scholarly journals Shipping Bunker Cost Risk Assessment and Management during the Coronavirus Oil Shock

2021 ◽  
Vol 13 (9) ◽  
pp. 4998
Author(s):  
Tzeu-Chen Han ◽  
Chih-Min Wang
Keyword(s):  
Risk Management ◽  
Financial Management ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Sustainable Supply Chain ◽  
Fuel Cost ◽  
Fuel Price ◽  
Marine Fuel ◽  
Oil Shock ◽  
High Volatility

This research explores ways to develop a risk management strategy that enables shipping companies to reduce unnecessary fuel cost risks, fuel price fluctuations and improve financial management. Through the Monte Carlo method, the study makes use of the simulation of the conditional value-at-risk (CVaR) model. First, the VaR of various shipping-fuel-cost combination over a ten-year period is simulated. Then, through the most appropriate probability distribution test, it is found that most of the VaR of shipping fuel cost combination are in Beta–Arcsine distribution. In other words, the high-frequency data are concentrated at both tails (minimum and maximum) with high volatility. Therefore, the best strategy is to install scrubbers on existing ships to purify their exhaust gas and choose natural gas-based marine fuel for new ships. This will benefit the shipping companies significantly more compared to the use of low-sulfur fuel and choosing forward bunker agreements. Bunker swaps and options of bunker prices to hedging the risk of bunker cost raised in the end of Coronavirus oil shock, the strategy could help achieve the goal of risk management in the sustainable supply chain.

scholarly journals Two-Stage Robust Optimization Model for Uncertainty Investment Portfolio Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Dongqing Luan ◽  
Chuming Wang ◽  
Zhong Wu ◽  
Zhijie Xia
Keyword(s):  
Robust Optimization ◽  
Financial Management ◽  
Value At Risk ◽  
Optimization Theory ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Equal Weight ◽  
Investment Portfolio ◽  
Two Stage ◽  
Mip Model

Investment portfolio can provide investors with a more robust financial management plan, but the uncertainty of its parameters is a key factor affecting performance. This paper conducts research on investment portfolios and constructs a two-stage mixed integer programming (TS-MIP) model, which comprehensively considers the five dimensions of profit, diversity, skewness, information entropy, and conditional value at risk. But the deterministic TS-MIP model cannot cope with the uncertainty. Therefore, this paper constructs a two-stage robust optimization (TS-RO) model by introducing robust optimization theory. In case experiments, data crawler technology is used to obtain actual data from real websites, and a variety of methods are used to verify the effectiveness of the proposed model in dealing with uncertainty. The comparison of models found that, compared with the traditional equal weight model, the investment benefits of the TS-MIP model and the TS-RO model proposed have been improved. Among them, the Sharpe ratio, Sortino ratio, and Treynor ratio have the largest increase of 19.30%, 8.25%, and 7.34%, respectively.

scholarly journals Risk assessment for ancillary services

2019 ◽  
Vol 9 (3) ◽  
pp. 1561
Author(s):  
Omer Hadzic ◽  
Smajo Bisanovic
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Power System ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Ancillary Services ◽  
Conditional Value At Risk ◽  
Risk Optimization ◽  
System Risk

The power trading and ancillary services provision comprise technical and financial risks and therefore require a structured risk management. Focus in this paper is on financial risk management that is important for the system operator faces when providing and using ancillary services for balancing of power system. Risk on ancillary services portfolio is modeled through value at risk and conditional value at risk measures. The application of these risk measures in power system is given in detail to show how to using the risk concept in practice. Conditional value at risk optimization is analysed in the context of portfolio selection and how to apply this optimization for hedging a portfolio consisting of different types of ancillary services.

scholarly journals On the Statistical GARCH Model for Managing the Risk by Employing a Fat-Tailed Distribution in Finance

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1698
Author(s):  
H. Viet Long ◽  
H. Bin Jebreen ◽  
I. Dassios ◽  
D. Baleanu
Keyword(s):  
Risk Management ◽  
Value At Risk ◽  
Risk Measures ◽  
Garch Model ◽  
Extreme Value Distribution ◽  
Conditional Value At Risk ◽  
Conditional Heteroskedasticity ◽  
Explicit Formulas ◽  
Simulation Results ◽  
Daily Returns

The Conditional Value-at-Risk (CVaR) is a coherent measure that evaluates the risk for different investing scenarios. On the other hand, since the extreme value distribution has been revealed to furnish better financial and economical data adjustment in contrast to the well-known normal distribution, we here employ this distribution in investigating explicit formulas for the two common risk measures, i.e., VaR and CVaR, to have better tools in risk management. The formulas are then employed under the generalized autoregressive conditional heteroskedasticity (GARCH) model for risk management as our main contribution. To confirm the theoretical discussions of this work, the daily returns of several stocks are considered and worked out. The simulation results uphold the superiority of our findings.

scholarly journals Strategic Wholesale Pricing for an Incumbent Supplier Facing with a Competitive Counterpart

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Jianwu Sun
Keyword(s):  
Risk Management ◽  
Value At Risk ◽  
Financial Risk ◽  
Wholesale Price ◽  
Pricing Strategy ◽  
Conditional Value At Risk ◽  
Profit Function ◽  
Financial Risk Management ◽  
Future Loss ◽  
Wholesale Pricing

We introduce a wholesale pricing strategy for an incumbent supplier facing with a competitive counterpart. We propose a profit function which considers both the present loss and future loss from a wholesale price and then study the optimal wholesale prices for different objectives about this profit function for the incumbent supplier. First, we achieve an optimal wholesale price for the incumbent supplier to maximize his expected profit. Then, to reduce the risk originating from the fluctuation in the competitive supplier’s wholesale price, we integrate the conditional value-at-risk (CVaR) measure in financial risk management into this study and derive an optimal wholesale price to maximize CVaR about profit for the incumbent supplier. Besides, the properties of the two optimal wholesale prices are discussed. Finally, some management insights are suggested for the incumbent supplier in a competitive setting.

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