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.
Highlights
Due to global trades, economic and policy uncertainties, the world dry bulk shipping market is characterized as a high-risk and highly volatile market, which brings various risks and opportunities to market participants [1].Value-at-risk (VaR) is widely used by financial institutions and Banks as a standard tool for quantifying market risks [2]
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
This paper only shows the Panamax sector of shipping markets under 5% and 95% quantiles
Summary
Economic and policy uncertainties, the world dry bulk shipping market is characterized as a high-risk and highly volatile market, which brings various risks and opportunities to market participants [1].Value-at-risk (VaR) is widely used by financial institutions and Banks as a standard tool for quantifying market risks [2]. Chao [3] applied the VaR model to analyze the Normal, Student-t (ST) and Skewed Student-T (SST) distribution performance to assess the risk of dry bulk freight charges, and considered SST distributed asymmetric long memory volatility structure can obtain accurate. Most of the researches on risk forecasting of the shipping market are based on the selection and comparison of the volatility model, yet the modeling of the returns distributions is an important factor affecting the VaR estimation [4]. Theodossiou [6] proposed the Skewed Generalized Error Distribution (SGED), which has shown good performance in market risk prediction research. Aas [11] later extended the Generalized Hyperbolic Skew-Student (GHST) distribution, which has a polynomial in the GH family and a unique exponential behavior
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: American Journal of Industrial and Business Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
