Abstract
The measurement of financial risk has been one of the main goals of the investors as well as actuaries and insurance practitioners. Measuring the risk of a financial portfolio involves firstly estimating the loss distribution of the portfolio, next computing chosen risk measure. In the resent study, the robustness of risk measurement procedures and their sensitivity into point out for the dataset in present. The results show a gap between the subadditivity and robustness of risk measurement procedures. We apply into analyses alternative risk measurement procedures that possess the robustness property. The quantile-based risk measures have been applied in sector portfolio analysis for the dataset from Warsaw Stock Exchange.
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Notes
- 1.
D ⊂ L is the convex set of cumulative distribution functions (cdf) on R.
- 2.
Huber [12].
- 3.
If C = D, we have qualitative robustness called asymptotic robustness as outlined [12].
- 4.
In other words, while ρ is the risk measure, we are interested in computing.
- 5.
For any 0 < α1 < α2 < 1, we can notice as β = α2 − α1.
- 6.
- 7.
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Trzpiot, G. (2019). Application Quantile-Based Risk Measures in Sector Portfolio Analysis—Warsaw Stock Exchange Approach. In: Tarczyński, W., Nermend, K. (eds) Effective Investments on Capital Markets. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-21274-2_15
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