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Verteilungseigenschaften der Renditen von Kryptowährungen: Sind sie mit Aktien vergleichbar?

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  2. Economics, Finance, Business & Management
  3. Vierteljahrshefte zur Wirtschaftsforschung
  4. Vol. 87, Issue 3
  5. pp. 83–105

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Varmaz, A., Abée, S. Verteilungseigenschaften der Renditen von Kryptowährungen: Sind sie mit Aktien vergleichbar?. Vierteljahrshefte zur Wirtschaftsforschung, 87(3), 83-105. https://doi.org/10.3790/vjh.87.3.83
Varmaz, Armin and Abée, Stephan "Verteilungseigenschaften der Renditen von Kryptowährungen: Sind sie mit Aktien vergleichbar?" Vierteljahrshefte zur Wirtschaftsforschung 87.3, 2018, 83-105. https://doi.org/10.3790/vjh.87.3.83
Varmaz, Armin/Abée, Stephan (2018): Verteilungseigenschaften der Renditen von Kryptowährungen: Sind sie mit Aktien vergleichbar?, in: Vierteljahrshefte zur Wirtschaftsforschung, vol. 87, iss. 3, 83-105, [online] https://doi.org/10.3790/vjh.87.3.83
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Verteilungseigenschaften der Renditen von Kryptowährungen: Sind sie mit Aktien vergleichbar?

Varmaz, Armin | Abée, Stephan

Vierteljahrshefte zur Wirtschaftsforschung, Vol. 87 (2018), Iss. 3 : pp. 83–105

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Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/vjh.87.3.83
Language
German
Pages
23
Keywords

Author Details

Armin Varmaz, Hochschule Bremen.

Stephan Abée, Hochschule Bremen.

Cited By

  1. Kryptowährungen in der Asset-Allokation: Eine empirische Untersuchung auf Basis eines beispielhaften deutschen Multi-Asset-Portfolios

    Glas, Tobias N.

    Poddig, Thorsten

    Vierteljahrshefte zur Wirtschaftsforschung, Vol. 87 (2018), Iss. 3 P.107

    https://doi.org/10.3790/vjh.87.3.107 [Citations: 5]

