Credit Rating

In this paper we deal with determination of chosen characteristics of vending business in the Czech Republic. Vending seems to be dynamically developing sector of economics. A strong competition is present in this market. This can be a reason that new ideas of improvement appear continuously. Primary data are used to characterize vending business from the perspective of consumer as well as vending operator. The data are used as input to statistical tests; results are summarized and presented in economic terms. At fi rst, survey (about 600 respondents) is analyzed in empirical way. It is informative in such sense, that vending machines are used by majority of users, more o en in school or workplace. The main reasons of using vending machines are speed of shopping and no other shopping possibility. Further part is devoted to comparison of prices under diff erent situations. For example, there are diff erences among various vending machine locations. Vending machine prices are not necessarily higher than prices in shops or cafeterias. Finally, operator profi tability is explored based on company internal data. Among others, hot drinks vending machines are more profi table than vending machines selling bottled beverages of packaged food in general. analysis of consumers, market research, primary data, vending business

Credit rating risks have become the backbone of bank performance. They are the reflection of the current status of the bank and the milestone for future planning. A good credit assessment can better anticipate expected losses and will minimize unexpected losses from accumulating. Given advancements in technology as well as the big data available within banks about customers in an oil country such as Kuwait, a built-in model to help in-household credit scoring is at management's decision. Compared to the current 'black box' rating models, we did a comparison between different classification models for two types of banking: conventional and Islamic. The classification models are as follows: Logistic Regression, Fine Decision Tree, Linear Support Vector Machines, Kernel Naïve Bayes, and RUSBoosted. Sufficiently, the last could be used to classify banks household customers and determine their default cases.

Purpose - The objective of the study is to investigate the relationship between the credit information sharing and the funding cost of banks of the top ten “AA rating” commercial banks of Pakistan as the Commercial banks also play a significant role in the economy of every country. Design/Methodology - In this study, panel data were analyzed from 2011 to 2017. We selected the top ten “AA rating” banks from Pakistan credit rating agency (PACRA) website, and data related to another related variables are obtained from financial statements of the respective banks. Generalized Method of Moments (GMM) statistical technique was employed to measure the relationship among related variables. Findings - The result of the study shows that there is a negative and significant relationship between credit information sharing, operation efficiency, and funding cost. On the other side, profitability has a positive and significant relationship with the funding cost of the bank. Practical Implications ...

for excellent comments and to Minye Zhang, Dawei Tian and Huanghai Li for outstanding research assistance. The authors also thank seminar participants at the Federal Reserve Board, UCLA, George Washington University, and the 2009 AEA session on the subprime crisis for helpful comments. All errors are our responsibility.

In the article the ratings developed by Moody's Corporation, Standard & Poor's Ratings Services and financial data of Polish windows manufactures were analyzed. Ratings published by international agencies were compared with an independently developed rating. Authors made an attempt to verify the hypothesis whether the internal rating created by means of operational research method, significantly differs from the ratings prepared by international rating agencies. In the article mathematical possibilities of potential changes in credit rating were presented.

Öz Uluslararası kredi derecelendirme kuruluşlarının verdiği ülke kredi notları ülkeler arasındaki fon akımlarının yönünü belirlemede önemli bir görev üstlenmektedir. Özellikle yeni sanayileşen ülkeler gelişimi sürdürülebilir hale getirmek ve gelecekteki yatırımları finanse etmek amacıyla sürekli olarak finansman ihtiyacı duymaktadır. Bu çalışmada, yeni sanayileşen ülkelerin kredi notlarını etkileyen unsurlar ele alınmıştır. Bu unsurların başında ekonomik özgürlük indeks göstergesi olarak ele aldığımız hukukun üstünlüğü, devletin büyüklüğü, mevzuat yeterliliği ve serbest piyasa değişkenleri gelmektedir. Ayrıca ülkelerin gelir düzeylerini kontrol etmek amacıyla gayri safi yurt içi hasılanın büyüme oranı ve makroekonomik düzeylerini kontrol etmek amacıyla enflasyon oranı modele dahil edilmiştir. 1995-2021 dönemine ait veriler kullanılarak hazırlanan panel veri seti üzerinde Driscoll-Kraay Standart Hatalar analizi uygulanarak parametreler tahmin edilmiştir. Yaptığımız analiz sonucunda serbest piyasa göstergesi dışındaki tüm ekonomik özgürlük indeks göstergelerinin ülkelerin kredi notları üzerinde pozitif ve anlamlı etkisi olduğu görülmektedir. Çalışma sonuçları, yeni sanayileşen ülkelerin ekonomik özgürlük unsurlarını iyileştirici hamleler yapmasının ülkelerin risk faktörlerinin düşmesine böylece yatırım yapılabilir ülke kredi notlarına ulaşmasına sağlayacağını öne sürmektedir.

