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The Cross-Section of Cryptocurrency Risk and Return

The Cross-Section of Cryptocurrency Risk and Return
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Year:    2020

Author:    Günther, Steffen, Fieberg, Christian, Poddig, Thorsten

Vierteljahrshefte zur Wirtschaftsforschung, Vol. 89 (2020), Iss. 4 : pp. 7–28

Abstract

Summary: We analyze the cross-section of more than 1200 cryptocurrencies derived from 350 exchanges in the time period from January 2014 to June 2020. Specifically, we investigate whether well-known cross-sectional characteristics like beta (Fama/MacBeth (1973)), size (Banz (1981)) or momentum (Jegadeesh/Titman (1993)) – which have been intensively investigated in the equities literature – explain the cross-section of cryptocurrency returns. We apply the monotonic relationship (Mr.) test developed by Patton and Timmermann (2010) to test for dependencies between characteristics and average portfolio returns and standard deviations. We extend the existing literature on cryptocurrencies showing that there are various characteristics which are able to explain cryptocurrency risk and return.

Zusammenfassung: Wir untersuchen den Querschnitt von über 1200 Kryptowährungen, gesammelt von 350 Handelsplätzen, in der Zeitspanne von Januar 2014 bis Juni 2020. Im speziellen untersuchen wir, ob weit verbreitete Charakteristika, wie Beta (Fama/MacBeth (1973)), Size (Banz (1981)) oder Momentum (Jegade‍esh/Titman (1993)) – die bereits intensiv in der Aktienliteratur untersucht werden – den Querschnitt der Kryptowährungsrenditen erklären können. Wir verwenden den Monotonic Relationship (MR) Test von Patton und Timmermann (2010) um auf Abhängigkeiten zwischen Charakteristika und durchschnittlichen Portfoliorenditen sowie Standardabweichungen zu testen. Wir erweitern die bestehende Literatur, indem wir zahlreiche Charakteristika identifizieren, die Risiko und Renditen von Kryptowährungen erklären können.

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.3790/vjh.89.4.7

Vierteljahrshefte zur Wirtschaftsforschung, Vol. 89 (2020), Iss. 4 : pp. 7–28

Published online:    2020-10

AMS Subject Headings:    Duncker & Humblot, Duncker & Humblot

Copyright:    COPYRIGHT: © Global Science Press

Pages:    22

Keywords:    Cryptocurrency Cryptocurrency risk Portfoliorendite G10 G11 G15

Author Details

Günther, Steffen

Fieberg, Christian

Poddig, Thorsten

  1. Diginomics Research Perspectives

    Asset Pricing in Digital Assets

    Günther, Steffen

    Glas, Tobias

    Poddig, Thorsten

    2022

    https://doi.org/10.1007/978-3-031-04063-4_7 [Citations: 0]

