Developing a java android application of KMV-Merton default rate model

A survivor swap refers to an agreement for the future exchange of cash flows based upon a mortality-dependent index. Pricing survivor swaps involves the determination of a fixed proportional premium, π, which results in an initial zero value of the swaps for each party. In order to provide a precise valuation of a mortality derivative, such as a survivor swap, an appropriate model is necessary for the forecasting of the mortality rate. The model proposed for use in this study extends the model provided by Cox et al. (2006) by simultaneously considering mortality jumps and default risk. Using this extended model to price survivor swaps in this study, we mainly find from our simulated results, based upon the data collected, that the survivor swap premium has a positive correlation with both the market price of risk and the frequency of jumps. Furthermore, we find that a stronger jump frequency will enhance the effects of the market price of risk on the premium. On the other hand, the survivor swap premium is also found to have a positive correlation with the market price of risk and the frequency of jumps at any level of default risk. However, when the market price of risk and jump frequency levels are both fixed, a higher (lower) default risk will imply a lower (higher) premium.

Credit risk management has become a must in this era due to the increase in the number of businesses defaulting. Building upon the legacy of Kealhofer, McQuown, and Vasicek (KMV), a mathematical model is introduced based on Merton model called KMV-Merton model to predict the credit risk of firms. The KMV-Merton model is commonly used in previous default studies but is said to be lacking in necessary detail. Hence, this study aims to combine the KMV-Merton model with the financial ratios to determine the firms’ credit scores and ratings. Based on the sample data of four firms, the KMV-Merton model is used to estimate the default probabilities. The data is also used to estimate the firms’ liquidity, solvency, indebtedness, return on asset (ROA), and interest coverage. According to the weightages established in this analysis, scores were assigned based on those estimates to calculate the total credit score. The firms were then given a rating based on their respective credit score. The credit ratings are compared to the real credit ratings rated by Malaysian Rating Corporation Berhad (MARC). According to the comparison, three of the four companies have credit scores that are comparable to MARC’s. Two A-rated firms and one D-rated firm have the same ratings. The other receives a C instead of a B. This shows that the credit scoring technique used can grade the low and the high credit risk firms, but not strictly for a firm with a medium level of credit risk. Although research on credit scoring have been done previously, the combination of KMV-Merton model and financial ratios in one credit scoring model based on the calculated weightages gives new branch to the current studies. In practice, this study aids risk managers, bankers, and investors in making wise decisions through a smooth and persuasive process of monitoring firms’ credit risk.

Through a new «law and finance» approach (they teach International Law and FinancialMathematics-Risk Management, respectively), the Authors analyse the guaranteesissued by the Member States of the Euro Area to the Esm fund in the framework ofthe European model of financial assistance to Member States. The A. point out theuncertainties of an evaluation of state guarantees due to technical and legal variables.As for the former, such guarantees are quite peculiar, i) because both parties are exposedto counterparty default risk and the risk of the guarantor could be higher thanthe one of the borrower, and ii) because of the structure of dependence that mightexists between the two risks. The A. compare numerical results on the value of a defaultableand correlated guarantee contract, for different values of the default intensitiesand using two different models, finding relevant differences and counter-intuitivedependence on the attainable correlation. As the level of default risk cannot providefor satisfactory diagnoses, it is hard to evaluate the state guarantees to the Esm, thelevel of dependence being an element of systemic nature, not directly observable. Asfor the legal variables, the aim of the financial aid on one side (a full fulfilment), andthe lack of tools for sanctioning properly a possible non-fulfilment by the beneficiaryon the other side, add further uncertainty.The A. deem crucial for facing such evaluation uncertainty the Ecb role of guarantor oflast resort: the credibility of the whole European financial assistance model lies bothon the number and range of tools available to the Ecb, and on the potentially unlimiteduse of some of them (Omt). Should the unlimited use of the Omt be questioned in thenear future, negative effects on the state guarantees to the Esm and on the Esm operationwould be hardly quantifiable.