This paper analyses the through-the-cycle rating concept; basically, we try to specify its main characteristics, focusing on the differences with point-in-time ratings. We also discuss the effects of this methodology on the prediction power of default probabilities ...

The participation of the private sector in the provision of infrastructure is now a fundamental element of discourse around state modernization especially in the face of fiscal crisis. This paper examines the dialogue against the backdrop of the impression that the concept is new as a tool of fiscal management as well as its adoption in the emerging economies using Nigeria as a reference point. The study traced the evolution, theoretical foundation and history of public private partnership in Nigeria. Our findings reveal that though the adoption of PPP has been positive, it has some challenges, which are as a result of contract term agreement, political influence and the need for renegotiation as a result of increasing cost and those conditions are not envisaged at the time of contracting.

Various statistics-based machine learning techniques have been employed for this task. "Curse of Dimensionality" is still a significant challenge in machine learning techniques. Some research has been carried out on Feature Selection (FS) using genetic algorithm as wrapper to improve the performance of credit scoring models. However, the challenge lies in finding an overall best method in credit scoring problems and improving the time-consuming process of feature selection. In this study, the credit scoring problem is investigated through feature selection to improve classification performance. This work proposes a novel approach to feature selection in credit scoring applications, called as Information Gain Directed Feature Selection algorithm (IGDFS), which performs the ranking of features based on information gain, propagates the top m features through the GA wrapper (GAW) algorithm using

V času gospodarskih nihanj in finančne negotovosti se podjetja lahko znajdejo v resnih težavah z likvidnostjo. Ena izmed zelo pogostih in velikokrat tudi zaskrbljujočih tem v takih okoliščinah je insolventnost – stanje, ko dolžnik več ne zmore poravnavati svojih obveznosti. Slovenska zakonodaja, zlasti ZFPPIPP (Zakon o finančnem poslovanju, postopkih zaradi insolventnosti in prisilnem prenehanju), določa jasne pogoje za insolventnost in postopek prisilne poravnave kot enega od možnih izhodov za dolžnika.

Al fine di individuare le possibili vie di sviluppo degli strumenti utilizzabili per misurare, gestire e controllare il rischio di credito si è tentato di definire i contorni di un approccio integrato che tenga conto delle principali dimensioni del rischio.
In questa esposizione verranno dapprima brevemente riassunte alcune considerazioni sui diversi approcci alla misurazione del rischio credito e poi sarà esemplificato il calcolo di un VaR integrato (finanziario e creditizio).

Purpose-An in-depth understanding of the credit channel of monetary policy (MP) is crucial because interbank rates influence bank funding choices. This study examines the relative roles of dual banking systems, comprising Islamic banks (IBs) and conventional banks (CBs), in transmitting the effects of monetary tightening in the developing economies of Pakistan and Malaysia. It also analyses bank credit expansion across different bank sizes and liquidity positions. Design/Methodology/Approach-The sample used for empirical analysis includes five fully operational IBs, six Islamic branches of CBs and 17 CBs in Pakistan, as well as 11 IBs and 10 CBs in Malaysia. The study employs the robust two-step System Generalised Method of Moments (GMM) estimator on an unbalanced annual bank-level panel dataset covering the period 2007-2022. Findings-The results validate the existence of a credit channel in the two countries examined by demonstrating that both types of banks considerably reduced their funding during tight MP periods. Additionally, the data showed that compared to CBs, IBs were less affected by tight MP policies. Furthermore, the data reveals that larger liquid amounts react less to changes in MP tightening. Originality/Value-Transmission mechanisms have drawn attention in the wake of the global financial crisis of 2007-08, particularly regarding the effectiveness of banks' credit channels. However, the literature is still lacking information about the role of IBs in the monetary policy transmission mechanism (MPTM).

