Endogenous Credit Spreads and Optimal Debt Financing Structure in the Presence of Liquidity Risk

Lütkebohmert, Eva; Oeltz, Daniel; Xiao, Yajun

doi:10.1111/eufm.12089

Cited by 5 publications

(1 citation statement)

References 53 publications

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“…The findings of the study have answered the main research question and proved the hypothesis that there is a non-monotonic relationship between the credit-rating scales and debt maturity choices of Asian firms. The evidence is also supported by Diamond's (1991) theoretical framework and is consistent with U.S.-based studies (Barclay and Smith 1995;Mark Hoven Stohs 2002) and U.K. based studies (Naeem 2012), Moreover, the importance of credit ratings in the financial structure is also supported by previous studies (Bedendo and Siming 2016;Daniels et al 2010;Gul and Goodwin 2010;Lütkebohmert et al 2017;Naeem 2012). The study also reveals that credit rating has a substantial effect on the optimization of debt maturity structure of Asian companies.…”

Section: Discussionsupporting

confidence: 86%

Credit Ratings and Liquidity Risk for the Optimization of Debt Maturity Structure

Sajjad

Zakaria

2018

JRFM

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The purpose of this study is to examine the relationship between credit rating scales and debt maturity choices. A liquidity hypothesis is used to formulate the testable proposition and conceptual framework. Generalized linear model (GLM) and pooled ordinary least square (OLS) are utilized by SAS programming to test the proposed hypothesis. Other different estimation techniques are also used for robust evidence. Results suggest that companies with high and low ratings have a shorter debt maturity. Companies with medium ratings have longer debt maturity structure. Liquidity shows a negative association with longer debt maturity structure. It is evident that at high rating scale with high liquidity, and at lower rating scales with lower liquidity firms have a shorter debt maturity. Mid rated firms with a low probability of refinancing risk show longer debt maturity structure. Considering refinancing risk by Asian companies make the nonlinear relationship between credit ratings and debt maturity choices. Results suggest the importance of credit ratings for the optimization of debt maturity structure of Asian firms, which was totally overlooked by the past studies. The findings of this study are consistent with the liquidity hypothesis. The findings also motivating financial managers and investors to consider credit ratings as a measure of financial constraints.

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Section: Discussionsupporting

confidence: 86%

Credit Ratings and Liquidity Risk for the Optimization of Debt Maturity Structure

Sajjad

Zakaria

2018

JRFM

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Rollover risk and credit risk under time-varying margin

Lütkebohmert

Xiao

2016

Quantitative Finance

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For a firm financed by a mixture of collateralized (short-term) debt and uncollateralized (long-term) debt, we show that fluctuations in margin requirements, reflecting funding liquidity shocks, lead to increasing firm's default risk and credit spreads. The severity with which a firm is hit by increasing margin requirements highly depends on both its financing structure and debt maturity structure. Our results imply that an additional premium should be added when evaluating debt in order to account for rollover risks especially for short-matured bonds. In terms of policy implications our results strongly indicate that regulators should intervene fast to curtail margins in crisis periods and maintain a reasonably low margin level in order to effectively prevent creditors' run on debt.JEL Codes: G10, G32, G33

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Financial Default Risk Prediction Algorithm Based on Neural Network under the Background of Big Data

Xie¹,

Zhang²

2022

Mobile Information Systems

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With the macroeconomy entering a new normal, many new problems are exposed in all walks of life, and the risk of default in the financial sector is also being exposed at an accelerated pace. In the context of big data, internet finance, as an important part of the financial market, also faces many risks in the process of its rapid development. Reasonable, scientific, and effective prediction and prevention of financial default risk have become a key link in the process of risk management practice in the financial industry. Based on the powerful prediction function of the neural network, this paper combined neural network and chaos theory to construct a chaotic RBF neural network. It was applied to financial default risk prediction, which made the prediction accuracy and efficiency higher. The chaotic neural network solves the shortcomings of unstable prediction in the basic neural network and can comprehensively and accurately predict the financial default risk, so as to take measures to prevent risks. The experimental results of this paper show that the accuracy rate of the chaotic RBF neural network reaches 95%, while the accuracy rates of the BP neural network and the RBF neural network are 67% and 78%, respectively. Although the prediction accuracy of these two methods is also high, it is still not as high as the chaotic RBF neural network. Therefore, it is very meaningful to choose the chaotic RBF neural network to predict financial default risk in this paper.

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Endogenous Credit Spreads and Optimal Debt Financing Structure in the Presence of Liquidity Risk

Cited by 5 publications

References 53 publications

Credit Ratings and Liquidity Risk for the Optimization of Debt Maturity Structure

Credit Ratings and Liquidity Risk for the Optimization of Debt Maturity Structure

Rollover risk and credit risk under time-varying margin

Financial Default Risk Prediction Algorithm Based on Neural Network under the Background of Big Data

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