Most previous literatures focus on the micro level default risk of individual borrowers whereas the platform default risk has not been rigorously studied yet. In this paper, we investigate the factors affecting platform default risk by employing the Chinese online P2P platform data. We find significant evidence that severe competition among platforms can increase risky behaviors of platforms by allowing riskier borrowers into the system. Some of the risk management devices could alleviate the default risk of platforms; however, others are not effective at alleviating the default risks. In addition, we find evidence that macro environment such as stock market condition or increases in speculative investment opportunities plays critical roles to increase the platform default rate. Our study sheds light on the platforms’ default risk issues and verifies key factors that influence their risky behaviors.

This study empirically examines the impact of borrower, loan, and mortgage parameters on default risk in residential mortgage loans. Using 6743 individual housing loan accounts data from Housing Finance Institutions in Lebanon, we develop a comprehensive model using the multivariable binary logistic regression, best subset logistic regression, and stepwise regression analysis procedures to investigate the impact of 21 predictors and 29 sub-predictor parameters on log odds of default risk. In addition, the study conducted a model diagnosis using the Hosmer - Lemeshow Goodness of fit test, Likelihood Ratio Test, Model accuracy- Classification Table, Statistically Significant Test- ROC curve, and Pregibon Delta Beta Statistics. The study aims to assist financial institutions in managing and assessing the default risk more effectively and develop effective strategies to mitigate this risk. The empirical results suggest that the estimated probability of defaulting on a housing loan is approximately 3.8% when all predicted variables are set at their lowest value. In addition, marital status and the existence of dependence have a positive impact on default risk. The higher the number of dependents is, the higher the risk of default. Moreover, a widowed borrower has a higher log odd of default compared to single, married, and divorced borrowers. Furthermore, the results revealed that self-employed borrowers positively impact the risk of default due to the absence of a steady flow of income. In addition, there is an inverse relationship between the market price-to loan ratio and the log odds of default since the borrower’s equity will increase when the house price increases. However, log odds of default will increase when the loan value is higher than the mortgage market price. Moreover, the result shows that the nature of the borrower’s occupation has a positive relationship with log odds of default where borrowers working in real estate and construction sectors have lower default rates than borrowers working in other industries. In addition, a high interest rate increases the loan's monthly payment and therefore increases the probability of default. Furthermore, the loans granted for purchase and renovation purposes have a lower risk of default than the ones given for construction and under-construction. In addition, the model's overall accuracy was demonstrated by a 91.61 percent visible correct classification rate.

