• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.717-729
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• 2021

This paper examines the efficacy of the default risk factor in an emerging market context using the Fama-French five-factor model. Our aim is to test whether the Fama-French five-factor model augmented with a default risk factor improves the predictability of returns of portfolios sorted on the firm's characteristics as well as on industry. The default risk factor is constructed by estimating the probability of default using a hybrid version of dynamic panel probit and artificial neural network (ANN) to proxy default risk. This study also provides evidence on the temporal stability of risk premiums obtained using the Fama-MacBeth approach. Using a sample of 3,806 firm-year observations on non-financial listed companies of Pakistan over 2006-2015 we found that the augmented model performed better when tested across size-investment-default sorted portfolios. The investment factor contains some default-related information, but default risk is independently priced and bears a significantly positive risk premium. The risk premiums are also found temporally stable over the full sample and more recent sample period 2010-2015 as evidence by the Fama-MacBeth regressions. The finding suggests that the default risk factor is not a useless factor and due to mispricing, default risk anomaly prevails in the Pakistani equity market.

• East Asian mathematical journal
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• 제36권1호
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• pp.55-60
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• 2020

We study a probabilistic approach for valuing an exchange option with default risk. The structural model of Klein [6] is used for modeling default risk. Under the structural model, we derive the closed-form pricing formula of the exchange option with default risk. Specifically, we provide the pricing formula of the option with the bivariate normal cumulative function via a change of measure technique and a multidimensional Girsanov's theorem.

• The Journal of Asian Finance, Economics and Business
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• 제6권2호
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• pp.15-24
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• 2019

This study analyzes the effect of the capital structure of Korean manufacturing firms on default risk based on Moody's KMV option pricing model where the probability of default is obtained by measuring the distance to default as a covariant in logit model developed by Merton (1974). Based on the panel data of manufacturing firms, this study achieves its primary objective, using a fixed effect regression model and examines the effect of a firm's capital structure on default risk amongst publicly listed firms on Korea exchange during 2005-2016. Empirical results obtained suggest that the rise in short-term debt to assets leads to increase the risk of default whereas the increase in long-term debt to assets leads to decrease the default risk. The benefits of short-term debt financing over a short-term period fade out in the presence of information asymmetry. However, long-term debt financing overcomes the information asymmetry and enjoys the paybacks of tax advantage associated with long-term debt. Additionally, size, tangibility and interest coverage ratio are also the important determinants of default risk. Findings support the trade-off theory of capital structure and recommend the optimal use of long-term debt in a firm's capital structure.

• 지식경영연구
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• 제11권1호
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• pp.19-35
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• 2010

This study explores the effects of technology innovation activity on a profitability and the default risk of firms. Sample for this study consists of manufacturing firms listed on the Korea Stock Exchange from 1st January 2000 to 31st December 2007. We use of R&D ratio as a proxy of technology innovation activity. The default probability proxied for the default risk of firms is measured by the Merton's (1974) model where accounts for a market value of firms and a volatility of it. This study provides evidence that technology innovation activity has a positive effect on a profitability, but a negative effect on the default risk of firms. Our study also finds the significant mediation effect of profitability that the enhancement in profitability resulting from technology innovation activity lowers the default risk of firms.

• 산업진흥연구
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• 제9권2호
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• pp.197-205
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• 2024

본 연구는 2012년부터 2022년까지 중국 A주 상장회사 중에서 중오염기업을 표본으로 하여 기업의 ESG 성과가 채무불이행 위험에 미치는 영향을 분석하였다. 연구 결과는 좋은 ESG 성과가 기업의 채무불이행 위험을 효과적으로 감소시키는 것으로 나타났다. 추가 분석에서는 기업의 ESG 성과가 자금조달 제약의 영향을 완화시켜 채무불이행위험을 감소시키는 것을 보여주었다. 본 연구는 ESG 성과의 관점에서 채무불이행 위험의 영향 요인을 탐색하고, 기업의 ESG 성과가 경제적으로 미치는 영향에 대해 연구하여, 기업의 채무불이행 위험 예방에 대한 실증적 자료를 제공하였다.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.497-502
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• 2021

This study aims to investigate the determinants of default risk of rural banks in East Java, Indonesia. The method used is descriptive verification and logistic regression analysis. The data used is secondary in the form of monthly annual financial reports of rural banks in East Java during the period 2009-2018. From the results, it was shown that net interest margin (NIM) as a proxy of market risk, non-performing loan (NPL) as a proxy of credit risk, operation efficiency as a proxy of operational risk and return on assets (ROA) as a proxy of profitability have a significant influence on default risk. Meanwhile, the loan to deposit (LDR) ratio as a proxy of liquidity risk has no significant influence on default risk. Banks need to implement risk management and meet the capital adequacy requirements of regulators so that they are resistant to risk, and also, compliant with bank governance to be able to produce high returns for rural banks have an impact on sustainability and its existence. The ability to identify setbacks in bank conditions and the ability to distinguish between healthy and problematic banks will enable to anticipate default banks.

