Managing inventory financing in a volatile market: A novel data-driven copula model

Abstract

Inventory financing is crucial to helping businesses survive during an economic crisis—such as that caused by the COVID-19 pandemic—and supporting economic recovery in the aftermath. However, inventory financing is particularly risky in a volatile market environment since fluctuating collateral prices increase default risk. This paper presents an innovative data-driven copula model that was combined with the European call option theory to set loan-to-value ratios and determine interest rates that can help improve inventory finance providers’ (IFPs) competitiveness and manage their risk in volatile market environments. Specifically, the loan-to-value ratio estimated by the data-driven GARCH-EVT-Copula can help IFPs extract more value from collateral units due to its ability to capture both the autocorrelations among collateral prices and the dependencies among prices in different collateral units. The interest rate determination model derived from the European call option theory is employed to derive interest rates and reveal the risks of collateral units. The generated loan-to-value ratio and interest rate can serve as a reference for IFPs when preparing inventory financing contracts. Furthermore, the copula parameterization process can help IFPs identify the least risky and most predictable collateral unit. Finally, an extended analysis over the COVID-19 period demonstrates that the proposed approach can offer superior performance in a highly volatile market environment.