A significant number of women are diagnosed with minor cytological abnormalities on cervical screening. Many authorities recommend surveillance as spontaneous regression might occur. However, attendance for cytological follow-up decreases with time and might put some women at risk of developing invasive disease. To assess the optimum management strategy for women with minor cervical cytological abnormalities (atypical squamous cells of undetermined significance - ASCUS or low-grade squamous intra-epithelial lesions - LSIL) at primary screening in the absence of HPV (human papillomavirus) DNA test. We searched the following electronic databases: Cochrane Central Register of Controlled Trials (CENTRAL Issue 4, 2016), MEDLINE (1946 to April week 2 2016) and Embase (1980 to 2016 week 16). We included randomised controlled trials (RCTs) comparing immediate colposcopy to cytological surveillance in women with atypical squamous cells of undetermined significance (ASCUS/borderline) or low-grade squamous intra-epithelial lesions (LSIL/mild dyskaryosis). The primary outcome measure studied was the occurrence of cervical intra-epithelial neoplasia (CIN). The secondary outcome measures studied included default rate, clinically significant anxiety and depression, and other self-reported adverse effects.We classified studies according to period of surveillance, at 6, 12, 24 or 36 months, as well as at 18 months, excluding a possible exit-examination. We calculated pooled risk ratios (RR) and 95% confidence intervals (CI) using a random-effects model with inverse variance weighting. Inter-study heterogeneity was assessed with I2 statistics. We identified five RCTs with 11,466 participants that fulfilled the inclusion criteria. There were 18 cases of invasive cervical cancer, seven in the immediate colposcopy and 11 in the cytological surveillance groups, respectively. Although immediate colposcopy detects CIN2+ and CIN3+ earlier than cytology, the differences were no longer observed at 24 months (CIN2+: 3 studies, 4331 women; 17.9% versus 18.3%, RR 1.14, CI 0.66 to 1.97; CIN3+: 3 studies, 4331 women; 10.3% versus 11.9%, RR 1.02, CI 0.53 to 1.97). The inter-study heterogeneity was considerable (I2 greater than 90%). Furthermore, the inclusion of the results of the exit examinations at 24 months, which could inflate the CIN detection rate of cytological surveillance, may have led to study design-derived bias; we therefore considered the evidence to be of low quality.When we excluded the exit examination, the detection rate of high-grade lesions at the 18-month follow-up was higher after immediate colposcopy (CIN2+: 2 studies, 4028 women; 14.3% versus 10.1%, RR 1.50, CI 1.12 to 2.01; CIN3+: 2 studies, 4028 women, 7.8% versus 6.9%, RR 1.24, CI 0.77 to 1.98) both had substantial inter-study heterogeneity (I2 greater than 60%) and we considered the evidence to be of moderate quality).The meta-analysis revealed that immediate referral to colposcopy significantly increased the detection of clinically insignificant cervical abnormalities, as opposed to repeat cytology after 24 months of surveillance (occurrence of koilocytosis: 2 studies, 656 women; 32% versus 21%, RR 1.49, 95% CI 1.17 to 1.90; moderate-quality evidence) incidence of any CIN: 2 studies, 656 women; 64% versus 32%, RR 2.02, 95% CI 1.33 to 3.08, low-quality evidence; incidence of CIN1: 2 studies, 656 women; 21% versus 8%, RR 2.58, 95% CI 1.69 to 3.94, moderate-quality evidence).Due to differences in trial designs and settings, there was large variation in default rates between the included studies. The risk for default was higher for the repeat cytology group, with a four-fold increase at 6 months, a six-fold at 12 and a 19-fold at 24 months (6 months: 3 studies, 5117 women; 6.3% versus 13.3%, RR 3.85, 95% CI 1.27 to 11.63, moderate-quality evidence; 12 months: 3 studies, 5115 women; 6.3% versus 14.8%, RR 6.39, 95% CI 1.49 to 29.29, moderate-quality evidence; 24 months: 3 studies, 4331 women; 0.9% versus 16.1%, RR 19.1, 95% CI 9.02 to 40.43, moderate-quality evidence). Based on low- or moderate-quality evidence using the GRADE approach and generally low risk of bias, the detection rate of CIN2+ or CIN3+ after two years does not appear to differ between immediate colposcopy and cytological surveillance in the absence of HPV testing, although women may default from follow-up. Immediate colposcopy probably leads to earlier detection of high-grade lesions, but also detects more clinically insignificant low-grade lesions. Colposcopy may therefore be the first choice when good compliance is not assured. These results emphasize the need for an accurate reflex HPV triage test to distinguish women who need diagnostic follow-up from those who can return safely to routine recall.

Introducing Global Term Structure in a Risk Parity Framework

        Innovative Approach for Forecasting Corporate Default Risk       Submitted To: Journal of Global Economy                              By: Prashanta Kumar Behera, PhD                              Email: pkb_behera@yahoo.in . Ph : 91+8108932693Abstract: Present time corporate default risk parameters are dynamic in nature and understanding how these parameters change in time is a fundamental task for risk management. In this research paper I am trying to forecast for corporate default rates.  I work with historical credit migrations data to construct some time series of interest and to visualize default rates dynamics and also, I use some of the series constructed and some additional data to fit a forecasting model for corporate default rates and to shows some back testing and stress testing. A linear regression model for corporate default rates is presented but the tools and concepts described can be used in combination with other forecasting methodologies.Keywords: Default risk, back testing, stress testing and transition matrix.