The aim of this study is to analyze the overall performance of the main sectors operating in Borsa Istanbul based on financial ratios and to compare their financial success levels. The financial performance of the sectors was evaluated using the Z-score method, utilizing sectoral financial ratios covering the eight-year period from 2017 to 2024. The findings indicate that the mining and quarrying sector ranks at the top in terms of overall performance, while the electricity, gas, and water sector, along with the transportation and storage sector, also demonstrate strong performances, placing them among the highest-ranked sectors. The wholesale and retail trade, technology, financial institutions, and manufacturing sectors exhibit balanced performances and are positioned in the mid-rankings. On the other hand, the education, health, sports, and entertainment services sector, along with real estate activities, have shown relatively lower performance compared to other sectors.

In recent years, Chinese housing policies have been shifting from encouraging homeownership toward developing the private rented sector, especially in the superstar cities. Nevertheless, what are the target groups and characteristics of private rental housing in Chinese superstar cities, and whether the private rental housing is capable of meeting private tenants' housing needs remain unclear. This paper attempts to bridge this gap from the tenant perspective by examining the determinants of private tenants' residential satisfaction. We argue that residential satisfaction should be examined within different submarkets. By adopting the Structure of Housing Provision thesis, we identified three main sub-sectors in China's superstar cities, i.e. urban village housing, commercial rented housing, and Long-term Rented Apartment (LTRA). Based on a questionnaire survey in Shenzhen, we examined the characteristics of the three subsectors and tenants living in each sub-sector. Furthermore, we evaluated whether the residential satisfaction levels varied significantly in different subsectors and the determinants of residential satisfaction in each sub-sector. The results of an ANOVA showed that tenants living in commercial rented housing and LTRAs were more satisfied than those living in urban village housing. The regression results showed that the determinants of residential satisfaction vary considerably among different sub-sectors. The results of this paper can be useful not only for the landlords to improve tenants' residential satisfaction but also for policy-makers engaged in private rental market development and urban renewal.

The advancement of their risk management activities makes it profi table for major banks to rely on internal credit ratings to calculate Basel II capital requirements (IRB approach). Firms and, more generally, market participants would benefi t from the disclosure of these ratings, as it would reduce their cost of capital and facilitate investment diversifi cation. Banks, however, have no interest in making their data publicly available. This paper proposes a regulatory framework to effi ciently solve this incentive issue. It fi rst shows that there are net benefi ts in requiring the disclosure of internal ratings. The paper then sketches regulatory requirements that would minimize disclosure costs and interest group opposition. Banks opting for the IRB approach would have to provide their internal ratings to one of several regional entities. The latter would consolidate the data collected and giver each borrower a rating equal to the average of the ratings it gets from its lenders. The average rating would be disclosed to the public unless the borrower has opted for non-disclosure. Relying upon multiple regional entities may cause some uncertainty (a given fi rm may get diverging average ratings), but it would also reduce moral hazard effects and foster competition in the rating industry.

We test if issuers of asset-and mortgage-backed securities receive rating favors from agencies with which they maintain strong business relationships. Controlling for issuer fixed effects and a large set of credit risk determinants, we show that agencies publish better ratings for those issuers that provide them with more bilateral securitization business. Such rating favors are larger for very complex structured debt deals and for deals issued during the credit boom period. Our analysis is based on a new deal-level rating statistic that accounts for the full distribution of tranche ratings below the AAA cut-off point of a structured debt deal.

Starting in 2014 with the implementation of the European Commission Capital Requirement Directive, banks operating in the Euro area were benefiting from a 25% reduction (the Supporting Factor or "SF" hereafter) in their own funds requirements against Small and Medium-sized enterprises ("SMEs" hereafter) loans. We investigate empirically whether this reduction has supported SME financing and to which extent it is consistent with SME credit risk. Economic capital computations based on multifactor models do confirm that capital requirements should be lower for SMEs. Taking into account the uncertainty surrounding their estimates and adopting a conservative approach, we show that the SF is consistent with the difference in economic capital between SMEs and large corporates. As for the impact on credit distribution, our difference-in-differences specification enables us to find a positive and significant impact of the SF on the credit supply.

