On Surrender and Default Risks

2010 ◽  
Author(s):  
Olivier Le Courtois ◽  
Hidetoshi Nakagawa
Keyword(s):  
Default Risks

Risk-Shifting or Risk-Hedging of Portfolio Choices Under Bond Default Risks

2012 ◽  
Author(s):  
Jin-ray Lu ◽  
Chih-Ming Chan ◽  
Yi-Long Hsiao ◽  
Kai-Ping Chen
Keyword(s):  
Risk Shifting ◽  
Risk Hedging ◽  
Portfolio Choices ◽  
Default Risks

scholarly journals The Impact of Banking Competition on Firm Credit Risk and Leverage

SAGE Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 215824402110615
Author(s):  
Chengxiao Feng ◽  
Zhubo Li ◽  
Zhen Peng
Keyword(s):  
Default Risk ◽  
Credit Constraints ◽  
Contingent Claims ◽  
Claims Analysis ◽  
Banking Competition ◽  
Leverage Ratio ◽  
Contingent Claims Analysis ◽  
Credit Risks ◽  
Default Risks ◽  
The Impact

A firm’s default risk is closely related to its macrofinancial stability. As financial reform deepens, banking competition may ease firms’ credit constraints, encouraging them to increase their leverage and default risks. This study uses contingent claims analysis to examine firms’ asset–liability ratio and default distance. We find that companies have low leverage and low overall default risks. Moreover, a pro-cyclical effect exists between leverage and economic growth. As banking competition becomes more intense, the default risk decreases, but firms’ leverage ratio rises significantly. The impact is more prominent for highly leveraged firms. Our findings also indicate that utilizing the contingent claims analysis method to measure firms’ leverage and default risks provides more accurate results. Moreover, we provide empirical evidence of the impact of banking competition on firms’ leverage and credit risks. The results suggest that enhancing financial competition has a positive effect on easing credit constraints and reducing default risks.

scholarly journals Option-Based Credit Spreads

2018 ◽  
Vol 108 (2) ◽  
pp. 454-488 ◽  
Author(s):  
Christopher L. Culp ◽  
Yoshio Nozawa ◽  
Pietro Veronesi
Keyword(s):  
Economic Growth ◽  
Credit Risk ◽  
Risk Premium ◽  
Bond Market ◽  
Credit Spreads ◽  
Put Options ◽  
No Arbitrage ◽  
Primary Determinant ◽  
Default Risks ◽  
Bond Spreads

We present a novel empirical benchmark for analyzing credit risk using “pseudo firms” that purchase traded assets financed with equity and zero-coupon bonds. By no-arbitrage, pseudo bonds are equivalent to Treasuries minus put options on pseudo firm assets. Empirically, like corporate spreads, pseudo bond spreads are large, countercyclical, and predict lower economic growth. Using this framework, we find that bond market illiquidity, investors' overestimation of default risks, and corporate frictions do not seem to explain excessive observed credit spreads but, instead, a risk premium for tail and idiosyncratic asset risks is the primary determinant of corporate spreads. (JEL E23, E32, E44, G13, G24, G32)

Solidarity and Responsibility in the Euro Area: Foes or Friends?

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Andrea Fracasso
Keyword(s):  
Risk Reduction ◽  
Euro Area ◽  
Sovereign Debt ◽  
Risk Sharing ◽  
Multiple Equilibria ◽  
Lender Of Last Resort ◽  
Short Article ◽  
Viable Solution ◽  
Correct Sequence ◽  
Default Risks

Abstract The recent debate on the reform of the economic governance in the euro area has been marred by a stark disagreement on the correct sequence between risk-reduction (responsibility) and risk-sharing (solidarity). In fact, the dichotomy between risk-reduction and risk-sharing may be fallacious as they reinforce each other, particularly in a monetary union with no lender of last resort for the public sector and no common macroeconomic stabilization mechanisms. The lack of risk-sharing mechanisms is per se a major source of redenomination and default risks and thus it makes the euro area prone to financial market segmentation along national borders and ultimately weaker. At the same time, greater structural convergence has to be achieved through structural reforms and fiscal prudence in order to reduce the likelihood of future negative idiosyncratic shocks in currently vulnerable countries. Notwithstanding some progress towards a politically viable solution encompassing both responsibility and solidarity, a number of important issues remain controversial. This short article summarizes the debate and introduces some of these controversial issues, ranging from the correct role of market discipline when markets are prone to self-fulfilling prophecies and multiple equilibria, to the (dis)advantages of sovereign debt restructuring mechanisms based on rules rather than discretion, from the pros and cons of new safe assets in the euro area to the primacy of coping with debt legacy problems, and the like.

Financial Crises, Financing Sources, and Default Risks

2014 ◽  
Author(s):  
Wan-Chien Chiu ◽  
Juan Ignacio Peea ◽  
Chih-Wei Wang
Keyword(s):  
Financial Crises ◽  
Default Risks

scholarly journals Comparison of Data Mining Classification Algorithms Determining the Default Risk

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Begüm Çığşar ◽  
Deniz Ünal
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Default Risk ◽  
Operating Characteristic ◽  
Classification Algorithms ◽  
Financial Industry ◽  
Statistical Institute ◽  
Default Risks ◽  
Characteristic Area ◽  
Statistical Criteria

Big data and its analysis have become a widespread practice in recent times, applicable to multiple industries. Data mining is a technique that is based on statistical applications. This method extracts previously undetermined data items from large quantities of data. The banking and insurance industries use data mining analysis to detect fraud, offer the appropriate credit or insurance solutions to customers, and better understand customer demands. This study aims to identify data mining classification algorithms and use them to predict default risks, avoid possible payment difficulties, and reduce potential problems in extending credit. The data for this study, which contains demographic and socioeconomic characteristics of individuals, were obtained from the Turkish Statistical Institute 2015 survey. Six classification algorithms—Naive Bayes, Bayesian networks, J48, random forest, multilayer perceptron, and logistic regression—were applied to the dataset using WEKA 3.9 data mining software. These algorithms were compared considering the root mean error squares, receiver operating characteristic area, accuracy, precision, F-measure, and recall statistical criteria. The best algorithm—logistic regression—was obtained and applied to the real dataset to determine the attributes causing the default risk by using odds ratios. The socioeconomic and demographic characteristics of the individuals were examined, and based on the odds ratio values, the results of which individuals and characteristics were more likely to default, were reached. These results are not only beneficial to the literature but also have a significant influence in the financial industry in terms of the ability to predict customers’ default risk.

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