Continuous Monitoring: Look Before You Leap

We present a model for pricing credit risk protection for a limited liability non-life insurance company. The protection is typically provided by a guaranty fund. In the case of continuous monitoring, i.e., where the market values of the company's assets and liabilities are continuously observable, and where the market values of assets and liabilities follow continuous processes, regulators can liquidate the insurance company at the instant the market value of its assets equals the market value of its liabilities, implying that the credit protection is worthless. When jumps are included in the claims process, the protection provided by the guaranty fund has a strictly positive market value. The ability to continuously monitor asset prices with continuous sample paths eliminates economic losses from default. Our analysis suggests that economic losses from default stem from jumps in continuously observed asset prices and/or that asset prices are not continuously observed.

INTRODUCTION One of the fundamental tenets of financial economics is that insurance companies, just as other financial and non-financial firms, have a very strong incentive to maximize current shareholder value. (1) This seemingly simple observation leads to a wide variety of managerial behavior. (2) In this article we will review a simple decomposition of the value of an insurance company. The decomposition illuminates the motivation for a variety of strategies that can be observed in practice. These strategies run the gamut from accounting manipulation through risk transfer schemes to positive present value (NPV) project selection. We will review these in some detail below. Then we will illustrate these techniques from our experience with every major U.S. life insurer insolvency since Baldwin United in 1984. COMPONENTS OF From VALUE The market value of insurance company owners' equity is defined as the difference between the market value of assets and the market value of liabilities. For purposes of valuation, it is helpful to partition more finely the components of equity value. In this section, we will partition the value of insurance company owners' equity, or stock in the case of a stock company, into its four major components: franchise value, market value of tangible assets, present value of liabilities, and put option value (see Figure 1). The first two of these components are clearly assets. The third component is related to liability value. The put option value can be treated as part of the liability value as a contra-liability or as an asset. We will discuss each component in turn. [FIGURE 1 OMITTED] The franchise value stems from what economists call quasi-rents. It is the present value of the quasi-rents that an insurer is expected to garner because it has scarce resources, scarce capital, charter value, licenses, a distribution network, personnel, reputation, and so forth. It includes renewal business. (3) Franchise value is dependent on firm insolvency risk. The less insolvency risk there is, the more likely the firm is to remain solvent long enough to capture all the available economic rents arising from its renewal business, its distribution network, its reputation, and so forth. (4) The market value of liabilities includes pricing of all contingencies within the insurance liabilities. In our decomposition we have elected to separate out the risk of the insurance company defaulting on its obligations. The risk of such a default is captured by the put option. Other contingencies, including such risks as interest rate contingencies, mortality contingencies, or equity market contingencies, are included in the present value of liabilities. Thus, the present value of liabilities is the present value of all promised liability cash flows discounted at Treasury rates rather than discounting at rates that would reflect the possibility of default. In the case of interest sensitive cash flows, it is the value of the Treasury security portfolio (including derivatives as needed) that fully defeases the liabilities with all non-default contingencies fully hedged. This quantity will be larger than the market value of liabilities where the promised liability cash flows are discounted at rates that reflect the risk of failure to make the promised cash flows. Merton (1974) initially observed that the value of a debt obligation could be expressed as the value of a default-risk free bond minus a put option. The put option represents the value to the issuer of the possibility of defaulting on the obligation. The same holds true for insurance liability obligations. The market value of liabilities can be expressed as the present value of liabilities minus the put option that represents the value to the issuer of potentially defaulting on the obligations. (5) The market value of tangible assets and the present value of liabilities can be netted together, producing what we will call net tangible value. …