This article proposes a new methodology for estimating the impact offuel price and tax changes on the general price level and the distribution of income and applies a model to Thailand using data for 1975-76 and 1981-82. Because the model allows for pricing under international competition where tax increases must be partially absorbed in reduced factor income ratber than always being passed on in higher consumer prices, the results are significantly different from those generated by the more conventional cost-plus pricing rule. The inflationary impact of fuel tax changes is slight because of both the openness of the economy and the low energy intensity of manufacturing and other production in Thailand. In contrast, taxes on imports engender price increases not only for imports but also for goods which substitute for imports. The model also indicates that the net effects of taxes on petroleum products (other than kerosene) are progressive in their distributional impact, relative to a tax on imports or consumption. A main policy conclusion of the study is that fuel taxes could be used to increase both equity and allocative efficiency without inducing significant inflationary responses. It follows that in the current circumstances of falling world oil prices, developing countries could generate revenues needed for structural adjustment by increasing fuel taxes to maintain domestic petroleum price levels.

Investing is a important thing in a capital market. Bond investment must be noticed the risk especially credit risk. From the information of credit risk, investor can choose the right investment. Credit Metrics is a reduced form model to estimate the risk. Credit Metrics is centered by the corporate rating. The risk not only occur when corporate rating be default but also if the rating upgrade or downgrade. For a bond portfolio, can calculate the optimal portfolio by Mean Variance Efficient Portfolio method. Empirical study can be used for two bonds, first bond is Obligasi Adira Dinamika Multi Finance V Tahun 2011 Seri A and second one is Obligasi BFI Finance Indonesia III Tahun 2011 Seri A. First bond has 127.01640 (Billion) of credit risk and the second one bonds has 18.33472 (Billion). For a portfolio of that two bonds, they have 179.82460 (Billion). For the optimal portfolio, first bond has propotion 66.39% and 33.61% for the second bond.

Investing is a important thing in a capital market. Bond investment must be noticed the risk especially credit risk. From the information of credit risk, investor can choose the right investment. Credit Metrics is a reduced form model to estimate the risk. Credit Metrics is centered by the corporate rating. The risk not only occur when corporate rating be default but also if the rating upgrade or downgrade. For a bond portfolio, can calculate the optimal portfolio by Mean Variance Efficient Portfolio method. Empirical study can be used for two bonds, first bond is Obligasi Adira Dinamika Multi Finance V Tahun 2011 Seri A and second one is Obligasi BFI Finance Indonesia III Tahun 2011 Seri A. First bond has 127.01640 (Billion) of credit risk and the second one bonds has 18.33472 (Billion). For a portfolio of that two bonds, they have 179.82460 (Billion). For the optimal portfolio, first bond has propotion 66.39% and 33.61% for the second bond.

Credit-scoring becomes increasingly important in poor economies and recessions. Decreasing liquidity due to reduced access to both money and debt markets has induced banks to impose restrictions on offering credit, including credit for mortgage loans. In this paper we analyze the internal rating system used by one of the top-five banks in Portugal, and propose a methodological framework which, based on an application of the Delphi technique, allows adjusting trade-offs among evaluation criteria and provides decision makers with a fairer, more accurate and transparent mortgage risk evaluation system.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

We propose an evaluation method for financial assets subject to default risk, when investors face imperfect information about the state variable triggering the default. The model we propose generalizes the one by in the following way: (i) it incorporates informational noise in continuous time, (ii) it respects the (H) hypothesis, (iii) it precludes arbitrage from insiders. The model is sufficiently general to encompass a large class of structural models. In this setting we show that the default time is totally inaccessible in the market's filtration and derive the martingale hazard process. Finally, we provide pricing formulas for default-sensitive claims and illustrate with particular examples the shapes of the credit spreads and the conditional default probabilities. An important feature of the conditional default probabilities is they are non Markovian. This might shed some light on observed phenomena such as the "rating momentum".

PurposeThe purpose of this study is to examine whether external debt procurements during the military and civilian regimes had a correlation with infrastructural developments using available data from Nigeria.Design/methodology/approachThe sample period covering 41 years, was divided into two periods representing the military and civilian regimes with respective secondary data secured from the World Bank Group online database. The study employed robust least square regression, autoregressive distributed lag and the error correction term to test the variables at the 0.05 significance level.FindingsThe results affirmed that external debts shows positive and significant relationship with infrastructural developments proxy for capital investments during the short-run for both military and civilian regimes in Nigeria, while the outcome was only significant and negatively signed for the civilian regime in the long-run with 52.28% speed of convergence to long-run. This study concludes that...