References

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  37. Jegadeesh, N. (1990): Evidence of predictable behavior of security returns. The Journal of Finance, 45 (3), 881–898.  Google Scholar
  38. Jegadeesh, N. und S. Titman (1993): Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48 (1), 65–91.  Google Scholar
  39. Keim, D. B. (1983): Size-Related Anomalies and Stock Return Seasonality: Further Empirical Evidence. Journal of Financial Economics, 12 (1), 13–32.  Google Scholar
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  41. Lamoureux, C. G. und W. D. Lastrapes (1990): Heteroskedasticity in stock return data: volume versus GARCH effects. The Journal of Finance, 45 (1), 221–229.  Google Scholar
  42. Ludvigson, S. C. und S. Ng (2007): The empirical risk-return relation: A factor analysis approach. Journal of Financial Economics, 83 (1), 171–222.  Google Scholar
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  45. Mandelbrot, B. (1967): The variation of some other speculative prices. The Journal of Business, 40 (4), 393–413.  Google Scholar
  46. Mandelbrot, B. und H. M. Taylor (1967): On the distribution of stock price differences, Operations Research, 15 (6), 1057–1062.  Google Scholar
  47. Markowitz, H. (1952): Portfolio selection. Journal of Finance, 7 (1), 77–91.  Google Scholar
  48. McNeil, A. J. und R. Frey (2000): Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7 (3), 271–300.  Google Scholar
  49. Mittnik, S. und S. T. Rachev (1993): Modeling asset returns with alternative stable distributions. Econometric reviews, 12 (3), 261–330.  Google Scholar
  50. Officer, R. R. (1972): The distribution of stock returns. Journal of the American Statistical Association, 67 (340), 807–812.  Google Scholar
  51. Osterrieder, J. und J. Lorenz (2017): A statistical risk assessment of bitcoin and its extreme teil behavior. Annals of Financial Economics, 12 (01), 1750003.  Google Scholar
  52. Pagan, A. (1996): The econometrics of financial markets, Journal of Empirical Finance, 3 (1), 15–102.  Google Scholar
  53. Poddig, T. (1996): Analyse und Prognose von Finanzmärkten. Bad Soden, Uhlenbruch.  Google Scholar
  54. Rohrbach, J., S. Suremann und J. Osterrieder (2017): Momentum and Trend Following Trading Strategies for Currencies Revisited-Combining Academia and Industry. SSRN. doi:10.2139/ssrn.2949379.  Google Scholar
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  56. Schiereck, D., W. De Bondt und M. Weber (1999): Contrarian and momentum strategies in Germany. Financial Analysts Journal, 55, 104–116.  Google Scholar
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  62. Deetz, M. et al. (2009): An evaluation of conditional multi-factor models in active asset allocation strategies: an empirical study for the German stock market. Financial Markets and Portfolio Management, 23 (3), 285–313.  Google Scholar
  63. Dzhabarov, C. und W. T. Ziemba (2010): Do seasonal anomalies still work? Journal of Portfolio Management, 36 (3), 93.  Google Scholar
  64. Engle, R. F. (1982): Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007.  Google Scholar
  65. Cont, R. (2001): Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1 (2), 223–236.  Google Scholar
  66. Hirshleifer, J. (1971): The private and social value of information and the reward to inventive activity. The American Economic Review, 61 (4), 561–574.  Google Scholar
  67. Hirshleifer, J. und J. G. Riley (1979): The analytics of uncertainty and information-an expository survey. Journal of Economic Literature, 17 (4), 1375–1421.  Google Scholar
  68. Hubrich, S. (2017): Know when to hodl them, know when to fodl them: An Investigation of Factor Based Investing in the Cryptocurrency Space. SSRN Electronic Journal, 1–54. 10.13140/RG.2.2.35090.96969.  Google Scholar
  69. Adhami, S., C. Giudici und S. Martinazzi (2017): Why Do Businesses Go Crypto? An Empirical Analysis of Initial Coin Offerings. SSRN Electronic Journal. doi:10.2139/ssrn.3046209.  Google Scholar
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  71. Aparicio, F. M. und J. Estrada (2001): Empirical distributions of stock returns: European securities markets, 1990–95. The European Journal of Finance. Taylor Francis, 7 (1), 1–21.  Google Scholar
  72. Asness, C. S., T. J. Moskowitz und L. H. Pedersen (2013): Value and momentum everywhere. The Journal of Finance. Wiley Online Library, 68 (3), 929–985.  Google Scholar
  73. Bollerslev, T. (1986): Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.  Google Scholar
  74. Lakonishok, J., A. Shleifer und R. W. Vishny (1994): Contrarian investment, extrapolation, and risk. The Journal of Finance, 49 (5), 1541–1578.  Google Scholar
  75. Lamoureux, C. G. und W. D. Lastrapes (1990): Heteroskedasticity in stock return data: volume versus GARCH effects. The Journal of Finance, 45 (1), 221–229.  Google Scholar
  76. Campbell, J. Y. et al. (1997): The econometrics of financial markets. Princeton University press, Princeton, NJ.  Google Scholar
  77. Keim, D. B. (1983): Size-Related Anomalies and Stock Return Seasonality: Further Empirical Evidence. Journal of Financial Economics, 12 (1), 13–32.  Google Scholar
  78. Jegadeesh, N. (1990): Evidence of predictable behavior of security returns. The Journal of Finance, 45 (3), 881–898.  Google Scholar
  79. Jacobs, H. (2015): What explains the dynamics of 100 anomalies?, Journal of Banking and Finance, 57, 65–85.  Google Scholar
  80. Ince, O. S. und R. B. Porter (2006): Individual equity return data from Thomson Datastream: Handle with care! Journal of Financial Research, 29 (4), 463–479.  Google Scholar
