An Examination of the Determinants of Optimal Corporate Credit Hegde: Perspectives from Firms Listed in DJIA30 and Nasdaq100


Raising number of corporate defaults could threaten the financial stability, thus modelling credit risk is a key ingredient of financial stability analysis. This paper tries to answer a specific question regarding the drivers of this risk: idiosyncratic risk (industry-specific or firm-specific) and/or systematic risk (macro-economic specific). As a first step, the paper estimates the optimal hedge ratio based on the interest coverage ratio, that acts as a measure for the probability of default. Against this background, the paper applies one of the fundamental-based models of credit risk, known as hybrid model after estimating the proposed indicator variable. The suggested model incorporates a number of accounting-based variables (financial ratios: such as measures of profitability, liquidity, and leverage) and a set of macroeconomic variables (such as output growth rate and inflation rate). In doing so, the data sample covers listed firms from both Dow Jones and Nasdaq databases, covering the Q1: 1998 till Q3: 2020. The paper applied the general-to-specific and cross-sectional estimation approach, it concludes with two specific hybrid models for Nasdaq. As for Dow Jones, there was no chance to estimate a hybrid model by including macroeconomic variables, due to the fact that all industries are headquartered in US, which didn’t give a room for enough variability and inference. On another note, the hybrid models reveal that inflation has the largest magnitude of impact on the optimal credit hedging ratio, which can be attributed to the fact that higher inflation might constitute a higher risk, in which companies hedge against it by having higher hedging ratio. The opposite happened in case of the GDP growth rate, in other words, higher GDP is associated with lower hedging ratio which gives the market higher level of certainty and confidence.


School of Business


Management Department

Degree Name

MS in Finance

Graduation Date

Summer 6-15-2021

Submission Date


First Advisor

Mohammed Bouaddi

Committee Member 1

Neveen Ahmed

Committee Member 2

Mohamed Bassuony


29 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Approval has been obtained for this item

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