During the 1980's, theories were developed to explain the striking correlation between real exchange rates and foreign direct investment (FDI). However, this relationship broke down for Japanese FDI in the 1990's, as the real exchange rate appreciated while FDI plummeted. We propose the relative access to credit hypothesis and show that unequal access to credit by Japanese firms contributes to the explanation of declining Japanese FDI. Using bank-level and firm-level data sets, we find that financial difficulties at banks were economically and statistically important in reducing the number of FDI projects by Japanese firms into the United States.

A transition matrix P is said to be embeddable if it has a generator matrix Q such that P =exp (Q) If the approximated transition matrix P is embeddable, then the estimator Q can be got for the generator matrix Q. What of cases when P is not embeddable? This paper will show how to evaluate P and find estimatorˆin cases where Pˆ is embeddable (Kingman, 1962). This problem can be solved by using four methods namely.1. Diagonal and Weighted adjustments Method 2. The Generator Quasi-Optimization method 3. The EM logarithm Method 4. The Gibbs sampler (Markov Chain Monte Carlo Method) Later after sampling each of the above methods, an L norm will be performed on the results so as to deduce which method is the best for estimating the Generator Matrix for an Credit transition Matrix.

Generalized Linear Mixed Models (GLMMs) can be used to model the occurrence of defaults in a loan or bond portfolio. In this paper, we used a Bernoulli mixture model, a type of GLMMs, to model the dependency of default events. We discussed how Bernoulli mixture models can be used to model portfolio credit default risk, with the probit normal distribution as the link function. The general mathematical framework of the GLMMs was examined, with a particular focus on using Bernoulli mixture models to model credit default risk measures. We showed how GLMMs can be mapped into Bernoulli mixture models. An important aspect in portfolio credit default modelling is the dependence among default events, and in the GLMM setting, this may be captured using the so called random effects. Both fixed and random effects influence default probabilities of firms and these are taken as the systemic risk of the portfolio. After describing the model, we also conducted an empirical study for the applicabili...

Credit quality changes need to be analysed from time to time. A good model for analysis needs to determine the Capital and reserves needed to support Credit Instruments portfolios as well as individual Credits. Conditions under which the Series method of finding Markov chains generators of Empirical Transition Matrices in Credit Ratings applications are identified in this article. Searching for valid Generators especially when a true generator does not exist and the properties of the series method is shown. Credit exposures Transition from one rating to another as well as Historical information are used to model estimation that provide a description of Credit quality evolution probability. Time Homogeneous Markov Model specification is popularly used but it is only good in providing a description of portfolio risk changes in the short run hence restrictive in long run Credit processes. I propose a test which is simple and is of time homogeneity null hypothesis performed on all types of data reported often. The data used in the test is Sovereign debt, Municipal Bonds and Commercial paper. I find that transitions on municipal Bond ratings are described adequately for a period of up to five years. The Commercial paper assumes Markovian characteristics for a period of up to Six Months on a scale of 30 days transitions and the Sovereign debt transitions are described adequately by the Markov model using small data sample sizes.

In many applications of Risk Management nature, Credit Migration Matrices or Transitional matrices are the main Cardinal inputs and the accuracy of their estimation is very critical. Three approaches namely Cohort, time homogeneous, non homogeneous are explored in this paper and the differences resulting statistically highlighted. Using techniques of bootstraps, a testing procedure is developed for assessing statistically the migration matrices differences. We find that using the Frequentist (Cohort) approach can yield huge discrepancies than the two duration methods namely Homogeneous and Non-Homogeneous duration methods. The more efficient Duration methods when put into use will have substantial significance in Capital Credit Risk differences between recession and expansion and economic regimes. Depending on the estimation technique used, the difference can be large and damaging and can result in possibility of false assumption being made of time homogeneity.