The market value of physical assets varies over time as a function of different factors. These factors are related, for instance, to the correct execution of construction works and installation of systems, the procedures for the operation of systems and equipment, the continuous implementation of maintenance operations and to the location and the perceived value. However, there is no clear evidence, in Italy, of how buildings’ market value is affected by the energy performance measured by the Energy Performance Certification (EPC), promoted thanks to the European Energy Performance of Buildings Directive in order to drive the assets’ energy efficiency. The aim of the research is the definition of a methodology for the identification of the impact of the energy performance on the market value of the assets. An analysis of the market values of assets has been carried out based on a public database. On this basis, an investigation of homogeneous assets’ price areas has been performed. The results have been geolocated and further processed, which allowed to associate differences in the market values to the energy performance retrieved from the Certificazione Energetica degli EDifici (CENED) database (the Italian regional EPC database). The buildings included in the analysis are residential buildings distributed in the regional territory (Lombardy Region). The methodology has been tested at national level but is potentially scalable in different contexts. It has been developed and applied in two case studies, cities with different size and characteristics located in the northern Italy.

Risk Can Be Good: The Tradeoff Between Agency Conflicts, CEO Risk-Taking Incentives, and Credit Risk

The essential issue of this book is the term credit, either in the context of credit markets, credit risk or credit rating. Credit markets’ existence is associated with credit risk, which refers to the risk of an economic loss from the failure of a counterparty to meet its contractual obligations. Due to credit risk, suppliers of credit need to assess the creditworthiness of prospective borrowers. Although modern approaches to credit risk analysis have been developed in recent decades, examining borrowers’ ability to repay their funds is one of the oldest lending activities. The main goal of this monograph is to apply and verify certain methods of credit risk modelling to real data from selected CEE countries. For the main purpose of this work, a micro approach is used to measure credit risk based on monitoring basic indicators and allowing creditors to take the necessary actions in time. The book aims at two partial financial and methodological objectives related to credit risk modelling in this context. Both of them are interconnected, and they complement each other throughout the book. In terms of financial application, this book’s principal objective is to analyse credit risk based on real data, assess its main factors, explore mutual relations, and draw conclusions related to risk assessment and market behaviour. The application is focused on two approaches of individual credit risk assessment within the areas of credit rating and corporate survival. The methodological purpose is the application and verification of rating and bankruptcy models. Rating models estimated using conventional approaches such as discriminant analysis or logistic regression are supplemented by an alternative survival analysis approach to determine the probability of rating downgrade over time. Survival analysis is subsequently used in the following empirical studies on corporate bankruptcy. We will investigate the relationship between the rating and corporate bankruptcy rates and estimate the rating assessment depending on the used model, input variables and the company's age. This book is intended for everyone interested in credit risk, particularly rating and corporate survival modelling, mainly for academia and students at all levels of study. This monograph aims to provide complex information on credit risk fundamentals, current trends and rating systems’ principles. However, the primary purpose is the practical application and estimating models using real corporate data. Thus, we can determine the main factors of rating assessment and corporate survival and demonstrate how these models can be developed through different statistical methods. The text is structured into three central parts: The theoretical background on credit risk and the credit rating industry, a description of econometric approaches used in the applications, and empirical studies on credit rating and corporate bankruptcy modelling. If the reader is particularly interested in estimating models and their comparison and interpretation, then it is suggested that they go directly to the practical application. However, reading the book step by step is recommended to understand the essence and main principles and use them in the application.

Up to the middle of the nineteenth century, most businesses were not too large or complex. They carried their inventories at cost and by the first-in first-out method. The problem was one of physical count and extension at the last invoice prices. Several authors of accounting books believed that the market values of assets should not be ignored and should at least be mentioned, probably by footnote in the balance sheet. During that time, the statement of financial condition was considered more important than the profit and loss statement, because the latter statement was merely used to prove the correctness of the change in the proprietary interest in the balance sheet. In 1929, the American Institute of Accountants reaffirmed the lower of cost or market rule for inventory valuation. In 1943, the Research Department of the American Institute made a survey of members of its Committee of Accounting Procedure for the purpose of formulating rules for inventory pricing. According to replies, it is evident that there was some agreement on application of the lower of cost or market value. However, the study reflects quite a bit of evidence indicating differences of opinion on many points in connection with that method of valuation and its application.