  81. Hagerman, R. L. (1978): More evidence on the distribution of security returns. The Journal of Finance, 33 (4), 1213–1221.  Google Scholar
  82. Glaser, M. und M. Weber (2003): Momentum and Turnover: Evidence from the German Stock Market. Schmalenbach Business Review, 55, 108–135.  Google Scholar
  83. French, K. R. (1980): Stock returns and the weekend effect. Journal of Financial Economics, 8 (1), 55–69.  Google Scholar
  84. Jegadeesh, N. und S. Titman (1993): Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48 (1), 65–91.  Google Scholar
  85. Ludvigson, S. C. und S. Ng (2007): The empirical risk-return relation: A factor analysis approach. Journal of Financial Economics, 83 (1), 171–222.  Google Scholar
  86. Lux, T. und M. Marchesi (1999): Scaling and criticality in a stochastic multi-agent model of a financial market. Nature, 397 (6719), 498–500.  Google Scholar
  87. Mandelbrot, B. (1963): The variation of certain speculative prices. The Journal of Business, 36 (4), 394–419.  Google Scholar
  88. Shannon, A. (1948): A Mathematical Theory of Communication. Urbana: University of Illinois Press.  Google Scholar
  89. Schiereck, D., W. De Bondt und M. Weber (1999): Contrarian and momentum strategies in Germany. Financial Analysts Journal, 55, 104–116.  Google Scholar
  90. Scherer, B. (2002): Portfolio resampling: Review and critique. Financial Analysts Journal, 58 part 6, 98–102.  Google Scholar
  91. Mandelbrot, B. (1967): The variation of some other speculative prices. The Journal of Business, 40 (4), 393–413.  Google Scholar
  92. De Bondt, W. F. M. und R. H. Thaler (1987): Further evidence on investor overreaction and stock market seasonality. Journal of Finance, 42 (3), 557–581.  Google Scholar
  93. De Bondt, W. F. M. und R. H. Thaler (1984): Does the Stock Market Overreact? Journal of Finance, 40 (3), 793–805.  Google Scholar
  94. Danielsson, J. und J. P. Zigrand (2006): On time-scaling of risk and the square-root-of-time rule. Journal of Banking and Finance, 30 (10), 2701–2713.  Google Scholar
  95. Fieberg, C., A. Varmaz und T. Poddig (2016): Covariances vs. characteristics: what does explain the cross section of the German stock market returns? Business Research, 9 (1), 27–50.  Google Scholar
  96. Fama, E. F. und A. B. Laffer (1971): Information and capital markets. The Journal of Business, 289–298.  Google Scholar
  97. Fama, E. F. (1991): Efficient capital markets: II. The Journal of Finance, 46, 1575–1617.  Google Scholar
  98. Fama, E. F. (1970): Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25, 383–417.  Google Scholar
  99. Fama, E. F. (1965): The Behavior of Stock-Market Prices. The Journal of Business, 38 (1), 34–105.  Google Scholar
  100. Fama, E. F. (1963): Mandelbrot and the stable Paretian hypothesis. The Journal of Business, 36 (4), 420–429.  Google Scholar
  101. Sidorovitch, I. (2010): Bewertungsmechanismen und der Stand der Integration auf dem europäischen Aktienmarkt. Berlin/Heidelberg, Pro Business.  Google Scholar
  102. Campbell, J. Y. et al. (2001): Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. The Journal of Finance, 56 (1), 1–43.  Google Scholar
  103. Campbell, J. Y. und J. H. Cochrane (2000): Explaining the poor performance of consumption-based asset pricing models, The Journal of Finance, 55 (6), 2863–2878.  Google Scholar
  104. Campbell, J. Y. und L. Hentschel (1992): No news is good news. Journal of Financial Economics, 31 (3), 281–318.  Google Scholar
  105. Cochrane, J. H. (2011): Presidential address: Discount rates. The Journal of Finance, 66 (4), 1047–1108.  Google Scholar
  106. Connor, G. (1984): A unified beta pricing theory. Journal of Economic Theory. Elsevier, 34 (1), 13–31.  Google Scholar
  107. Connor, G. und R. A. Korajczyk (1986): Performance measurement with the arbitrage pricing theory. A new framework for analysis. Journal of Financial Economics, 15 (3), 373–394.  Google Scholar
  108. Connor, G. und R. A. Korajczyk (1989): An intertemporal equilibrium beta pricing model. Review of Financial Studies, 2 (3), 373–392.  Google Scholar
  109. Rohrbach, J., S. Suremann und J. Osterrieder (2017): Momentum and Trend Following Trading Strategies for Currencies Revisited-Combining Academia and Industry. SSRN. doi:10.2139/ssrn.2949379.  Google Scholar
  110. Goyal, A. und P. Santa-Clara (2003): Idiosyncratic risk matters! The Journal of Finance, 58 (3), 975–1007.  Google Scholar
  111. Gray, J. B. und D. W. French (1990): Empirical comparisons of distributional models for stock index returns. Journal of Business Finance & Accounting, 17 (3), 451–459.  Google Scholar
  112. Silvennoinen, A. und T. Teräsvirta (2009): Multivariate GARCH models. In: Handbook of financial time series. Springer, 201–229.  Google Scholar
  113. Tay, A. S. und K. F. Wallis (2000): Density forecasting: a survey. Journal of Forecasting, 19 (4), 235–254.  Google Scholar
  114. Wang, J. N., J. H. Yeh und N. Y. P. Cheng (2011): How accurate is the square-root-of-time rule in scaling tail risk: A global study. Journal of Banking and Finance, 35 (5), 1158–1169.  Google Scholar
  115. Mandelbrot, B. und H. M. Taylor (1967): On the distribution of stock price differences, Operations Research, 15 (6), 1057–1062.  Google Scholar
  116. Markowitz, H. (1952): Portfolio selection. Journal of Finance, 7 (1), 77–91.  Google Scholar
  117. McNeil, A. J. und R. Frey (2000): Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7 (3), 271–300.  Google Scholar
