Dual Focus on Systemic Risk in Portfolio Management

dc.contributor.authorNeděla, David
dc.contributor.authorTichý, Tomáš
dc.contributor.otherEkonomická fakultacs
dc.description.abstractIn this paper, we examine a complex portfolio selection strategy with a dual emphasis on systemic risk. This strategy or only its elements are advisable for both portfolio managers as well as macroprudential regulators. In particular, first, we present the concept of an early warning system (alarm) employing selected entropy measures, which allow us to detect systemic risk in financial markets. Secondly, we apply the two-phase optimization framework to determine the optimal composition of the portfolio. Essentially, the first phase of this strategy includes the reward‒risk ratio maximization part and the following phase aims at systematic risk minimization. Furthermore, we approximate the returns using a dynamic set of components obtained from the principal component analysis and the classical ordinary least squares regression. In the empirical analysis using US market data, the wealth paths and statistics of different portfolio strategies are compared with each other. Ex-post results confirm higher profitability of the early warning system with double optimization, even if the transaction costs are taken into account. However, the main benefit lies in the significantly better risk properties of the proposed strategy.en
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.relation.isbasedonACHARYA, V. V., L. H. PEDERSEN, T. PHILIPPON and M. RICHARDSON. (2017). Measuring systemic risk. The Review of Financial Studies, 2017, 30(1): 2–47. https://doi.org/10.26509/frbc-wp-201002
dc.relation.isbasedonADRIAN, T. and M. K. BRUNNERMEIER. (2016). CoVaR. American Economic Review, 2016, 106(7): 1705–1741. https://doi.org/10.3386/w17454
dc.relation.isbasedonAHN, K., D. LEE, S. SOHN, and B. YANG. (2019). Stock market uncertainty and economic fundamentals: an entropy-based approach. Quantitative Finance, 2019, 19(7): 1151–1163. https://doi.org/10.1080/14697688.2019.1579922
dc.relation.isbasedonBILLIO, M., R. CASARIN, M. COSTOLA, and A. PASQUALINI. (2016). An entropy-based early warning indicator for systemic risk. Journal of International Financial Markets, Institutions and Money, 2016, 45: 42-59. https://doi.org/10.2139/ssrn.2604754
dc.relation.isbasedonGRADOJEVIC, N. and M. CARIC. (2016). Predicting systemic risk with entropic indicators. Journal of Forecasting, 2016, 36(1): 16-25. https://doi.org/10.1002/for.2411
dc.relation.isbasedonKOUAISSAH, N., and A. HOCINE. (2021). Forecasting systemic risk in portfolio selection: The role of technical trading rules. Journal of Forecasting, 2021, 40(4): 708–729. https://doi.org/10.1002/for.2741
dc.relation.isbasedonMARKOWITZ, H. M. (1952). Portfolio selection. Journal of Finance, 1952, 7(1): 77–91. https://doi.org/10.12987/9780300191677
dc.relation.isbasedonMERCURIO, P. J., Y. WU, and H. XIE. (2020). An entropy-based approach to portfolio optimization. Entropy, 2020, 22(3): 332. https://doi.org/10.3390/e22030332
dc.relation.isbasedonNEDĚLA, D. (2022). Systemic Risk Prediction Using Entropy Rule in Double Portfolio Selection Strategy: Evidence on US Stock Market. In Proceedings of 13th International Scientific Conference Karviná Ph.D. Conference on Business and Economics: Horní Lomná: Silesian University in Opava, 2022. pp. 63–73.
dc.relation.isbasedonORTOBELLI, S., F. PETRONIO and T. LANDO. (2017). A portfolio return definition coherent with the investors' preferences. IMA Journal of Management Mathematics, 2017, 28(3): 451–466. https://doi.org/10.1093/imaman/dpv029
dc.relation.isbasedonORTOBELLI, S. and T. TICHÝ (2015). On the impact of semidefinite positive correlation measures in portfolio theory. Annals of Operations Research, 2015, 235(1): 625–652. https://doi.org/10.1007/s10479-015-1962-x
dc.relation.isbasedonPOLA, G. (2016). On entropy and portfolio diversification. Journal of Asset Management, 2016, 17(4): 218–228. https://doi.org/10.1057/jam.2016.10
dc.relation.isbasedonPOST, T. and V. POTÌ. (2017). Portfolio analysis using stochastic dominance, relative entropy, and empirical likelihood. Management Science, 2017, 63(1): 153–165. https://doi.org/10.1287/mnsc.2015.2325
dc.relation.isbasedonRACHEV, S. T., S. V. STOYANOV and F. J. FABOZZI. (2008). Advanced stochastic models, risk assessment and portfolio optimization: The ideal risk, uncertainty and performance measures. New York: Wiley Finance.
dc.relation.isbasedonRUTTIENS, A. (2013). Portfolio risk measures: The time’s arrow matters. Computational Economics, 2013, 41: 407–424. https://doi.org/10.1007/s10614-012-9336-9
dc.relation.isbasedonSHANNON, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 1948, 27(3): 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x
dc.relation.isbasedonSHARPE, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 1994, 21(1): 49-58. https://doi.org/10.3905/jpm.1994.409501
dc.relation.isbasedonTORRI, G., D. RADI, and H. DVOŘÁČKOVÁ. (2022). Catastrophic and systemic risk in the non-life insurance sector: A micro-structural contagion approach. Finance Research Letters, 2022, 47, https://doi.org/10.1016/j.frl.2022.102718
dc.relation.isbasedonTSALLIS, C. (1988). Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics, 1988, 52(1): 479–487. https://doi.org/10.1007/bf01016429
dc.relation.ispartofLiberecké ekonomické fórum 2023cs
dc.relation.ispartofLiberec Economic Forum 2023en
dc.subjectearly warning systemen
dc.subjectsystemic risken
dc.subjectportfolio optimizationen
dc.titleDual Focus on Systemic Risk in Portfolio Managementen
dc.typeproceeding paperen
local.facultyFaculty of Economics
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
829.1 KB
Adobe Portable Document Format
conference paper