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Overview of Risk Estimation Methods for Cryptocurrency

Aishwarya Ganesan and Balusa Venkata Sai Harika

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Ganesan, A. and Harika, B.V.S., 2021. Overview of Risk Estimation Methods for Cryptocurrency. United International Journal for Research & Technology (UIJRT), 2(8), pp.133-137.

Abstract

In the current climate, cryptocurrency trading is becoming increasingly more accessible through smartphones. Coupled with a high market value of cryptocurrency, it is essential to strategize trading and investment to make the most of the cryptocurrency boom. This paper provides a systematic survey on risk estimation methods for cryptocurrencies based on the empirical results of relevant academic literature. Volatility analysis revealed that cryptocurrency markets are highly volatile in comparison to stock and gold. Value-at-Risk (VaR) measures showed varied distributions. Tail risk analysis was commonly measured through VaR using quantile regression. Ratio-based estimations were part of the larger goal of portfolio optimization techniques. Single-objective and multi-objective optimizations relied on Omega and Sharpe ratio respectively. This survey provides useful guidance in assessing various empirical, statistical and advanced risk estimation for cryptocurrency markets. It also provides an insight into quantitative analysis for risk estimation and the utility of risk estimation in portfolio optimization.

Keywords: Bitcoin, Cryptocurrency, Regression, Risk, Sharpe Ratio, Volatility.

References

  1. M. Dempere, “Factors Affecting the Return and Volatility of Major Cryptocurrencies,” 2019 Sixth HCT Information Technology Trends (ITT), 2019, pp.104-109,doi: 10.1109/ITT48889.2019.9075117.
  2. Liang, L. Li, D. Zeng and Y. Zhao, “Correlation-based Dynamics and Systemic Risk Measures in the Cryptocurrency Market,” 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), 2018, pp. 43-48, doi: 10.1109/ISI.2018.8587395
  3. Liang, L. Li, W. Chen and D. Zeng, “Towards an Understanding of Cryptocurrency: A Comparative Analysis of Cryptocurrency, Foreign Exchange, and Stock,” 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), 2019, pp. 137-139, doi: 10.1109/ISI.2019.8823373.
  4. N. Y. Vo and G. Xu, “The volatility of Bitcoin returns and its correlation to financial markets,” 2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC), 2017, pp. 1-6, doi: 10.1109/BESC.2017.8256365.
  5. Silahli, B., Dingec, K. D., Cifter, A., & Aydin, N. (2019). Portfolio value-at-risk with two-sided Weibull distribution: Evidence from cryptocurrency markets. Finance Research Letters, 101425.
  6. Hrytsiuk, P., Babych, T., & Bachyshyna, L. (2019, September). Cryptocurrency portfolio optimization using Value-at-Risk measure. In 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019) (pp. 385-389). Atlantis Press.
  7. Nguyen, L. H., Chevapatrakul, T., & Yao, K. (2020). Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach. Journal of Empirical Finance, 58, 333-355.
  8. Borri, N. (2019). Conditional tail-risk in cryptocurrency markets. Journal of Empirical Finance, 50, 1-19.
  9. Estalayo, Ismael, et al. “Return, diversification and risk in cryptocurrency portfolios using deep recurrent neural networks and multi-objective evolutionary algorithms.” 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019.
  10. Castro, Javier Gutiérrez, et al. “Crypto-assets portfolio optimization under the omega measure.” The Engineering Economist 65.2 (2020): 114-134.

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