AJIBM  Vol.9 No.5 , May 2019
Forecasting and Backtesting of VaR in International Dry Bulk Shipping Market under Skewed Distributions
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.
Cite this paper: Du, Q. (2019) Forecasting and Backtesting of VaR in International Dry Bulk Shipping Market under Skewed Distributions. American Journal of Industrial and Business Management, 9, 1168-1186. doi: 10.4236/ajibm.2019.95079.

[1]   Alexandridis, G., Kavussanos, M.G. and Kim, C.Y. (2018) A Survey of Shipping Finance Research: Setting the Future Research Agenda. Transportation Research Part E: Logistics and Transportation Review, 115, 164-212.

[2]   Pérignon, C. and Smith, D. (2010) The Level and Quality of Value-at-Risk Disclosure by Commercial Banks. Journal of Banking & Finance, 34, 362-377.

[3]   Chang, C.C., Chou, H.C. and Wu, C.C. (2014) Value-at-Risk Analysis of the Asymmetric Long-Memory Volatility Process of Dry Bulk Freight Rates. Maritime Economics & Logistics, 16, 298-320.

[4]   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.

[5]   Pagan, A. (1996) The Econometrics of Financial Markets. Journal of Empirical Finance, 3, 15-102.

[6]   Theodossiou, P. (2001) Skewed Generalized Error Distribution of Financial Assets and Option Pricing. Working Paper, School of Business and Rutgers University, Piscataway.

[7]   Bollerslev, T. (1987) A Conditional Heteroskedastic Time Series Model for Security Prices and Rates of Return Data. The Review of Economics and Statistics, 69, 542-547.

[8]   Hansen, B.E. (1994) Autoregressive Conditional Density Estimation. International Economic Review, 35, 705-730.

[9]   Ferreira, J.T.A.S. and Steel, M.F.J. (2006) A Constructive Representation of Univariate Skewed Distributions. Journal of the American Statistical Association, 101, 823-829.

[10]   Barndorff-Nielsen, O. and Blæsild, P. (1983) Hyperbolic Distributions. In: Johnson, N.L., Kotz, S. and Read, C.B., Eds., Encyclopedia of Statistical Sciences, Vol. 3, Wiley Interscience, New York, 700-707.

[11]   Aas, K. and Haff, I.H. (2006) The Generalized Hyperbolic Skew Student’s t-Distribution. Journal of Financial Economics, 4, 275-309.

[12]   Escanciano, J.C. and Pei, P. (2012) Pitfalls in Backtesting Historical Simulation VaR Models. Journal of Banking & Finance, 36, 2233-2244.

[13]   Kupiec, P. (1995) Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3, 73-84.

[14]   Christofersen, P. (1998) Evaluating Intervals Forecasts. International Economic Review, 39, 841-862.

[15]   Engle, R.F. and Manganelli, S. (2004) CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business & Economic Statistics, 22, 367-381.

[16]   Dumitrescu, E., Hurlin, C. and Pham, V. (2012) Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests. Finance, 33, 79-112.

[17]   Glosten, L.R., Jagannathan, R. and Runkle, D.E. (1993) On the Relation between the Expected Value and the Volatility of Nominal Excess Return on Stocks. Journal of Finance, 48, 1779-1801.

[18]   Theodossiou, P. (2015) Skewed Generalized Error Distribution of Financial Assets and Option Pricing. Multinational Finance Journal, 19, 223-266.

[19]   Lin, C.H., Changchien, C.C., Kao, T.C. and Kao, W.S. (2014) High-Order Moments and Extreme Value Approach for Value-at-Risk. Journal of Empirical Finance, 29, 421-434.