• 한국안전학회지
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• 제15권1호
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• pp.161-166
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• 2000

Regulators often specify default values that are considered acceptable for use in risk analyses as input to regulatory decisions. Because both performing and validating a detailed risk analysis of a complex system are costly and time-consuming undertakings, the use of default values can greatly facilitate the process of performing a risk analysis in the first place as well as the process of reviewing and verifying the risk analysis. It may also ensure more uniform in quality of risk analyses. However, different regulatory agencies differ in their approaches to the use of default values, and the implications of these differences are not yet widely understood. Moreover, large heterogeneity among licensees makes it difficult to set suitable defaults. This paper focuses on the effect of default values on estimates of risk. Some insights on the effects of different levels of conservatism in setting defaults will be provided. The results can help decision makers evaluate the levels of safety likely to result from their regulatory policies.

• 산업경영시스템학회지
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• 제44권1호
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• pp.1-8
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• 2021

As a nation experiencing rapid economic growth, South Korea and its government have made a continuous effort toward efficient research investments to achieve transformation of the Korean industry for the fourth industrial revolution. To achieve the maximum effectiveness of the research investments, it is necessary to evaluate its funding's worth and default risk. Thus, incorporating the concepts of the Black-Scholes-Merton model and the Greeks, this study develops a default-risk evaluation model in the foundation of a system dynamics methodology. By utilizing the proposed model, this study estimates the monetary worth and the default risks of research funding in the public and private sectors of Information and Communication technologies, along with the sensitivity of the R&D economic worth of research funding to changes in a given parameter. This study finds that the public sector has more potential than the private sector in terms of monetary worth and that the default risks of three types of research funding are relatively high. Through a sensitivity analysis, the results indicate that uncertainty in volatility, operation period, and a risk-free interest rate has trivial impacts on the monetary worth of research funding, while volatility has large impacts on the default risk among the uncertain factors.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제19권1호
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• pp.23-45
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• 2015

We consider counterparty risk in CDS rates. To do so, we use a multivariate jump diffusion process for obligors' default intensity, where jumps (i.e. magnitude of contribution of primary events to default intensities) occur simultaneously and their sizes are dependent. For these simultaneous jumps and their sizes, a homogeneous Poisson process. We apply copula-dependent default intensities of multivariate Cox process to derive the joint Laplace transform that provides us with joint survival/default probability and other relevant joint probabilities. For that purpose, the piecewise deterministic Markov process (PDMP) theory developed in [7] and the martingale methodology in [6] are used. We compute survival/default probability using three copulas, which are Farlie-Gumbel-Morgenstern (FGM), Gaussian and Student-t copulas, with exponential marginal distributions. We then apply the results to calculate CDS rates assuming deterministic rate of interest and recovery rate. We also conduct sensitivity analysis for the CDS rates by changing the relevant parameters and provide their figures.

• 지능정보연구
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• 제26권2호
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• pp.105-129
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• 2020

본 연구는 부도위험 예측을 위해 K-IFRS가 본격적으로 적용된 2012년부터 2018년까지의 기업데이터를 이용한다. 부도위험의 학습을 위해, 기존의 대부분 선행연구들이 부도발생 여부를 기준으로 사용했던 것과 다르게, 본 연구에서는 머튼 모형을 토대로 각 기업의 시가총액과 주가 변동성을 이용하여 부도위험을 산정했으며, 이를 통해 기존 방법론의 한계로 지적되어오던 부도사건 희소성에 따른 데이터 불균형 문제와 정상기업 내에서 존재하는 부도위험 차이 반영 문제를 해소할 수 있도록 하였다. 또한, 시장의 평가가 반영된 시가총액 및 주가 변동성을 기반으로 부도위험을 도출하되, 부도위험과 매칭될 입력데이터로는 비상장 기업에서 활용될 수 있는 기업 정보만을 활용하여 학습을 수행함으로써, 포스트 팬데믹 시대에서 주가 정보가 존재하지 않는 비상장 기업에게도 시장의 판단을 모사하여 부도위험을 적절하게 도출할 수 있도록 하였다. 기업의 부도위험 정보가 시장에서 매우 광범위하게 활용되고 있고, 부도위험 차이에 대한 민감도가 높다는 점에서 부도위험 산출 시 안정적이고 신뢰성 높은 평가방법론이 요구된다. 최근 머신러닝을 활용하여 기업의 부도위험을 예측하는 연구가 활발하게 이루어지고 있으나, 대부분 단일 모델을 기반으로 예측을 수행한다는 점에서 필연적인 모델 편향 문제가 존재하고, 이는 실무에서 활용하기 어려운 요인으로 작용하고 있다. 이에, 본 연구에서는 다양한 머신러닝 모델을 서브모델로 하는 스태킹 앙상블 기법을 활용하여 개별 모델이 갖는 편향을 경감시킬 수 있도록 하였다. 이를 통해 부도위험과 다양한 기업정보들 간의 복잡한 비선형적 관계들을 포착할 수 있으며, 산출에 소요되는 시간이 적다는 머신러닝 기반 부도위험 예측모델의 장점을 극대화할 수 있다. 본 연구가 기존 머신러닝 기반 모델의 한계를 극복 및 개선함으로써 실무에서의 활용도를 높일 수 있는 자료로 활용되기를 바라며, 머신러닝 기반 부도위험 예측 모형의 도입 기준 정립 및 정책적 활용에도 기여할 수 있기를 희망한다.