  118. Mittnik, S. und S. T. Rachev (1993): Modeling asset returns with alternative stable distributions. Econometric reviews, 12 (3), 261–330.  Google Scholar
  119. Officer, R. R. (1972): The distribution of stock returns. Journal of the American Statistical Association, 67 (340), 807–812.  Google Scholar
  120. Osterrieder, J. und J. Lorenz (2017): A statistical risk assessment of bitcoin and its extreme teil behavior. Annals of Financial Economics, 12 (01), 1750003.  Google Scholar
  121. Pagan, A. (1996): The econometrics of financial markets, Journal of Empirical Finance, 3 (1), 15–102.  Google Scholar
  122. Poddig, T. (1996): Analyse und Prognose von Finanzmärkten. Bad Soden, Uhlenbruch.  Google Scholar
  123. Deetz, M. et al. (2009): An evaluation of conditional multi-factor models in active asset allocation strategies: an empirical study for the German stock market. Financial Markets and Portfolio Management, 23 (3), 285–313.  Google Scholar
  124. Dzhabarov, C. und W. T. Ziemba (2010): Do seasonal anomalies still work? Journal of Portfolio Management, 36 (3), 93.  Google Scholar
  125. Engle, R. F. (1982): Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007.  Google Scholar
  126. Cont, R. (2001): Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1 (2), 223–236.  Google Scholar
  127. Hirshleifer, J. (1971): The private and social value of information and the reward to inventive activity. The American Economic Review, 61 (4), 561–574.  Google Scholar
  128. Hirshleifer, J. und J. G. Riley (1979): The analytics of uncertainty and information-an expository survey. Journal of Economic Literature, 17 (4), 1375–1421.  Google Scholar
  129. Hubrich, S. (2017): Know when to hodl them, know when to fodl them: An Investigation of Factor Based Investing in the Cryptocurrency Space. SSRN Electronic Journal, 1–54. 10.13140/RG.2.2.35090.96969.  Google Scholar
  130. Adhami, S., C. Giudici und S. Martinazzi (2017): Why Do Businesses Go Crypto? An Empirical Analysis of Initial Coin Offerings. SSRN Electronic Journal. doi:10.2139/ssrn.3046209.  Google Scholar
  131. Andersen, T. G. et al. (2001): The distribution of realized stock return volatility. Journal of Financial Economics. Elsevier, 61 (1), 43–76.  Google Scholar
  132. Aparicio, F. M. und J. Estrada (2001): Empirical distributions of stock returns: European securities markets, 1990–95. The European Journal of Finance. Taylor Francis, 7 (1), 1–21.  Google Scholar
  133. Asness, C. S., T. J. Moskowitz und L. H. Pedersen (2013): Value and momentum everywhere. The Journal of Finance. Wiley Online Library, 68 (3), 929–985.  Google Scholar
  134. Bollerslev, T. (1986): Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.  Google Scholar
  135. Lakonishok, J., A. Shleifer und R. W. Vishny (1994): Contrarian investment, extrapolation, and risk. The Journal of Finance, 49 (5), 1541–1578.  Google Scholar
  136. Lamoureux, C. G. und W. D. Lastrapes (1990): Heteroskedasticity in stock return data: volume versus GARCH effects. The Journal of Finance, 45 (1), 221–229.  Google Scholar
  137. Campbell, J. Y. et al. (1997): The econometrics of financial markets. Princeton University press, Princeton, NJ.  Google Scholar
  138. Keim, D. B. (1983): Size-Related Anomalies and Stock Return Seasonality: Further Empirical Evidence. Journal of Financial Economics, 12 (1), 13–32.  Google Scholar
  139. Jegadeesh, N. (1990): Evidence of predictable behavior of security returns. The Journal of Finance, 45 (3), 881–898.  Google Scholar
  140. Jacobs, H. (2015): What explains the dynamics of 100 anomalies?, Journal of Banking and Finance, 57, 65–85.  Google Scholar
  141. Ince, O. S. und R. B. Porter (2006): Individual equity return data from Thomson Datastream: Handle with care! Journal of Financial Research, 29 (4), 463–479.  Google Scholar
  142. Hagerman, R. L. (1978): More evidence on the distribution of security returns. The Journal of Finance, 33 (4), 1213–1221.  Google Scholar
  143. Glaser, M. und M. Weber (2003): Momentum and Turnover: Evidence from the German Stock Market. Schmalenbach Business Review, 55, 108–135.  Google Scholar
  144. French, K. R. (1980): Stock returns and the weekend effect. Journal of Financial Economics, 8 (1), 55–69.  Google Scholar
  145. Jegadeesh, N. und S. Titman (1993): Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48 (1), 65–91.  Google Scholar
  146. Ludvigson, S. C. und S. Ng (2007): The empirical risk-return relation: A factor analysis approach. Journal of Financial Economics, 83 (1), 171–222.  Google Scholar
  147. Lux, T. und M. Marchesi (1999): Scaling and criticality in a stochastic multi-agent model of a financial market. Nature, 397 (6719), 498–500.  Google Scholar
  148. Mandelbrot, B. (1963): The variation of certain speculative prices. The Journal of Business, 36 (4), 394–419.  Google Scholar
  149. Shannon, A. (1948): A Mathematical Theory of Communication. Urbana: University of Illinois Press.  Google Scholar
  150. Schiereck, D., W. De Bondt und M. Weber (1999): Contrarian and momentum strategies in Germany. Financial Analysts Journal, 55, 104–116.  Google Scholar
  151. Scherer, B. (2002): Portfolio resampling: Review and critique. Financial Analysts Journal, 58 part 6, 98–102.  Google Scholar
  152. Mandelbrot, B. (1967): The variation of some other speculative prices. The Journal of Business, 40 (4), 393–413.  Google Scholar
  153. De Bondt, W. F. M. und R. H. Thaler (1987): Further evidence on investor overreaction and stock market seasonality. Journal of Finance, 42 (3), 557–581.  Google Scholar
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Abstract

