Supply Chain Finance Models and Applications

: Copula-based Approaches

Abstract

Supply chain finance (SCF) seeks to integrate physical, financial, and information flows along the supply chain. Among SCF practices, inventory financing has played a vital role in helping many capital-constrained businesses survive the COVID-19 crisis through alternative financing schemes and supporting the post-COVID economic recovery. However, inventory financing is particularly risky in a volatile market environment, as fluctuating collateral prices increase the default risk. A proper impawn rate (loan-to-value ratio), interest rate, and collateral portfolio can help an inventory financing provider (IFP) effectively manage the risk from inventory financing and be competitive in the financial market. However, prior studies seldom explore how an IFP improves the performance of inventory financing by managing the impawn, interest rates, and collateral portfolio. This doctoral research explores the use of data-driven copula models to determine these three factors. The analytical results show that a copula model can depict the dependence among a series of collateral prices, and its forecasting performance is ideal for determining impawn, interest rates, and the weights of a collateral portfolio. This research contributes to the SCF literature by presenting an innovative data-driven approach to manage risk in inventory financing. Integrating a predictive analytical approach (i.e., data-driven copula model to predict risk) and a prescriptive analytical model (i.e., impawn and interest rate model) to address a contemporary issue such as determining the appropriate impawn rates and interest rates for inventory financing contributes to the emerging business analytics field. This doctoral research presents innovative approaches that can be used by IFPs to control the default risk inherent in inventory financing and gain competitive advantage in financial markets.

Date of Award	2 Dec 2021
Original language	English
Awarding Institution	University of Bristol
Supervisor	Xiaojun Wang (Supervisor) & Fangming Xu (Supervisor)