At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

I develop methods that produce consistent estimates of the Vasicek-Basel IRB (VAIRB) credit risk model parameters. I apply these methods to Moody's data on corporate defaults over the period 1920-2008 and assess the model fit and construct hypothesis tests using bootstrap methods. The results show that the VAIRB does not capture the variability in Moody's default data: there are numerous episodes in which obligors default with much greater frequency than predicted. This pattern is consistent with a missing common factor that affects default correlation only intermittently-a missing factor similar to the frailty covariate in Duffie et al. (2009). Unlike Lopez ( ), I find the VAIRB correlation parameter to be larger for lower-rated credits. I use estimates of the VAIRB error distribution to construct capital allocations for model risk and find that the capital buffers for model risk are substantial, especially for lower-graded credits. VAIRB common factor estimates exhibit positive autocorrelation and thus long time series are usually necessary to produce reliable model estimates. Alternatively, I use common factor and correlation parameter estimates from the 1920-2008 data to control for common factor realizations when estimating unconditional default rates (PDs) from short samples. I estimate PDs and confidence intervals using default data for Moody's alpha-numeric rating grades (1998-2008). After correcting for common factor effects, sample average default rates are shown to overstate the PD for most credit grades in this sample period.

This study examines the existing credit rating methodology proposed in the literature to explore the development of a new credit rating model based on the financial variables of the enterprise. The focus is on the period after the financial crisis of 2018. This study aims to develop a credit rating model using neural networking and tests the same for its accuracy. The goal of this study is to address the issue brought up by previous research on subjectivity in the data used to determine creditworthiness. The database for the study includes financial data up to July 2022 from December 2018. A model is created to assess an enterprise's creditworthiness using neural networking. This study first evaluated the existing credit rating models proposed in the literature. Next, based on financial data and neural networking, a model is developed. It was evident that the model developed in this study has an accuracy of 85.16% and 76.47% on train and test data respectively. There exist several models to determine the creditworthiness of borrowers but all failed to address the concern of subjectivity in the data. The model created in this study made use of cutting-edge technology such as neural networking and financial data. This paper's unique approach and model construction based on a comparison of existing models is rare in the literature and justify the originality of this paper with a practical value at the global level.

In regulatory and competitive environments increasingly tight, banks have been forced to constantly improve their internal rating models. Despite the increased supply models and statistical approaches that has been proposed to them, the answer to the question: how much to lend ? remains always at the discretion of the credit risk manager. The aim of this paper is to propose a statistical approach that allows an estimation of the maximum facility bank overdraft (limits) that the credit risk manager may allow coorporate and to identify the determinants of this limits.

This research investigates the partial and simultaneous the influence of leverage, profitability, credit rating on risk disclosure. This research involved thirteen public banks on the Indonesia Stock Exchange in 2014-2019. Risk disclosure is measured by counting risk keywords in each annual report. The panel data analysis was employed to test the effect of Leverage (X1), Profitability (X2), and Credit Rating (X3) on Risk Disclosure (Y). Hypotheses testing used multiple linear regression or OLS (Ordinary Least Square). The finding indicates that Leverage and Credit Rating do not influence Risk Disclosure. Leverage, Profitability, and Credit Rating simultaneously influence Risk Disclosure. 

This study examined the challenges confronting the operators of private primary and secondary schools in Awka South Local Government Area of Anambra State. Three specific purposes, three research questions and three hypotheses guided the study. Descriptive survey design was adopted for the study. The population of the study comprised all the 214 (58 private secondary school and 156 private primary school) operators in the local government area; which was used as the sample for the study. Researchers structured rating scale "Challenges Confronting Private School Operators Rating Scale (CCPSORS)" was used for data collection. The instrument was face validated by three experts in Faculty of Education. Average internal consistency coefficient of 0.79 was obtained using Cronbach Alpha statistical method. The researcher administered the instrument to the respondents with the help of two research assistants. Data were analyzed using aggregate mean. The findings revealed that inadequate funds, inadequate infrastructures, and inadequate instructional materials are challenges that confront the operators of private primary and secondary schools in Awka South Local Government Area of Anambra State. The study concluded that these challenges could impede students' learning experiences, limit educational opportunities, and hinder overall academic achievement; and addressing them is crucial for ensuring equitable access to quality education and fostering positive learning outcomes. Based on the findings, the study recommended among others that Private school management should make adequate funding a top priority by creating and implementing sustainable funding strategies. They should focus on improving infrastructure