The main objective of this article is to study the effect of the regulatory environment on the behaviour of Islamic and conventional banks in 10 countries in the MENA region regarding the level of capital, the risk of default and banking efficiency. In other words, we seek to show the importance of the conventional and Islamic banking system in MENA countries and to assess the efficiency of these banks under the pressure of prudential regulations through a comparative analysis. Our results show that the riskiest banks are those with a high level of capital. Furthermore, the results suggest that the most profitable banks in the MENA region tend to increase their level of risk. We also find that a higher capital requirement encourages banks to take a higher risk with higher profitability.

Using a genetic backpropagation neural network model for credit risk assessment in the micro, small and medium-sized enterprises

Risco de crédito representa uma dimensão importante de risco a ser considerada por bancos quando realizam empréstimos. Em particular, em mercados emergentes as taxas de inadimplência em produtos de empréstimos bancários são muito altas, demandando maior cuidado com relação ao gerenciamento deste risco. Usualmente o risco de crédito de carteiras que envolvem empréstimos é obtido a partir de simulações de Monte Carlo de alto custo computacional que consideram diferentes fatores estocásticos relacionados a incerteza dos pagamentos dos passivos associados aos devedores. Neste trabalho, sob determinadas hipóteses, nós derivamos fórmulas fechadas para as distribuições de probabilidades das taxas de inadimplência de produtos bancários envolvendo um número grande de clientes. O conhecimento de tais distribuições nos permite calcular com facilidade o risco de crédito associado a cada produto oferecido pelo banco. Adicionalmente, a partir destas distribuições, utilizando Retorno Total como medida de retorno, e Perda Máxima como medida de risco, resolvemos o problema de obtenção de carteiras ótimas envolvendo risco de crédito.

oan default poses a significant threat to the sustainability of financial institutions, necessitating the development of intelligent, data-driven systems for early risk detection. This research presents a robust and interpretable machine learning framework for predicting loan default risk using a real-world financial dataset comprising 255,347 anonymized loan records with a pronounced class imbalance (11.6% default rate). To address the skewed class distribution, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training set, enhancing the model's sensitivity to defaulters. Four supervised learning algorithms Logistic Regression, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost) were implemented and rigorously evaluated using stratified 5-fold cross-validation. Performance metrics included Accuracy, Precision, Recall, F1-Score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC), with particular emphasis on metrics sensitive to class imbalance. Among the models tested, the Random Forest classifier achieved the best overall performance, attaining a test accuracy of 96.26%, F1-Score of 0.8014, and AUC-ROC of 0.9215, thereby offering a balanced and reliable prediction of default risk. To ensure model transparency and support regulatory compliance, HasMortgage, EmploymentType, and LoanPurpose as key drivers of default risk, aligning with domain knowledge and enhancing stakeholder trust. The study concludes that combining ensemble machine learning models with class imbalance handling and explainable AI techniques offers a practical and effective solution for credit risk assessment. Recommendations were made for financial institutions, data scientists, and policymakers to adopt interpretable, fair, and performance-optimized predictive systems. This research contributes to the growing body of literature on responsible AI in finance and lays a foundation for future advancements in ethical and data-driven credit decision-making.

