(untitled) (Communications in Mathematical Research, 2023)
Value-at-Risk (VaR) and expected shortfall (ES) are two key risk measures in financial risk management. Comparing these two measures has been a hot debate, and most discussions focus on risk measure properties. This paper uses independent data and autoregressive models with normal or t-distribution to examine the effect of the heavy tail and dependence on comparing the nonparametric inference uncertainty of these two risk measures. Theoretical and numerical analyses suggest that VaR at 99% level is better than ES at 97.5% level for distributions with heavier tails.