There are more than 1,500 other cryptocurrencies, which differ significantly from each other in terms of their usage or the underlying blockchain technology. Most of these cryptocurrencies can be traded on exchanges and can serve as investment instruments. In this paper, the empirical distribution properties of their returns for a very broad cross-section are examined and compared with those of stock returns. Returns on cryptocurrencies have several characteristics similar to equity returns: Returns are more likely observable around the averages; the autocorrelation of returns is very weak; the phenomenon of volatility clustering and the asymmetry of gains and losses do exist; the factor analysis of the returns reveals that one factor (the first principal component) explains about 60 percent of the common variation of returns; there is a weekday effect. However, there are some differences. For example, no heavy-tails can be identified and the momentum effect is only very weak. The results suggest that the stylezed facts of cryptocurrency returns are not very different from stock returns.

Table of Contents

Section Title Page Action Price
Armin Varmaz / Stephan Abée: Verteilungseigenschaften der Renditen von Kryptowährungen: Sind sie mit Aktien vergleichbar? 1
Zusammenfassung 1
Summary 1
1 Einleitung 2
2 Literatur 4
3 Daten 6
4 Ergebnisse 8
4.1 Verteilungseigenschaften 8
4.2 Autokorrelation und Volatility-Clustering 1
4.3 Saisonale Effekte und Kennzahleneffekte 1
4.4 Hauptkomponentenanalyse 1
5 Fazit 2
Literaturverzeichnis 2

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