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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Risk Measures in Cryptocurrency Market |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 65 |
| Date | 2019 |
| Abstract Text | Over the past few years, the cryptocurrency market experienced very high volatility prices, which has attracted scholars to analyze and anticipate this market. Nowadays, there exist more than 2000 different digital coins, among which Bitcoin is the most famous one. Since the level of returns and volatilities in the stock market are lower than those of the cryptocurrency market, in this article, we try to find some new risk measures. The goal of this research is by using time series analysis and some statistical methods, such as statistical process control, extreme values theory and loss distribution approach to estimate loss distribution of some cryptocurrencies which have the highest rate of capital in the whole market, in particular, Bitcoin, Ethereum, and Ripple. To do that, we need to first, review the pricing models which can be applied to the digital coins. The next step is stylized facts about these coins. Then, we have to defi ne and compare different measures of risk. These can be obtained by estimating the loss distribution and time series analysis. Next, we can discuss whether these risk measures are capable of being applied to the stock market or not. To verify that idea, we made a portfolio and optimized it in several different ways. We used three main risk measures such as Standard Deviation, Value at Risk and Expected Tail Loss. Then, by using obtained weights, we constructed a new portfolio and used future data set. Deriving the return of each portfolio proved which risk measure works better in this context. Relation and dependencies are another essential part of this research. We applied different correlations, such as Distance correlation and rank correlations, and also copula to discover the relations between different markets, gold, USD index, S&P 500 and S&P GSCI for instance. Since we are dealing with several indices, we combined all three coin into one index. Also, we used change-point detection to separate time in three main periods. In the end, we made some applications of Extreme Value Theory and fi nd out which coin is riskier. |
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