A Comparison of Conditional and Unconditional Approaches in Value-at-Risk Estimation

In this paper, we attempt to find the most important factor causing the differences in the performance of Value-at-Risk (VaR) estimation by comparing the performances of conditional and unconditional approaches. For each approach, we use various methods and models with different degrees of flexibility in their distributions including SU-normal distribution, which is one of the most flexible distribution functions. Our empirical results underscore the importance of the flexibility-of-distribution function in VaR estimation models. Even though it seems to be unclear which approach is better between conditional and unconditional approaches, it seems to be clear that the more flexible distribution we use, the better the performance, regardless of which approach we use.