How does the sovereign credit ratings history provided by independent ratings agencies affect domestic financial sector development and international capital inflows to emerging countries? We address this question utilizing a comprehensive dataset of sovereign credit ratings from Standard and Poor's from 1995-2003 for a cross-section of 51 emerging markets. Within a panel data estimation framework, we examine financial sector development and the influence of sovereign credit ratings provision, controlling for various economic and corporate governance factors identified in the financial development literature. We find strong evidence that our sovereign credit rating measures do affect financial intermediary sector developments and capital flows. We find that i) long-term foreign currency sovereign credit ratings are important for encouraging financial intermediary development and for attracting capital flows. ii) Long-term local currency ratings stimulate domestic market growth but discourage international capital flows. iii) Short-term ratings (both foreign and local currency denominated) retard all forms of financial developments and capital flows. There are important implications in this research for policy makers to encourage the provision of longerterm credit ratings to promote financial development in emerging economies.

Education tremendously impacts a woman's capacity to build relationships with others. Pakistan has one of the lowest schooling rates in South Asia, which limits its human resources and makes reforming the country more difficult. Patriarchal ideas heavily influence the societal structure in Pakistan. Education for young men is favored over education for young women. The Human Development Report lists Pakistan as having "low human advancement" The country's growth and prosperity depend on its educational system. So, this study aimed to analyze barriers to women’s educational empowerment in Pakistan. Subsequently, a descriptive content analysis was conducted through an intensive research review. Research shows that mentoring young women often yields more substantial income and benefit gains. A woman's ability to find well-paying work and support a healthy family can be significantly increased through education. Education is also greatly impacted by a girl or woman...

When pricing OTC contracts in the presence of additional risk factors and costs, such as credit risk and funding and collateral costs, the starting “clean price” is modified additively by valuation adjustments (XVAs) that account for each factor or cost in isolation, while seemingly ignoring the combined effects. Instead, risk factors and costs can be jointly accounted for ab initio in the pricing mechanism at the level of cash flows, and this “adjusted cash flow" approach leads to a nonlinear valuation formula. While for practitioners this made more sense because it showed which discount factor is used for which cash flow (recall the multi-curve environment post-crisis), for academics, the focus was on checking that the resulting nonlinear valuation formula is consistent with the theoretical arbitrage-free “replication approach” that we also analyse in the paper. We formulate specific reasonable assumptions, which ensure that the valuation formulae obtained by the two approach...

We develop a consistent, arbitrage-free framework for valuing derivative trades with collateral, counterparty credit risk, and funding costs. Credit, debit, liquidity, and funding valuation adjustments (CVA, DVA, LVA, and FVA) are simply introduced as modifications to the payout cash flows of the trade position. The framework is flexible enough to accommodate actual trading complexities such as asymmetric collateral and funding rates, replacement close-out, and re-hypothecation of posted collateral-all aspects which are often neglected. The generalized valuation equation takes the form of a forward-backward SDE or semi-linear PDE. Nevertheless, it may be recast as a set of iterative equations which can be efficiently solved by our proposed least-squares Monte Carlo algorithm. We implement numerically the case of an equity option and show how its valuation changes when including the above effects. In the paper we also discuss the financial impact of the proposed valuation framework and of nonlinearity more generally. This is fourfold: First, the valuation equation is only based on observable market rates, leaving the value of a derivatives transaction invariant to any theoretical risk-free rate. Secondly, the presence of funding costs makes the valuation problem a highly recursive and nonlinear one. Thus, credit and funding risks are non-separable in general, and despite common practice in banks, CVA, DVA, and FVA cannot be treated as purely additive adjustments without running the risk of double counting. To quantify the valuation error that can be attributed to double counting, we introduce a "nonlinearity valuation adjustment" (NVA) and show that its magnitude can be significant under asymmetric funding rates and replacement close-out at default. Thirdly, as trading

We develop an arbitrage‐free valuation framework for bilateral counterparty risk, where collateral is included with possible rehypothecation. We show that the adjustment is given by the sum of two option payoff terms, where each term depends on the netted exposure, i.e., the difference between the on‐default exposure and the predefault collateral account. We then specialize our analysis to credit default swaps (CDS) as underlying portfolios, and construct a numerical scheme to evaluate the adjustment under a doubly stochastic default framework. In particular, we show that for CDS contracts a perfect collateralization cannot be achieved, even under continuous collateralization, if the reference entity’s and counterparty’s default times are dependent. The impact of rehypothecation, collateral margining frequency, and default correlation‐induced contagion is illustrated with numerical examples.

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