<p>Credit risk refers to the probability of financial loss resulting from a borrower’s failure to repay a loan. Essentially, it encompasses the risk that a lender may not receive the owed principal and interest, leading to disrupted cash flows and increased collection costs. Lenders can mitigate credit risk by analyzing factors related to a borrower’s creditworthiness, such as their current debt load and income. In the last decade, many banks have started to make use of models to assess the risks of lending credit. The credit risk models are very complex and include algorithm-based methods of assessing credit risk. Such a model aims to help banks quantify, aggregate, and manage credit risk. Despite the method, the focus of credit risk assessment stays on credit quality and risk exposure. Strategies to reduce losses and manage risks are pertinent in credit risk management. However, banks have to organize and manage the lending function professionally and proactively and use advanced techniques to measure and manage risks. Credit risk management has become a hot topic due to the ongoing global economic crises, the rapid digital transformation, the recent technological innovations, and the growing use of artificial intelligence in banking. Regulators expect banks to have a clear and comprehensive understanding of their customers and their credit risk and to be transparent and capable in this area. As the Basel regulations change, banks will face more regulatory pressure. To meet the changing regulatory demands and to manage risk better, many banks are changing their credit risk practices. However, banks that see this as only a compliance issue are missing the point. The research findings indicate that the bank’s credit management practices have strongly influenced its profitability. In brief, manoeuvring through the complex risk terrain poses a significant hurdle for financial organizations. This requires ongoing adjustments and strategic choices to ensure both stability and profitability. Consequently, persistent academic research is essential to guide management, governments, and regulators.</p><p><strong> </strong></p><p><strong>JEL</strong>: G21, G32, G33, C53, E58, D81</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0771/a.php" alt="Hit counter" /></p>

Investment is essentially placing some funds at present with the expectation of future profits. The basic thing that an investor needs to know is that there is a risk that follows the profit/return. In determining the proper allocation of funds, an investor needs to consider the company's assets and liabilities. Company assets can be in the form of shares, property, and others. Meanwhile, the company's liabilities include debts and other obligations. One of the sectors whose company value has stagnated or increased during the Covid-19 Pandemic is the financial sector. Securities companies are a sub-sector of the financial sector which has a fairly strong position during the Pandemic. This research aims to determine the weight of fund allocation in each company forming the optimum portfolio and to see the effect of the company's assets and liabilities on the formation of the optimum portfolio. One of the methods used is the Lagrange Multiplier method for model formulation. The results of this study show that the optimal portfolio weight of PANS companies is 16.31% with an allocation of funds amounting to Rp163.612.976,00, the optimum portfolio weight of RELI companies is 83.003% with an allocation of funds of Rp830.029.681,00, and the optimum portfolio weight of TRIM companies is 0.636% with the allocation of funds amounting to Rp6.358.243,00. In this study, it was also found that the greater the percentage difference between the company's assets and liabilities, the greater the company's optimum portfolio weight.

