Evaluation Of Multi-period Price Risks And Portfolio Optimization Of Multi-period Loan In Supply Chain Finance

Small businesses are the important part of the national economy,and play an important role in promoting economic growth and solving the employment problem.However,it is difficult for them to obtain financing from the formal financial institutions due to their small sizes and low credit.To effectively overcome the financing difficulties,the supply chain financial services conducted jointly by financial institutions and logistics enterprises arise at the historic moment.Although supply chain finance development space is vast,its risk is also increasing with its rapid development.Supply chain finance take the self-liquidation trade finance as the premise,and the value of the pledge will fluctuate uncertainly affected by the external environment factors,and seriously endanger the stable development of the supply chain finance.How to evaluate and prevent multi-period price risks of a single pledge,and optimize the multi-period loan portfolio of different pledges to avoid loan concentration risk have become key issues of financial institutions and logistics enterprises.The main contributions of this thesis includes two parts.(1)To evaluate and prevent multi-period price risks of a single pledge in supply chain finance,Firstly,we first propose a QRNN+GARCH method for evaluating multi-period VaR in supply chain finance as the quantile regression neural network(QRNN)can reveal the facts of asymmetry,nonlinearity,multiperiod and the GARCH model can depict the feature of volatility clustering.Secondly,the likelihood ratio test and the average relative error are given to evaluate the performance.Thirdly,the uncontrollable risk ratio and efficiency loss rate are given to evaluate the validity of impawn rate.Finally,we conduct an empirical research on the fluctuant price of the spot of aluminum,and get some conclusions.In terms of evaluating multi-period price risks,the QRNN+GARCH method is more accurate,efficient and robust than the GARCH model.In terms of preventing risk,the multi-period dynamic impawn rate determined by the QRNN+GARCH method can reduce the efficiency loss better.(2)To optimize the multi-period loan portfolio of different pledges in supply chain finance,we propose a Copula-quantile regression method by employing quantile regression to fit marginal distribution of a single asset and Copula function to capture nonlinear dependence structures among assets.Our method is flexible and adapt to describe stylized facts in supply chain finance,such as asymmetry and nonlinearity.We first apply the Copula-quantile regression method to predict the multi-period loan return rate.We then provide a decision-making scheme for the loan portfolio by minimizing the traditional Sharpe ratio and generalized Omega ratio.To illustrate the efficacy of our method,we conduct an empirical research on the spot of aluminum and copper which are the most common form of the pledge in supply chain finance.one fact can be drawn from the empirical results.The Copula-quantile regression method outperforms the Copula-GARCH model in that the former poses higher Sharpe ratio and generalized Omega ratio than the latter for all portfolios at different periods.In this thesis,it provides methods on evaluation of multi-period price risks and portfolio optimization of multi-period loan in supply chain finance and empirical results.These findings can provide a decision-making reference for the healthy development of supply chain finance,and conducive to achieve the win-win situation of the financial institutions,logistics enterprises,the core enterprises and small businesses.