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Cheu, Q., Gerlach, R. and Lu, Z. (2012) Bayesian Value-at-Risk and Expected Shortfall Forecasting via the Asymmetric Laplace Distribution. Computational Statistics and Data Analysis, 56, 3498-3516. https://doi.org/10.1016/j.csda.2010.06.018

has been cited by the following article:

• TITLE: Forecasting and Backtesting of VaR in International Dry Bulk Shipping Market under Skewed Distributions

  AUTHORS: Qiannan Du

  KEYWORDS: Dry Bulk Shipping Market, Value-at-Risk, Skewed Generalized Error Distribution, Backtesting

  JOURNAL NAME: American Journal of Industrial and Business Management, Vol.9 No.5, May 22, 2019

  ABSTRACT: It is extremely important to model the empirical distributions of dry bulk shipping returns accurately in estimating risk measures. Based on several commonly used distributions and alternative distributions, this paper establishes nine different risk models to forecast the Value-at-Risk (VaR) of dry bulk shipping markets. Several backtests are explored to compare the accuracy of VaR forecasting. The empirical results indicate the risk models based on commonly used distributions have relatively poor performance, while the alternative distributions, i.e. Skewed Student-T (SST) distribution, Skewed Generalized Error Distribution (SGED), and Hyperbolic distribution (HYP) produce more accurate VaR measurement. The empirical results suggest risk managers further consider more flexible empirical distributions when managing extreme risks in dry bulk shipping markets.