Do coherent risk measures identify assets risk profiles similarly? Evidence from international futures markets
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Author(s)Sharif Mozumder , M. Humayun Kabir ,Michael DempseyLink to ORCID Index: https://orcid.org/0000-0002-9059-0416
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DOIhttp://dx.doi.org/10.21511/imfi.14(3-2).2017.07
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Article InfoVolume 14 2017, Issue #3, pp. 361-380
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The authors consider Lévy processes with conditional distributions belonging to a generalized hyperbolic family and compare and contrast full density-based Lévy-expected shortfall (ES) risk measures and Lévy-spectral risk measures (SRM) with those of a traditional tail-based unconditional extreme value (EV) approach. Using the futures data of leading markets the authors find that ES and SRM often differ in recognizing the risk profiles of different assets. While EV (extreme value) is often found to be more consistent than Lévy models, Lévy measures often perform better than EV measures when compared with empirical values. This becomes increasingly apparent as investors become more risk averse.
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JEL Classification (Paper profile tab)C52, G13
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References38
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Tables5
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Figures5
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- Figure 1. Conditional and unconditional quantiles in excess of threshold (2): long position in S&P 500
- Figure 2. Conditional and unconditional quantiles in excess of threshold (1.5): long position in FTSE100
- Figure 3. Conditional and unconditional quantiles in excess of threshold (2): long position in DAX
- Figure 4. Conditional and unconditional quantiles in excess of threshold (2): long position in Hang Seng
- Figure 5. Conditional and unconditional quantiles in excess of threshold (2): long position in Nikkei 225
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- Table 1. Generalized Pareto distribution (GDP) parameter estimates for futures indexes
- Table 2. Conditional maximum likelihood estimates for futures indexes
- Table 3. Anderson-Darling and left-truncated Anderson-Darling goodness-of-fit tests
- Table 4. Estimates of ES and Lévy spectral risk measures for futures position
- Table 5. Frequency distribution of significant estimates of ES and Lévy spectral risk measures
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An evaluation and comparison of Value at Risk and Expected Shortfall
Tom Burdorf , Gary van Vuuren
doi: http://dx.doi.org/10.21511/imfi.15(4).2018.02
Investment Management and Financial Innovations Volume 15, 2018 Issue #4 pp. 17-34 Views: 1250 Downloads: 258 TO CITE АНОТАЦІЯAs a risk measure, Value at Risk (VaR) is neither sub-additive nor coherent. These drawbacks have coerced regulatory authorities to introduce and mandate Expected Shortfall (ES) as a mainstream regulatory risk management metric. VaR is, however, still needed to estimate the tail conditional expectation (the ES): the average of losses that are greater than the VaR at a significance level These two risk measures behave quite differently during growth and recession periods in developed and emerging economies. Using equity portfolios assembled from securities of the banking and retail sectors in the UK and South Africa, historical, variance-covariance and Monte Carlo approaches are used to determine VaR (and hence ES). The results are back-tested and compared, and normality assumptions are tested. Key findings are that the results of the variance covariance and the Monte Carlo approach are more consistent in all environments in comparison to the historical outcomes regardless of the equity portfolio regarded. The industries and periods analysed influenced the accuracy of the risk measures; the different economies did not.
