Credit Rating Dynamics in the Presence of Unknown Structural Breaks

2010 ◽  
Author(s):  
Haipeng Xing ◽  
Ning Sun ◽  
Ying Chen
Keyword(s):  
Structural Breaks ◽  
Credit Rating

scholarly journals What Affects the Relationships between Oil and Industrial Sector? Case of Eurozone

2017 ◽  
Vol 9 (9) ◽  
pp. 52
Author(s):  
Melik Kamisli ◽  
Serap Kamisli ◽  
Fatih Temizel ◽  
Ethem Esen
Keyword(s):  
Structural Breaks ◽  
Credit Rating ◽  
Debt Crisis ◽  
Strong Relationship ◽  
Industrial Sector ◽  
Oil Price ◽  
Production Costs ◽  
Cost Item ◽  
Economic Developments ◽  
Sector Returns

Oil, which is one of the fundamental energy sources, is an important cost item especially for industrial sector. Increases in oil prices decrease the profits of the firms by causing increase in the production costs. For this reason, it is claimed that there is a strong relationship between oil price and industrial sector profitability. On the other hand, oil is an alternative investment vehicle that can be included to the portfolio. Therefore, in this study the relationships between oil price and industrial sector returns of European countries are analyzed with Maki (2012) cointegration test under multiple structural breaks, on the basis of European Debt Crisis. The results show that announcements of credit rating agencies, elections, resignations, announcements of European Central Bank and IMF, recovery packages and economic developments cause structural breaks in relationships. Results also indicate that there is no cointegration between oil price and industrial sector returns of Austria, Belgium and Holland.

Credit rating dynamics in the presence of unknown structural breaks

2012 ◽  
Vol 36 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Haipeng Xing ◽  
Ning Sun ◽  
Ying Chen
Keyword(s):  
Structural Breaks ◽  
Credit Rating

scholarly journals Firm’s Credit Risk in the Presence of Market Structural Breaks

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 136 ◽  
Author(s):  
Haipeng Xing ◽  
Yang Yu
Keyword(s):  
Financial Crises ◽  
Structural Breaks ◽  
Credit Rating ◽  
Structural Break ◽  
Estimating Equation ◽  
Inference Procedure ◽  
Model Assumption ◽  
Macroeconomic Variables ◽  
Rating Transitions ◽  
The Impact

The financial crises which occurred in the last several decades have demonstrated the significant impact of market structural breaks on firms’ credit behavior. To incorporate the impact of market structural break into the analysis of firms’ credit rating transitions and firms’ asset structure, we develop a continuous-time modulated Markov model for firms’ credit rating transitions with unobserved market structural breaks. The model takes a semi-parametric multiplicative regression form, in which the effects of firms’ observable covariates and macroeconomic variables are represented parametrically and nonparametrically, respectively, and the frailty effects of unobserved firm-specific and market-wide variables are incorporated via the integration form of the model assumption. We further develop a mixtured-estimating-equation approach to make inference on the effect of market variations, baseline intensities of all firms’ credit rating transitions, and rating transition intensities for each individual firm. We then use the developed model and inference procedure to analyze the monthly credit rating of U.S. firms from January 1986 to December 2012, and study the effect of market structural breaks on firms’ credit rating transitions.

scholarly journals Firm's Credit Risk in the Presence of Market Structural Breaks

2018 ◽  
Author(s):  
Haipeng Xing ◽  
Yang Yu
Keyword(s):  
Financial Market ◽  
Continuous Time ◽  
Structural Breaks ◽  
Credit Rating ◽  
Estimating Equation ◽  
Inference Procedure ◽  
Model Assumption ◽  
Macroeconomic Variables ◽  
The Past ◽  
Rating Transitions

Various sudden shifts in financial market conditions over the past decades have demonstrated the significant impact of market structural breaks on firms' credit behavior. To characterize such effect quantitatively, we develop a continuous-time modulated Markov model for firms' credit rating transitions with the possibility of market structural breaks. The model takes a semi-parametric multiplicative regression form, in which the effects of firms' observable covariates and macroeconomic variables are represented parametrically and nonparametrically, respectively, and the frailty effects of unobserved firm-specific and market-wide variables are incorporated via the integration form of the model assumption. We further develop a mixtured-estimating-equation approach to make inference on the effect of market variations, baseline intensities of all firms' credit rating transitions, and rating transition intensities for each individual firm. We then use the developed model and inference procedure to analyze the monthly credit rating of U.S. firms from January 1986 to December 2012, and study the effect of market structural breaks on firms' credit rating transitions.

scholarly journals Statistical Surveillance of Structural Breaks in Credit Rating Dynamics

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1072
Author(s):  
Haipeng Xing ◽  
Ke Wang ◽  
Zhi Li ◽  
Ying Chen
Keyword(s):  
Likelihood Ratio ◽  
Global Economy ◽  
Structural Breaks ◽  
Credit Rating ◽  
Credit Market ◽  
Statistical Process ◽  
Transition Dynamics ◽  
Markovian Models ◽  
Ratio Rule ◽  
Detection Rule

The 2007–2008 financial crisis had severe consequences on the global economy and an intriguing question related to the crisis is whether structural breaks in the credit market can be detected. To address this issue, we chose firms’ credit rating transition dynamics as a proxy of the credit market and discuss how statistical process control tools can be used to surveil structural breaks in firms’ rating transition dynamics. After reviewing some commonly used Markovian models for firms’ rating transition dynamics, we present several surveillance rules for detecting changes in generators of firms’ rating migration matrices, including the likelihood ratio rule, the generalized likelihood ratio rule, the extended Shiryaev’s detection rule, and a Bayesian detection rule for piecewise homogeneous Markovian models. The effectiveness of these rules was analyzed on the basis of Monte Carlo simulations. We also provide a real example that used the surveillance rules to analyze and detect structural breaks in the monthly credit rating migration of U.S. firms from January 1986 to February 2017.

Role of Credit Rating in determining Capital structure: Evidence from Non-Financial sector of Pakistan

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Shoaib Ali ◽  
Imran Yousaf ◽  
Muhammad Naveed
Keyword(s):  
Capital Structure ◽  
Credit Rating ◽  
Least Square ◽  
Annual Data ◽  
Leverage Ratio ◽  
Financial Decisions ◽  
Financial Firms ◽  
High Level ◽  
The Impact ◽  
The Capital Structure

This paper aims to examine the impact of external credit ratings on the financial decisions of the firms in Pakistan.  This study uses the annual data of 70 non-financial firms for the period 2012-2018. It uses ordinary least square (OLS) to estimate the impact of credit rating on capital structure. The results show that rated firm has a high level of leverage. Moreover, Profitability and tanagability are also found to be a significantly negative determinant of the capital structure, whereas, size of the firm has a significant positive relationship with the capital structure of the firm.  Besides, there exists a non-linear relationship between the credit rating and the capital structure. The rated firms have higher leverage as compared to the non-rated firms. The high and low rated firms have a low level of leverage, while mid rated firms have a higher leverage ratio. The finding of the study have practical implications for the manager; they can have easier access to the financial market by just having a credit rating no matter high or low. Policymakers must stress upon the rating agencies to keep improving themselves as their rating severs as the measure to judge the creditworthiness of the firm by both the investors and management as well.

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