The Risk Management Of Supply Chain Finance Predominated By Logistics Enterprise With Finanancial Attributes

Different from the logistics finance predominated by banks, this paper proposes an innovation of supply chain finance provided by logistics enterprise in the presence of financial property, including the developing sequence, concept, characteristics, comparative advantage and risk management. The main regularities are documented: first, the nature of supply chain finance predominated by logistics enterprise is the redistribution of bank credit via trade credit in the supply chain; second, as a micro-practice of structural reform of supply front, the emergence of financial attributes of logistics enterprise is in line with the process from the traditional functional firms upgrade to the platform and ecosystem dominated by logistics enterprise; there are three characteristics, including the integration of four flows around goods, information, financial and trade; Self-liquidation and Composability, and three attributes, i.e., trade finance, logistics finance and supply chain management in the supply chain finance provided by logistics enterprise. Last, the crucial of integerated risk management of supply chain finance provided by logistics enterprise depends on the transaction goods and cash flow derived. The optimizations of portfolio based on risk diversification and dynamic hedge ratio based on risk hedging are proposed.In order to mitigate concentration risk due to sharp fluctuations of price of single inventory in supply chain finance, Chapter 2 presents an application of Copula-GARCH model in the dynamic estimation of the inventory portfolios' VaR with rolling time window based on the principle of risk diversification of Markowitz's. The samples are composed by two different correlation portfolios: copper and rebar and copper and aluminum. This Copula-GARCH could better depict the characteristics of the autocorrelation,heteroskedasticity, leptokurtosis and fat-tails of the marginal distribution, but also the conditional dependence of the joint distribution. In addition, four groups of portfolios with different correlation are generated by the conditional volatility of the portfolio and Clayton-Copula function, which provides the impact different dependence structure on portfolios' VaR. At last, the back testing are performed which consist of Kupiec testing and the efficiency loss testing based on dynamic impawn rate considering fund cost from the perspective of long-term forecasting. The results show that Clayton-Copula could reasonably estimate the risk of portfolio and diversify risk markedly.In Chapter 3, we propose a mean-CVaR portfolio optimization framework for both conservative and aggressive investment strategies based on long-term risk prediction,which is different from financial assets such as stocks, bonds portfolio optimization framework based on the short-term risk prediction, so as to mitigate concentration risk due to sharp fluctuations of price of single inventory in supply chain finance. The long-term risk prediction based on Monte Carlo simulation of the inventory portfolio is proposed, and it is more practical than square root rule, which overcomes the shortcoming of the square root rule which heavily depends on the independent normal distribution. In methodology,AR(1)-EGARCH(1,1)-EVT model is set up to better depict the characteristics of the autocorrelation, heteroskedasticity, leptokurtosis and fat-tails of the marginal distribution,furthermore, the multivariate t-Copula function is introduced to model the dependency structure of individual pledged inventory. The empirical results show that, the mean-CVaR optimization framework outperforms the improved mean-variance from the perspective of long-term risk prediction, which are robust to the choice of risk window, confidence level,simulation times and sample size.Long memory in volatility, which attests a slower than exponential decay in the autocorrelation function of standard proxies of volatility, yields an additional improvement in specification of multi-period volatility models and further impact on the term structure of risk. Thus, long memory is indispensable to model and measure long-term risk. In Chapter 4, we shed new light on the impact of the existence and persistence of long memory in volatility on inventory portfolio optimization. Firstly, we investigate the existence of long memory in volatility of the inventory returns, and examine the impact of long memory on the modeling and forecasting of multi-period volatility, the dependence structure between inventory returns and portfolio optimization. Secondly, we further explore the impact of the persistence of long memory in volatility on the efficient frontier of inventory portfolio via a data generation process with different long memory parameter in the FIGARCH model.The extensive Monte Carlo evidence reveal that both GARCH and IGARCH models without accounting for long memory will misestimate the actual long-term risk of the inventory portfolio and further bias the efficient frontier; Besides, through a sensitive analysis of long memory parameter, it is proved that the portfolio with higher long memory parameter possesses higher expected return and lower risk level.As pro-cyclical industries, the prices of commodities are generally correlated with the state of the macro-economy, in particular, they take the biggest hit during periods of economic downturn, which will discount the diversification effect of inventory portfolio.For this end, Chapter 5 proposes the dynamic hedge framework in the presence of heterogeneous risk aversion of logistics enterprise, which is used to hedge the systematic tisk elements caused by the macroeconomic fundamentals. In methodology, we formulate two GARCH-MIDAS volatility models with secular component driven by realized volatility and Business Cycle Signal indices respectively, allowing us to better handle with the dilemma between the small sample problem of high frequency historical series and long-run forecasting. The results show that, first, the conditional volatility and long-run component for spot and futures exhibit counter-cyclicality; second, as a systematic risk factor, the volatility of macroeconomic state is a significant fraction of price variation of spot commodity, especially in the period of economic downturn; third, the robust loss functions, DMW and MCS test further indicate that the models pay off in various out-of-sample forecasting in terms of statistical and economic significance. Last, the logistic enterprises with high levels of risk aversion reduce the risk of the hedge ratio.These findings are of importance for Logistics enterprises and other participants of supply chain finance.