Enterprise Valuation and Credit Risk Evaluation Research I DCF-Based Credit Risk Model—Application in Project/Corporate Bonds & CLO ABSTRACT There are many research models about enterprise credit risk and can be roughly categorized into structural-form and reduced-form according to their assumptions and focuses. Under these two types of credit risk models, few people use stochastic cash flow model to evaluate enterprise credit risk. It’s because people generally think it difficult to estimate cash flow, and no one had ever developed an applicable model to describe the stochastic characteristics of cash flow. Through our observations of cash flow, however, we discover that the behavior of cash flow exist some stochastic characteristics, including mean-reversion and fluctuating above or under zero. We therefore use the concept of “varying coefficient model” (also called time-varying parameter models) to construct a “Time-dependent stochastic cash flow model”. And then based on this model, we further establish a “Multi-periods DCF-Based Credit Risk Model” to estimate a firm’s value and to evaluate a firm’s credit risk. By using these two models and the concept of expected payoff ratio used to evaluate risky bonds developed by Jarrow-Turnbull Model (1995), we can now value the project bonds and corporate bonds. Furthermore, we expand the single firm’s “Multi-periods DCF-Based Credit Risk Model” to multi-firms and then construct a “Multi-assets & periods DCF-Based Credit Risk Model”. In this way, we then can valuing the securitized products including CLO(Collateralized Loan Obligation), CDO(Collateralized Debt Obligation) re-pooled from all kinds of collateral debts . In our research, we select 24 companies as our sample and then make empirical tests of valuation and credit risk by our models. The empirical results show that our models precisely value more than two-third of sample companies and successfully assess credit risks of more than three-fourth of sample companies. These results indicate that our models are preliminarily supported by the empirical evidences. However, in valuing the securitized products such as the CLO by using the “Multi-assets & periods DCF-Based Credit Risk Model” is limited because the model correctly can only deal with the portfolio that assets are cross-default. To handle with this limitation, the Fourier Transform Method (FTM) can be introduced into our models. Fortunately, it seems very promising that the limitation can be relieved by FTM. It is also the focus of our further study. Research II The Influencing Factors’ Research of Market’s Acceptance on M&A Activities—Empirical Tests on Stock Exchange Cases ABSTRACT Due to hot merges and acquisitions in the recent years in the world, we are interested in the roles and hidden meanings of market’s reaction toward M&A activities. However, market reaction is primarily influenced by expected synergy and merger premium, which individually represents expected benefit in the future and cost for mergers. For this reason, the relationship between expected synergy and merger premium becomes the focus of our research. But in historical researches, there doesn’t exist an integrated-analytical framework to describe the relationship. In this paper, we set a measure for expected synergy through stock price’s fluctuating. Additionally, we construct a new plane for firm’s value and exchange ratio based on L-G model by consideration of the expected synergy and the time-varying earning performance. Furthermore, we develop a LMA model which can integrate expected synergy and merger premium in the analytical framework. This model overcomes the difficulties that expected synergy is hard to compare with merger premium in the past. Moreover, we also create a new measure “the level of market’s acceptance” (LMA) through the analytical framework. Therefore, there are two main purposes in our research. First, to examine whether the merger case is reasonable or not. Second, to find out what are the factors which can explain market’s reaction (LMA) and discuss the meanings of these significant factors. In our research, we select 251 stock-exchange mergers (214 for positive premium, 37 for negative premium) as our sample and make judgments for the merger reasonableness by LMA model and statistical tests. As a result, we make some important conclusions: First, 148 of the 251 proposed mergers produced sufficient combined value to maintain each participant’s quadrant I status at the time of merger announcement. Alternatively, the announcement effects produced wealth losses for one or both firms in 103 of the mergers. In the merger period examined (i.e., announcement to the month following completion), at least 40% do not conform to the rationality assumption of LMA model. Second, acquiring firms of the 148 cases outperform the market since one month prior to announcement. It reveals that there may be information leakage. Third, some ratios of relative wealth status significantly influence the level of market’s acceptance in the same direction during the three periods. To sum up, the main contributions of this study is that, by constructing LMA model and making expected synergy can be efficiently compared with merger premium. We not only solve the constraint of the original L-G model but also create a new and effective measurement of market‘s reaction toward M&A activities. In addition, we explore the relationship between LMA and relative wealth’s ratio and try to discuss its economic meanings.

This study, using a sample of 162 firms and industry-adjusted cash flow returns on market value of assets as performance criteria, examines the financial performance of the combined target and acquiring firms over a 5-year post-merger period in relation to the corresponding pre-merger period. We find that post-merger performance is negatively associated with relative target size and positively associated with long-term incentive compensation plans. Firms that are in dissimilar industries also show improved performance, as do firms that merged prior to 1983.