  1. Amaya, Diego, Peter Christoffersen, Kris Jacobs, and Aurelio Vasquez (2015): “Does realized skewness predict the cross-section of equity returns?” In: Journal of Financial Economics 118.1, pp. 135 – 167.  Google Scholar
  2. Amihud, Yakov (2002): “Illiquidity and stock returns: cross-section and time-series effects”. In: Journal of Financial Markets 5.1, pp. 31 – 56.  Google Scholar
  3. Asness, Clifford S., Anti Ilmanen, and Ronen Israel (2015): “Investing with Style”. In: Journal of Investment Management 13, pp. 27 – 63.  Google Scholar
  4. Asness, Clifford S., Tobias J. Moskowitz, and Lasse H. Pedersen (2013): “Value and Momentum Everywhere”. In: The Journal of Finance 68.3, pp. 929 – 985.  Google Scholar
  5. Ballis, Antonis, and Konstantinos Drakos (2020): “Testing for herding in the cryptocurrency market”. In: Finance Research Letters 33.  Google Scholar
  6. Banz, Rolf W. (1981): “The relationship between return and market value of common stocks”. In: Journal of Financial Economics 9.1, pp. 3 – 18.  Google Scholar
  7. Bhambhwani, Siddharth, Stefanos Delikouras, and George M. Korniotis (2019): “Do Fundamentals Drive Cryptocurrency Prices?” In: SSRN Electronic Journal.  Google Scholar
  8. Bianchi, Daniele (2020): “Cryptocurrencies as an Asset Class? An Empirical Assessment”. In: The Journal of Alternative Investments.  Google Scholar
  9. Bikhchandani, Sushil, and Sunil Sharma (2001): “Herd Behavior in Financial Markets”. In: IMF Staff Papers 47.  Google Scholar
  10. Black, Fischer, Michael C. Jensen, and Myron S. Scholes (1972): “The Capital Asset Pricing Model: Some Empirical Tests”. In: Praeger Publisher Inc.  Google Scholar
  11. Blitz, David C., and Pim van Vliet (2007): “The Volatility Effect”. In: The Journal of Portfolio Management 34.1, pp. 102 – 113.  Google Scholar
  12. Bouri, Elie, Rangan Gupta, and David Roubaud (2019a): “Herding behaviour in cryptocurrencies”. In: Finance Research Letters 29, pp. 216 – 221.  Google Scholar
  13. Bouri, Elie, Chi K. M. Lau, Brian Lucey, and David Roubaud (2019b): “Trading volume and the predictability of return and volatility in the cryptocurrency market”. In: Finance Research Letters 29, pp. 340 – 346.  Google Scholar
  14. Brauneis, Alexander and Roland Mestel (2018): “Price discovery of cryptocurrencies: Bitcoin and beyond”. In: Economics Letters 165, pp. 58 – 61.  Google Scholar
  15. Brauneis, Alexander, and Roland Mestel (2019): “Cryptocurrency-portfolios in a mean-variance framework”. In: Finance Research Letters 28, pp. 259 – 264.  Google Scholar
  16. Caporale, Guglielmo, and Alex Plastum (2019a): “Momentum Effects in the Cryptocurrency Market After One-Day Abnormal Returns”. In: CESifo Working Paper Series 7917, CESifo.  Google Scholar
  17. Caporale, Guglielmo, and Alex Plastum (2019b): “The day of the week effect in the cryptocurrency market”. In: Finance Research Letters 31, pp. 258 – 269.  Google Scholar
  18. Carhart, Mark M. (1997): “On Persistence in Mutual Fund Performance”. In: The Journal of Finance 52.1, pp. 57 – 82.  Google Scholar
  19. Chu, Jeffrey, Stephen Chan, and Yuanyuan Zhang (2020): “High frequency momentum trading with cryptocurrencies”. In: Research in International Business and Finance 52, 101176.  Google Scholar
  20. Cochrane, John H. (2011): “Presidential Address: Discount Rates”. In: The Journal of Finance 66.4, pp. 1047 – 1108.  Google Scholar
  21. Corbet, Shaen, Brian Lucey, Andrew Urquhart, and Larisa Yarovaya (2019): “Cryptocurrencies as a financial asset: A systematic analysis”. In: International Review of Financial Analysis 62, pp. 182 – 199.  Google Scholar
  22. da Gama Silva, Paulo Vitor Jordão, Marcelo Cabus Klotzle, Antonio Carlos Figueiredo Pinto, and Leonardo Lima Gomes (2019): “Herding behavior and contagion in the cryptocurrency market”. In: Journal of Behavioral and Experimental Finance 22, pp. 41 – 50.  Google Scholar
  23. Datar, Vinay T., Narayan Y. Naik, and Robert Radcliffe (1998): “Liquidity and stock returns: An alternative test”. In: Journal of Financial Markets 1.2, pp. 203 – 219.  Google Scholar
  24. Fama, Eugene F., and Kenneth R. French (1992): “The Cross-Section of Expected Stock Returns”. In: The Journal of Finance 47.2, pp. 427 – 465.  Google Scholar
  25. Fama, Eugene F., and Kenneth R. French (1993): “Common risk factors in the returns on stocks and bonds”. In: Journal of Financial Economics 33.1, pp. 3 – 56.  Google Scholar
  26. Fama, Eugene F., and Kenneth R. French (2013): “A Four-Factor Model for the Size, Value, and Profitability Patterns in Stock Returns”. In: SSRN Electronic Journal.  Google Scholar
  27. Fama, Eugene F., and Kenneth R. French (2015): “A five-factor asset pricing model”. In: Journal of Financial Economics 116.1, pp. 1 – 22.  Google Scholar
  28. Fang, Hsing, and Tsong-Yue Lai (1997): “Co-Kurtosis and Capital Asset Pricing”. In: The Financial Review 32.2, pp. 293 – 307.  Google Scholar
  29. Frazzini, Andrea, and Lasse Heje Pedersen (2014): “Betting against beta”. In: Journal of Financial Economics 111.1, pp. 1 – 25.  Google Scholar
  30. Glas, Tobias N. (2019): “Investments in Cryptocurrencies: Handle with Care!” In: The Journal of Alternative Investments 22.1, pp. 96 – 113.  Google Scholar
