocurrencies and traditional assets.

Regarding traditional assets, equity indices exhibit slightly positive mean returns with S&P 500 showing the highest average daily returns and the lowest standard deviation. As expected, corporate and government bonds indices have low mean returns and showcase the lowest standard deviation among all financial assets. Commodities depicted by S&P GSCI gold provide the worst reward to volatility with negative mean return and an annualized Sharpe ratio of -0.04. On the other hand, real estate exhibit promising performance with a favorable annualized Sharpe ratio of 0.44.

Meanwhile, in line with Chuen et Al. (2017), I find that cryptocurrencies outperform traditional financial assets in terms of average daily returns and have the highest standard deviation by far.

14

As can be noticed, the 1% and 99 % percentiles show that extreme price movements are more severe for cryptocurrencies than for traditional assets. Albeit, the higher magnitude of positive returns is emphasized for cryptocurrencies when compared with negative ones.

In the case of skewness and kurtosis, I find Ripple, Dash and Litecoin to be positively skewed, a significant characteristic rational investors look for. In contrast, Bitcoin, equities, bonds and real estate display a negative skewness that indicates a higher tail risk. Additionally, I find that all-time series are leptokurtic. Eurostoxx 50 and Shanghai stock exchange present high excess kurtosis but to a lesser extent than altcoins.

Apart from Bitcoin, cryptocurrencies have very high excess kurtosis as the market for altcoins is still developing.

Therefore, the Jarque-Bera test supports the latter findings by rejecting the normality for all assets at 1% significance level. All the conventional assets and cryptocurrencies are not normally distributed. Moreover, I conduct Ljung box test to check for serial correlation. Hence, I find most conventional assets as well as Ripple to show significant autocorrelation. Nevertheless, Bitcoin, Dash, S&P500, Sovereign bonds and Gold display a low autocorrelation of daily returns, which suggests a lack of predictability.

Cryptocurrencies pronounced deviation from normality is visualized in Figure 1. The black line depicts a theoretical normal distribution of Bitcoin. I observe that the latter is the least volatile with more observations around the mean and a less pronounced tail than altcoins.

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Bitcoin is a swarm of cyber hornets serving the goddess of wisdom, feeding on the fire of truth, exponentially growing ever smarter, faster, and stronger behind a wall of encrypted energy








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