Do crypto-currencies form a new asset class?
Název práce v češtině: | Do crypto-currencies form a new asset class? |
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Název v anglickém jazyce: | Do crypto-currencies form a new asset class? |
Klíčová slova: | crypto-currency, Bitcoin, ripple, Litecoin, stylized fact, asset return, autocorrelation, leverage effect, volatility clustering |
Akademický rok vypsání: | 2013/2014 |
Typ práce: | bakalářská práce |
Jazyk práce: | angličtina |
Ústav: | Institut ekonomických studií (23-IES) |
Vedoucí / školitel: | prof. PhDr. Ladislav Krištoufek, Ph.D. |
Řešitel: | skrytý![]() |
Datum přihlášení: | 20.04.2014 |
Datum zadání: | 20.04.2014 |
Datum a čas obhajoby: | 09.09.2015 00:00 |
Místo konání obhajoby: | IES |
Datum odevzdání elektronické podoby: | 30.07.2015 |
Datum proběhlé obhajoby: | 09.09.2015 |
Oponenti: | Mgr. Luboš Hanus, Ph.D. |
Kontrola URKUND: | ![]() |
Seznam odborné literatury |
1. Asmussen, S. Steady-State Properties of of GI/G /1 Stochastic Modelling and Applied Probability, 2003, 51, 266-301
2. Baek, C. & Elbeck, M. Bitcoins as an investment or speculative vehicle? A first look Applied Economics Letter, 2015, 22, 30-34 3. Barber, S.; Boyen, X.; Shi, E. & Uzun, E. Keromytis, A. D. (ed.) Bitter to Better — How to Make Bitcoin a Better Currency 29 Financial Cryptography and Data Security, Springer Berlin Heidelberg, 2012, 399-414 4. Black, F. Studies of stock price volatility changes Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economics Statistics Section, 1976, 177-181 5. Bouchaud, J. P. & Potters, M. More stylized facts of financial markets: leverage effect and downside correlations Physica A: Statistical Mechanics and its Applications, 2001, 299, 60-70 6. Box, G. E. P.; Jenkins, G. M. & Reinsel, G. C. Time Series Analysis: Forecasting and Control John Wiley & Sons, Hoboken, N. J., 2008 7. Clark, P. K. A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices Econometrica, 1973, 41, 135-155 8. Cocco, L.; Concas, G. & Marchesi, M. Using an Artificial Financial Market for studying a Cryptocurrency Market 2014 9. Cont, R. Teyssière, G. & Kirman, A. P. (ed.) Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models Long memory in economics, Springer Berlin Heidelberg, 2007, 289-309 10. Cont, R. Empirical properties of asset returns: stylized facts and statistical issues Quantitative Finance, 2001, 1, 223-236 11. D'Agostino, R. B.; Belanger, A. & D'Agostino, R. B. J. A Suggestion for Using Powerful and Informative Tests of Normality The American Statistician, 1990, 44, 316-321 12. Darrat, A.; Zhong, M. & Cheng, L. T. W. Intraday volume and volatility relations with and without public news Journal of Banking & Finance, 2007, 31, 2711-2729 13. Hill, B. M. A Simple General Approach to Inference About the Tail of a Distribution The Annals of Statistics, 1975, 3, 1163-1174 14. Jarque, C. M. & Bera, A. K. A Test for Normality of Observations and Regression Residuals International Statistical Review / Revue Internationale de Statistique, 1987, 55, 163-172 15. Kristoufek, L. What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis PLOS ONE, 2015, 10, 1-15 16. Kristoufek, L. BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era Scientific reports, 2013, 3, 1-7 17. Nakamoto, S. Bitcoin: A peer-to-peer electronic cash system 2008 18. Reid, F. & Harrigan, M. Altshuler, Y.; Elovici, Y.; Cremers, A. B.; Aharony, N. & Pentland, A. (ed.) An Analysis of Anonymity in the Bitcoin System Security and Privacy in Social Networks, Springer New York, 2012, 197-223 19. Royston, P. Approximating the Shapiro-Wilk W-test for non-normality Statistics and Computing, 1992, 2, 117-119 20. Royston, P. Comment on sg3.4 and an Improved D'Agostino Test Stata Technical Bulletin, 1992, 1, 23-24 21. Shapiro, S. S. & Wilk, M. B. An Analysis of Variance Test for Normality (Complete Samples) Biometrika, 1965, 52, 591-611 22. Valstad, O. C. A. & Vagstad, K. A bit risky? A comparison between Bitcoin and other assets using an intraday Value at Risk approach Institutt for industriell økonomi og teknologiledelse, 2014 23. Wilson-Nunn, D. & Zenil, H. On the Complexity and Behaviour of Cryptocurrencies Compared to Other Markets 2014 24. Yermeck, D. Is Bitcoin a real currency? An economic appraisal 2013 25. Zolotoy, L. & Melenberk, B. Trading Volume, Volatility, and the Serial Correlation of Stock Market Returns 2009 |
Předběžná náplň práce |
In this work, I would like to analyse phenomena of last couple of years - crypto-currencies, also known as virtual currencies. To simplify the whole analysis a little bit, I will primarily focus on first broadly known and by far the biggest crypto-currency currently used called Bitcoin. Obviously, there are many others but it is unnecessary to study each of them separately as they operate similarly and their market capitalization is much lower, meaning that their significance in real economy is much lower as well. On the example of Bitcoin currency I will try to spot and describe differences between virtual currencies generally and other known asset classes. For that purpose, I will examine its properties from statistical point of view using econometric modelling of financial time series and I will use stylized facts of stocks, bonds and other common asset classes to compare them with price and return data properties of the Bitcoin currency. This research should provide us with an answer to the question, whether crypto-currencies are or are not a new asset class itself and how could the observed differences be possibly used in an investment portfolio.
Hypotheses: 1. Bitcoin and crypto-currencies respectively form a new asset class. 2. Bitcoin is closer to a stock than to a currency. 3. How do stylized facts of Bitcoin currency differ in terms of Cont, R. (2001) from stylized facts of other asset classes (stocks, currencies, bonds)? 4. Could the observed properties be used during investment portfolio creation? Core Bibliography: 1. CONT, R. (2001) Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance. [Online] 1. p.223-236. Available from: http://www-stat.wharton.upenn.edu/~steele/Resources/FTSResources/StylizedFacts/Cont2001.pdf [Accessed: 30 May 2014] 2. BRIERE, M., OOSTERLINCK, K. & SZAFARZ, A. (2013) Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins. [Online] Available from: http://internet.jroepeumw.net/wp-content/uploads/2014/01/wp13031.pdf [Accessed: 30 May 2014] 3. DORIT, R. & SHAMIR, A. (2012) Quantitative Analysis of the Full Bitcoin Transaction Graph. [Online] Available from: http://eprint.iacr.org/2012/584.pdf [Accessed: 30 May 2014] 4. CAMPBELL, J. Y., LO, A. W. & MACKINLEY, A. C. (1997) The econometrics of financial markets. Princeton, N.J.: Princeton University Press 5. MILJKOVIĆ, V. & RADOVIĆ, O. (2006) Stylized facts of asset returns: Case of BELEX. Facta universitatis - series: Economics and Organization. [Online] 3 (2). p.189-201. Available from: http://facta.junis.ni.ac.rs/eao/eao200602/eao200602-09.pdf [Accessed: 30 May 2014] |