  31. Glas, Tobias N., and Thorsten Poddig (2018): “Kryptowährungen in der Asset-Allokation: Eine empirische Untersuchung auf Basis eines beispielhaften deutschen Multi-Asset-Portfolios”. In: Vierteljahrshefte zur Wirtschaftsforschung 87.3, pp. 107 – 128.  Google Scholar
  32. Groot, Wilma de, Joop Huij, and Weili Zhou (2011): “Another Look at Trading Costs and ShortTerm Reversal Profits”. In: SSRN Electronic Journal.  Google Scholar
  33. Gu, Shihao, Bryan Kelly, and Dacheng Xiu (2020): “Autoencoder asset pricing models”. In: Journal of Econometrics.  Google Scholar
  34. Harvey, Campbell R., and Akhtar Siddique (2000): “Conditional Skewness in Asset Pricing Tests”. In: The Journal of Finance 55.3, pp. 1263 – 1295.  Google Scholar
  35. Haugen, Robert A., and A. James Heins (1972): “On the Evidence Supporting the Existence of Risk Premiums in the Capital Market”. In: SSRN Electronic Journal.  Google Scholar
  36. Hu, Yang, Harold Glenn A. Valera, and Les Oxley (2019): “Market efficiency of the top marketcap cryptocurrencies: Further evidence from a panel framework”. In: Finance Research Letters 31, pp. 138 – 145.  Google Scholar
  37. Jegadeesh, Narasimhan, and Sheridan Titman (1993): “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”. In: The Journal of Finance 48.1, pp. 65 – 91.  Google Scholar
  38. Kaiser, Lars (2019): “Seasonality in cryptocurrencies”. In: Finance Research Letters 31.  Google Scholar
  39. Kelly, Bryan T., Seth Pruitt, and Yinan Su (2019): “Characteristics are covariances: A unified model of risk and return”. In: Journal of Financial Economics 134.3, pp. 501 – 524.  Google Scholar
  40. Köchling, Gerrit, Janis Müller, and Peter N. Posch (2019): “Price delay and market frictions in cryptocurrency markets”. In: Economics Letters 174, pp. 39 – 41.  Google Scholar
  41. Kristoufek, Ladislav, and Miloslav Vosvrda (2019): “Cryptocurrencies market efficiency ranking: Not so straightforward”. In: Physica A: Statistical Mechanics and its Applications 531.  Google Scholar
  42. Kuo Chuen, David Lee, Li Guo, and Yu Wang (2017): “Cryptocurrency: A New Investment Opportunity?” In: The Journal of Alternative Investments, pp. 16 – 40.  Google Scholar
  43. Lewellen, Jonathan (2015): “The Cross-section of Expected Stock Returns”. In: Critical Finance Review 4.1, pp. 1 – 44.  Google Scholar
  44. Liu, Weiyi, Xuan Liang, and Guowei Cui (2020): “Common risk factors in the returns on cryptocurrencies”. In: Economic Modelling 86, pp. 299 – 305.  Google Scholar
  45. Liu, Yukun, and Aleh Tsyvinski (2018): “Risks and Returns of Cryptocurrency”. In: Working Paper, Yale University. URL: https://www.nber.org/papers/w24877.  Google Scholar
  46. Liu, Yukun, Aleh Tsyvinski, and Xi Wu (2019): Common Risk Factors in Cryptocurrency. NBER Working Papier Series, no. 25882.  Google Scholar
  47. Makarov, Igor, and Antoinette Schoar (2020): “Trading and arbitrage in cryptocurrency markets”. In: Journal of Financial Economics 135.2, pp. 293 – 319.  Google Scholar
  48. Merton, Robert C. (1987): “A Simple Model of Capital Market Equilibrium with Incomplete Information”. In: The Journal of Finance 42.3, pp. 483 – 510.  Google Scholar
  49. Momtaz, Paul P. (2020): “Initial Coin Offerings”. In: PloS one 15(5): e0233018  Google Scholar
  50. Moskowitz, Tobias J., Yao Hua Ooi, and Lasse Heje Pedersen (2012): “Time series momentum”. In: Journal of Financial Economics 104.2, pp. 228 – 250.  Google Scholar
  51. Pástor, L’uboš, and Robert F. Stambaugh (2003): “Liquidity Risk and Expected Stock Returns”. In: Journal of Political Economy 111.3, pp. 642 – 685.  Google Scholar
  52. Patton, Andrew J., and Allan Timmermann (2010): “Monotonicity in asset returns: New tests with applications to the term structure, the CAPM, and portfolio sorts”. In: Journal of Financial Economics 98.3, pp. 605 – 625.  Google Scholar
  53. Sharpe, William F. (1992): “Asset allocation”. In: The Journal of Portfolio Management 18.2, pp. 7 – 19.  Google Scholar
  54. Shen, Dehua, Andrew Urquhart, and Pengfei Wang (2020): “A three-factor pricing model for cryptocurrencies”. In: Finance Research Letters 34, 101248.  Google Scholar
  55. Wei, Wang Chun (2018): “Liquidity and market efficiency in cryptocurrencies”. In: Economics Letters 168, pp. 21 – 24.  Google Scholar
  56. Zargar, Faisal Nazir, and Dilip Kumar (2019): “Informational inefficiency of Bitcoin: A study based on high-frequency data”. In: Research in International Business and Finance 47, pp. 344 – 353.  Google Scholar
  57. Zhang, Yuanyuan, Stephen Chan, Jeffrey Chu, and Hana Sulieman (2020): “On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market”. In: Journal of Risk and Financial Management 13.1, pp. 1 – 14.  Google Scholar

Section Title Page Action Price
Steffen Günther, Christian Fieberg and Thorsten Poddig: 7
 The Cross-Section of Cryptocurrency Risk and Return 7
1 Introduction 8
2 Literature Review 9
3 Data and Methodology 11
4 Results 7
4.1 Returns 7
4.2 Risk 7
4.3 Reversal and Momentum in Cryptocurrencies – a closer look 7
4.4 Double Sorts 7
4.5 Transaction Costs 7
5 Conclusion 7
References 7