A new portfolio selection problem in bubble condition under uncertainty: Application of Z-number theory and fuzzy neural network

The study aims to predict the value of intellectual capital (IC) based on the performance contribution approach. Theoretically, through the development of a derivative model (DM) that explains the relationship between IC and the firm's performance. The study develops a DM of 16 equations to construct the relationship between the investment in IC and its effectiveness in generating income. This is one of the few studies in predicting IC and the only empirical study applied to UAE listed companies. The study applies the neural network system of 47 firms listed in DFM over five years. The study ranks return on assets, p/e ratio, market value of assets, and return on equity as predictors of IC. It helps managers in predicting and determining investment in IC and its impact on performance. In the future, other than financial factors' need to be included, increase the sample, and conduct research on predicting the components of IC.

We consider the problem of calculating tail loss probability and conditional excess for the Bernoulli mixture model of credit risk. This is an important problem as all credit risk models proposed in literature can be represented as Bernoulli mixture models. Thus, we deviate from the efficient simulation of credit risk literature in that we propose an efficient simulation algorithm for this general Bernoulli mixture model in contrast to previous works that focus on specific credit risk models like CreditRisk\(^+\) or Credit Metrics. The algorithm we propose is a combination of stratification, importance sampling based on cross-entropy, and inner replications using the geometric shortcut method. We evaluate the efficiency of our general method considering three different examples: CreditRisk\(^+\) and two of the latent variable models, the Gaussian and the t-copula model. Numerical results suggest that the proposed general algorithm is more efficient than the benchmark methods for these specific models.

Introduction Three main variables affect the credit risk of a financial asset: (i) the probability of default (PD), (ii) the ‘loss given default’ (LGD), which is equal to one minus the recovery rate in the event of default (RR), and (iii) the exposure at default (EAD). While significant attention has been devoted by the credit risk literature on the estimation of the first component (PD), much less attention has been dedicated to the estimation of RR and to the relationship between PD and RR. This is mainly the consequence of two related factors. First, credit pricing models and risk management applications tend to focus on the systematic risk components of credit risk, as these are the only ones that attract risk-premia. Second, credit risk models traditionally assumed RR to be dependent on individual features (e.g. collateral or seniority) that do not respond to systematic factors, and therefore to be independent of PD. This traditional focus only on default analysis has been partly reversed by the recent increase in the number of studies dedicated to the subject of RR estimation and the relationship between the PD and RR (Fridson et al . 2000, Gupton et al . 2000, Altman et al . 2001, Altman et al . 2003, 2005, Frye 2000a, 2000b, 2000c, Hu and Perraudin 2002, Hamilton et al . 2001, Jarrow 2001 and Jokivuolle and Peura 2003). This is partly the consequence of the parallel increase in default rates and decrease of recovery rates registered during the 1999–2002 period.

Default risk is one of the most important and fastest growing areas in derivatives. Single-name credit default swaps are now well established, and markets for contracts based on the default experience of a credit portfolio, like collateralized debt obligations (CDOs) and basket default swaps, are developing rapidly. Portfolio credit risk requires default correlation as a critical input. But defaults are rare, so default correlation must be estimated in another way, typically from correlations in stock returns. In a „structural” credit risk model, a credit event occurs when the net asset value of the firm falls below a critical threshold. Transitions among credit rating classes can be modeled with a set of thresholds corresponding to the different ratings. The widely used „normal copula” model treats the default probability for a single issuer, and default correlations among pairs of issuers, as arising from exposure to a set of common stochastic risk factors plus an idiosyncratic term. The resulting models are intuitive, but calibration to (implied) correlations embedded in market CDO prices can be difficult, especially as the number of factors increases. In this article, Overbeck and Schmidt offer an alternative structural approach that produces similar values to the normal copula model, with considerably less difficulty. Their trick is to alter the time scales of the Wiener processes driving the underlying asset values, which is equivalent to allowing time-varying volatilities, and permits a much easier calibration of the correlations.