In this study we present a comprehensive forward-looking portfolio simulation methodology for assessing the correlated impacts of market risk, private sector and Sovereign credit risk, and inter-bank default risk. In order to produce better integrated risk assessment for banks and systemic risk assessments for financial systems, we argue that reasonably detailed modeling of bank asset and liability structures, loan portfolio credit quality, and loan concentrations by sector, region and type, as well as a number of financial and economic environment risk drivers, is required. Sovereign and inter-bank default risks are increasingly important in the current economic environment and their inclusion is an important model extension. This extended model is demonstrated through an application to both individual Brazilian banks (i.e., 28 of the largest banks) and groups of banks (i.e., the Brazilian banking system) as of December 2004. When omitting Sovereign risk, our analysis indicates that none of the banks face significant default risk over a 1-year horizon. This low default risk stems primarily from the large amount of government securities held by Brazilian banks, but also reflects the banks' adequate capitalizations and extraordinarily high interest rate spreads. We note that none of the banks which we modeled failed during the very stressful 2007-2008 period, consistent with our results. Our results also show that a commonly used approach of aggregating all banks into one single bank, for purposes of undertaking a systemic banking system risk assessment, results in a misestimate of both the probability and the cost of systemic banking system failures. Once Sovereign risk is considered and losses in the market value of government securities reach 10% (or higher), we find that several banks could fail during the same time period. These results demonstrate the well known risk of concentrated lending to a borrower, or type of borrower, which has a non-zero probability of default (e.g., the Government of Brazil). Our analysis also indicates that, in the event of a Sovereign default, the Government of Brazil would face constrained debt management alternatives. To the best of our knowledge no one else has put forward a systematic methodology for assessing bank asset, liability, loan portfolio structure and correlated market and credit (private sector, Sovereign, and inter-bank) default risk for banks and banking systems. We conclude that such forward-looking risk assessment methodologies for assessing multiple correlated risks, combined with the targeted collection of specific types of data on bank portfolios, have the potential to better quantify overall bank and banking system risk levels, which can assist bank management, bank regulators, Sovereigns, rating agencies, and investors to make better informed and proactive risk management and investment decisions.

The emergence of an active interest rate swap market has transformed the nature of corporate debt issuance and risk management. Many firms nowadays routinely use swaps to adjust their ratio of fixed to floating rate debt when there is a change in management's view on interest rates. For instance, if it is felt that market rates have bottomed, a firm could raise that ratio by entering a swap to pay a fixed rate and receive the prevailing level of some reference rate, such as LIBOR (London Interbank Offer Rate). There is, however, default risk on the swap. In this example, the risk is that the counterparty defaults when the fixed rate on a replacement swap is higher than the one originally contracted. Potential default risk at origination is bilateral in that each party to the agreement must consider the riskiness of the other. At any point in the lifetime of the swap, the actual default risk is unilateral in that the swap would have positive economic value to only one of its counterparties.

Default is an increasingly important issue for the Fall, as market stress builds in a weak economic and financial environment rally. The study seeks to investigate if financial tools such as hedging and working capital management can be used for a precaution against default risk in the textile sector of Pakistan. Furthermore, it provides us an analysis on how macroeconomic variables are managed reduces the risk of default. Several elements can potentially impact the likelihood of a corporation experiencing a default on its debt. Nevertheless, this research has only analyzed Working Capital Management, Hedging, and macroeconomic covariables. Data of 102 textile firms was collected for the time of 2015-2020. The objective of this analysis is to assess the influence of these variables on the likelihood of default. The present study employs a logical approach to inquiry. The deductive approach would be the most suitable to evaluate the hypothesis of causation in the study. Our study provides evidence of a strong and statistically significant negative relationship between working capital management and default risk. The importance of the impact of CPI and R&D Investment is observed at a significant level. The statistical significance of the relationship between GDP growth and the probability of default is acceptable. The statistical significance of the impact of interest rates on default likelihood is insignificant. This study offers significant theoretical contributions by incorporating well-established financial theories, including Credit Rationing Theory, to develop a complete framework for understanding default risk. This article examines how these ideas shed light on the behavior of lenders, conflicts of interest inside agencies, and the impact of free cash flow on corporate decision-making. Furthermore, this study investigates the often-overlooked aspect of working capital management and the role of hedging in risk management. This study offers practical insights for finance experts, corporate administrators, legislators, and investors.