Skewness in the Cryptocurrency Market

Authors

  • Taoyi Yang

DOI:

https://doi.org/10.54691/bcpbm.v21i.1268

Keywords:

cryptocurrency, skewness, market

Abstract

The cryptocurrency market is generally accepted in the world, and its price has soared and plummeted sharply. Meanwhile, skewness is an index reflecting the rapid rise and fall of asset prices in a short period. Studying the relationship between the skewness of cryptocurrency and its returns can help risk evaders expect bad news and provide a reference for risk enthusiasts to make investment decisions. Therefore, through univariate combination analysis, this paper groups cryptocurrency according to the skewness of the previous month before buying and holding it in the next week. Moreover, the excess return series are calculated to make statistical tests on it. Then we construct a three-factor model of cryptocurrency and adjust the return series. To enhance the robustness of the conclusion, we also use other measures of skewness such as idiosyncratic skewness to conduct a univariate combination analysis. The results show that a positive correlation between cryptocurrency skewness and its returns exists, which can be used as a reference index of the returns.

Downloads

Download data is not yet available.

References

Amaya, D. et al. (2011). Does realized skewness and kurtosis predict the cross-section of equity returns? Available at SSRN 1785736.

Bali, T. G., Cakici, N. & Whitelaw, R. F. (2011). Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics. 99, 427-446.

Chen, J. & Zhang, Y. F. (2018). Realized skewness of Chinese stock market and the predictability of stock return. Journal of Financial Research, (09), 107-125.

Harvey, C. R. & Siddique, A. (2000). Conditional skewness in asset pricing tests. Journal of Finance, 55, 1263-1295.

Hou, X. Q. (2021). Study of cryptocurrency market volatility based on the ARIMA-GARCH model. Operations Research and Fuzziology, 11(4), 387-399.

Li, Y. et al. (2021). MAX momentum in cryptocurrency markets. International Review of Financial Analysis, 77, 101829.

Liu, Y. K., Tsyvinski, A. & Xi, W. (2022). Common risk factors in cryptocurrency. Journal of Finance, 77(2), 1133-1177.

Ma, Y., Hu, W. & Gao, C. (2021). A reanalysis of casualty between yield fluctuations of several major cryptocurrencies. Statistics and Application, 10(3), 529-537.

Phillips, R. C. & Gorse, D. (2018). Cryptocurrency price drivers: Wavelet coherence analysis revisited. PloS one, 13(4), e0195200.

Shahzad, S. J. H. et al. (2021). The pricing of bad contagion in cryptocurrencies: a four-factor pricing model. Finance Research Letters, 41, 101797.

Shen, D., Urquhart, A. & Wang, P. F. (2020). A three-factor pricing model for cryptocurrencies. Finance Research Letters, 34, 101248.

Xia, S. L. (2021). Is skewness an important factor in A-share pricing. Nanjing Business Review, (02), 108-122.

Xiang, W. R. (2020). Influence of skewness on the future cross-sectional return of stocks-an empirical study based on China’s share market. Master Degree Thesis of Guangdong University of Foreign Studies.

Xu, J. L. (2021). The predictability of skewness for the Chinese aggregate stock market excess return. Master Degree Thesis of Southwest Jiaotong University. DOI:10.27414/d.cnki.gxnju.2021.003013.

Yu, W., Wang, Y. R. & Yi, X. R. (2022). Features, mechanism, and prevention of cryptocurrencies’ financial risks. Jianghai Academic Journal, (01), 81-90.

Zhang, Y. F. (2018). Skewness risk of China stock market and the predictability of stock return. Xiamen: Master Degree Thesis of Xiamen University.

Zhou, W. H., Li, Y. N. & Tan, J. (2021). Research on the risk correlation between cryptocurrencies and stock market. China Soft Science, (S1), 116-126.

Downloads

Published

2022-07-20

How to Cite

Yang, T. (2022). Skewness in the Cryptocurrency Market. BCP Business & Management, 21, 425-432. https://doi.org/10.54691/bcpbm.v21i.1268