Price Risk Measurement Model of Pledge Financing of Lending Institution in Natural Rubber Supply Chain Based on VaR-GARCH Method

LISS2019 ◽  
2020 ◽  
pp. 395-407
Author(s):  
Xuezhong Chen ◽  
Yang Liu ◽  
Anran Chen
Keyword(s):  
Supply Chain ◽  
Natural Rubber ◽  
Measurement Model ◽  
Risk Measurement ◽  
Price Risk ◽  
Lending Institution

scholarly journals RESEARCH ON RISK MEASUREMENT OF SUPPLY CHAIN FINANCE BASED ON FRACTAL THEORY

Fractals ◽  
2020 ◽  
Vol 28 (08) ◽  
pp. 2040013
Author(s):  
XUN LIU ◽  
XIA PENG ◽  
MARTIN STUART
Keyword(s):  
Supply Chain ◽  
Financial Risk ◽  
Fractal Theory ◽  
Measurement Model ◽  
Risk Measurement ◽  
Random Structure ◽  
Supply Chain Finance ◽  
Volatility Model ◽  
Study Results ◽  
Capital Services

Supply chain finance is a new financing model tailored for small and medium-sized enterprises, which integrates capital flow into supply chain management, providing commercial trade capital services for enterprises in all aspects of the supply chain and providing new loan financing services for vulnerable enterprises in the supply chain. Fractal originally is a general term for a graph, structure or phenomenon that does not have a feature length but has a statistically significant self-similarity; fractal theory is an emerging edge science that describes the complex system with a random structure and has been widely used in physics, chemistry, geography, economics and many other fields. On the basis of summarizing and analyzing previous published literature works, this paper expounded the research situation and significance of risk measurement in supply chain finance, elaborated the development background, current status and future challenges of fractal theory, proposed the improved fractal volatility model and financial evaluation model, performed risk analysis of supply chain finance through evaluation modeling and elastic fractal dimension, constructed a financial risk measurement model based on fractal theory, and discussed the importance of model parameter estimation, residual test and accuracy examination in risk measurement of supply chain finance. The final empirical analysis shows that the improved fractal volatility model and the proposed financial risk measurement model has better risk measurement ability under different out-of-sample prediction periods, and obtain more accurate conclusion of asymmetry determination of financial assets gains under the common inspection level. The study results of this paper provide a reference for the further researches on risk measurement of supply chain finance based on fractal theory.

scholarly journals Research on Risk Measurement of Supply Chain Emergencies in International Capacity Cooperation

2019 ◽  
Vol 11 (19) ◽  
pp. 5184
Author(s):  
Bo-Rui Yan ◽  
Qian-Li Dong ◽  
Qian Li
Keyword(s):  
Supply Chain ◽  
Financial Risk ◽  
Fuzzy Ahp ◽  
Risk Index ◽  
Risk Measurement ◽  
Country Risk ◽  
Analytic Hierarchy ◽  
Analytic Hierarchy Process Method ◽  
Measurement Index System ◽  
Hierarchy Process

International capacity cooperation is easily affected by the interweaving of its internal and external environment. As the risk accumulation exceeds the threshold, a supply chain crisis and even emergency will occur and serious losses will be caused. Regarding multinational operation and international capacity cooperation, 208 cases were summarized to identify risk types and high-incidence areas, and a risk measurement index system was established. A Fuzzy AHP (Analytic Hierarchy Process) method was used to evaluate the importance of each risk index. It was found that country risk was the main cause of supply chain emergencies in international capacity cooperation. Construction, water and electricity supply, mining and manufacturing were major areas of emergencies. In international capacity cooperation, country risk and cross-cultural risk were more important in external risks, while in internal risk, financial risk and decision risk were more important.

scholarly journals Crude oil futures to manage the price risk of natural rubber: Empirical evidence from India

2021 ◽  
Vol 67 (No. 10) ◽  
pp. 423-434
Author(s):  
Kepulaje Abhaya Kumar ◽  
Prakash Pinto ◽  
Iqbal Thonse Hawaldar ◽  
Cristi Spulbar ◽  
Ramona Birau
Keyword(s):  
Natural Rubber ◽  
Crude Oil ◽  
Pearson Correlation ◽  
Structural Var ◽  
Price Risk ◽  
Causality Test ◽  
Oil Futures ◽  
Indian Rupee ◽  
Crude Oil Futures ◽  
Exchange Rate Shocks

The trading of natural rubber derivatives in the Indian commodity exchanges was banned several times in the past. Hence, in India, the derivatives on natural rubber are not traded actively and regularly. We have examined the possibility of a forecast model and a cross hedge tool for the natural rubber price by using crude oil futures in India. Results of the Johansen cointegration test proved that there is no cointegration equation in the model; hence, there is no scope to develop long-run models or error correction models. We have developed a vector autoregressive [VAR(2)] model to forecast the rubber price, and we examined the possibility of a cross hedge for natural rubber further by using the Pearson correlation coefficient and Granger causality test. We have extended our research to a structural VAR analysis to examine the effect of crude futures and exchange rate shocks on the natural rubber price. Our results showed that there is a short-term relationship between the crude oil futures price, the exchange rates of the US dollar to the Indian rupee, the Malaysian ringgit to the Indian rupee and the Thai baht to the Indian rupee; and the natural rubber price in India. The effort of policymakers to cause the Indian rupee to appreciate against the Thai baht and Malaysian ringgit may increase the natural rubber price in India. Natural rubber traders, growers and consumers can use crude futures to hedge the price risk. The Indian Rubber Board can suggest the VAR(2) model to predict the short-run price for natural rubber.

