The inverse 1-median location problem on uncertain tree networks with tail value at risk criterion

Soltanpour, Akram; Baroughi, Fahimeh; Alizadeh, Behrooz

doi:10.1016/j.ins.2019.08.018

Cited by 10 publications

(1 citation statement)

References 23 publications

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“…In addition, widening the scenario of uncertainty modelling, the tail VaR metric on a tree network, as shown in [36], computes the probability of the loss and its severity in an uncertain environment.…”

Section: Paths For Future Researchmentioning

confidence: 99%

Value at Risk Based on Fuzzy Numbers

Guerra

Sorini

2020

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Value at Risk (VaR) has become a crucial measure for decision making in risk management over the last thirty years and many estimation methodologies address the finding of the best performing measure at taking into account unremovable uncertainty of real financial markets. One possible and promising way to include uncertainty is to refer to the mathematics of fuzzy numbers and to its rigorous methodologies which offer flexible ways to read and to interpret properties of real data which may arise in many areas. The paper aims to show the effectiveness of two distinguished models to account for uncertainty in VaR computation; initially, following a non parametric approach, we apply the Fuzzy-transform approximation function to smooth data by capturing fundamental patterns before computing VaR. As a second model, we apply the Average Cumulative Function (ACF) to deduce the quantile function at point p as the potential loss VaRp for a fixed time horizon for the 100p% of the values. In both cases a comparison is conducted with respect to the identification of VaR through historical simulation: twelve years of daily S&P500 index returns are considered and a back testing procedure is applied to verify the number of bad VaR forecasting in each methodology. Despite the preliminary nature of the research, we point out that VaR estimation, when modelling uncertainty through fuzzy numbers, outperforms the traditional VaR in the sense that it is the closest to the right amount of capital to allocate in order to cover future losses in normal market conditions.

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Section: Paths For Future Researchmentioning

confidence: 99%