The mean–variance portfolio optimization framework has been argued to be problematic in practices due to extreme weights, corner solutions and high sensitivity to estimation errors in the input parameters (Harris et al., 2017). Several robust optimisation methods have been widely adopted in the literature to mitigate the impact of estimation error (Kolm et al., 2014).
The cryptocurrency market has gained growing interests from investors, regulators and the media since the first cryptocurrency Bitcoin was proposed in 2008. Evidence shows that Bitcoin are mainly used as a speculative investment rather than the medium of exchange (Baur et al., 2018), and have diversification benefits to other financial assets (Bouri et al., 2017; Kajtazi and Moro, 2018). Platanakis et al. (2018) claim that there is very little difference in performance between naïve diversification and optimal diversification of cryptocurrencies. Platanakis and Urquhart (2019) suggest using more sophisticated portfolio techniques that control for estimation errors in the input parameters to manage cryptocurrency portfolios to improve performance.
The aim of this research project is to explore the diversification benefits of cryptocurrencies, to apply more advanced and sophisticated portfolio optimization techniques to construct the cryptocurrency portfolios and to examine the portfolio performance in contrast to the traditional benchmark.
In addition to the Director of Studies (Dr. Linzhi Tan), the supervisory team will include Dr. Jeremy Cheah and Dr. Thong Dao.
Baur, D.G., Hong, K., & Lee, A.D. (2018). Bitcoin: medium of exchange or speculative assets? Journal of International Financial Markets, Institutions & Money, 54, 177–189.
Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L.I. (2017). On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier? Finance Research Letter, 20, 192–198.
Harris, R. D., Stoja, E., & Tan, L. (2017). The dynamic Black–Litterman approach to asset allocation. European Journal of Operational Research, 259(3), 1085-1096.
Kolm, P. N. , Tütüncü, R. , & Fabozzi, F. J. (2014). 60 Years of portfolio optimization: Practical challenges and current trends. European Journal of Operational Research, 234 (2), 356–371.
Kajtazi, A., & Moro, A. (2019). The role of bitcoin in well diversified portfolios: A comparative global study. International Review of Financial Analysis, 61, 143-157.
Platanakis, E., Sutcliffe, C., & Urquhart, A. (2018). Optimal vs naïve diversification in cryptocurrencies. Economics Letters, 171, 93-96.
Platanakis, E., & Urquhart, A. (2019). Portfolio management with cryptocurrencies: The role of estimation risk. Economics Letters, 177, 76-80.
Candidates should have:
- A strong interest in financial markets and innovations such as cryptocurrencies and Blockchain
- High level of motivation and intellectual curiosity
- A good background in numerical analysis with reasonable knowledge of and experience in trading strategy and quantitative methods (e.g. regression, portfolio optimisation etc.)
- (Desirable) Coding skills in one or more applications (e.g. R, Matlab)
For more information please visit the NTU Doctoral School – Research Degrees webpages.
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