Managing Commodity Price Risk: A Supply Chain Perspective

2011 ◽  
Author(s):  
George Zsidisin ◽  
Janet Hartley
Keyword(s):  
Supply Chain ◽  
Commodity Price ◽  
Price Risk

scholarly journals Measurement Model for Supply Chain Performance of Shallot

2019 ◽  
Vol 334 ◽  
pp. 012069
Author(s):  
D E Pratiwi ◽  
B Setiawan ◽  
C Puspo ◽  
Nugroho ◽  
A E Hardana
Keyword(s):  
Supply Chain ◽  
Measurement Model ◽  
Supply Chain Performance

scholarly journals Research on measurement of supply chain finance credit risk based on Internet of Things

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987400 ◽  
Author(s):  
Waseem Ahmed Abbasi ◽  
Zongrun Wang ◽  
Yanju Zhou ◽  
Shahzad Hassan
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Credit Risk ◽  
Measurement Model ◽  
The Internet ◽  
Financial Model ◽  
Model Based ◽  
Supply Chain Finance ◽  
Credit Risk Measurement ◽  
The Internet Of Things

This article first expounds the concept of supply chain finance and its credit risk, describes the hierarchical structure of the Internet of Things and its key technologies, and combines the unique functions of the Internet of Things technology and the business process of the inventory pledge financing model to design the supply chain financial model based on the Internet of Things. Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. Finally, an example analysis shows that the credit risk measurement model has a high accuracy rate for determining whether small and medium-sized enterprises in the supply chain financial model based on the Internet of Things are trustworthy. This will facilitate the revision and improvement of the existing credit evaluation system and improve the accuracy of measuring the current financial risk of supply chain. This research adopts the Internet of Things to measure financial credit risk in supply chain and provides a reference for the following researches.

Spatial intelligent decision support system for increasing productivity on natural rubber agroindustry by green productivity approach

2017 ◽  
Vol 66 (6) ◽  
pp. 742-765 ◽  
Author(s):  
Yuliana Kaneu Teniwut ◽  
Marimin Marimin ◽  
Nastiti Siswi Indrasti
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Natural Rubber ◽  
Support System ◽  
Measurement Model ◽  
Raw Material ◽  
Rubber Plantation ◽  
Content Type ◽  
Green Productivity ◽  
Decision Alternatives

Purpose The purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP) approach. The SIDSS was used to measure the productivity of rubber plantation and rubber agroindustry by GP approach, and select the best strategies for increasing the productivity of rubber agroindustry. Design/methodology/approach This system was developed by combining spatial analysis, GP, and fuzzy analytic network process (ANP) with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry. Rubber plantation productivity measurement model was used to find the productivity level of rubber plantation with fuzzy logic, and also to provide information and decision alternatives to all stakeholders regarding spatial condition of rubber agroindustry, production process flow, and analysis of the seven green wastes at each production process flow using the geographic information system. GP measurement model was used to determine the productivity performance of the rubber agroindustry with the green productivity index (GPI). The best strategy for increasing the productivity was determined with fuzzy ANP. Findings Rubber plantation measurement model showed that the average of plantation productivity was 6.25 kg/ha/day. GP measurement model showed that the GPI value of ribbed smoked sheet (RSS) was 0.730, whereas of crumb rubber (CR) was 0.126. The best strategy for increasing the productivity of rubber agroindustry was raw material characteristics control. Based on the best strategy, the GPI value of RSS was 1.340, whereas of CR was 0.228. Research limitations/implications This decision support system is still limited as it is based on static data; it needs further development so that it can be more dynamically based on developments in the rubber agroindustry related levels of productivity and environmental impact. In addition, details regarding the decision to increase the productivity of the rubber section by benchmarking efforts should be studied further, both among plantation as well as among countries such as Thailand so that the productivity of rubber plantation and agroindustry can be integrated. Practical implications This research can help the planters to select superior clones for rubber trees, to improve the technique of tapping latex, and to use a better coagulant. The good quality and quantity of raw material is a key factor in increasing the productivity of rubber agroindustry; if the quality of latex is good then the resulting product will also have a good quality and production cost can be reduced. In addition, the application of GP through the calculation of GPI value using improvement scenarios can be used as a reference and comparison for evaluating the performance of rubber agroindustry to reduce the waste generated by the activities of rubber processing plant. Social implications Reduction of waste generated by production activities can improve the quality of life of the workforce and the environment. The calculation of GPI value can also be used as a basis to use raw materials, water, and electricity more efficiently. Originality/value This system was developed by combining spatial analysis, GP, and fuzzy ANP with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry.

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