To study the merits of the popular K-means clustering technique while predicting failures of commercial banks, we contrast hereafter two forecasting systems. The first one contains two complementary stages, with unsupervised K-means clustering followed by logistic regression deployed over the three most hazardous clusters (out of five) formed. The second system incorporates logistic regression over the entire sample of banks. We find that the first prognostic system is relatively strict. It better identifies bank failures beforehand, but it also projects more potential failures among the solvent banks. The second system is more lenient. It does not identify in advance actual bank failures in many cases, yet it does not speculate failures for many solvent banks either. The second system achieves a slightly higher overall predictive power than the first system. The minor statistical disadvantage of the K-means clustering is observed mainly because of the scarcity of bank failures in practice. The K-means clustering prescreening stage intensifies the systemic costs associated with type-II errors (predicting failures for solvent banks), but it simultaneously reduces the systemic costs linked to type-I errors (not predicting failures for eventually failed banks). Overall, the K-means clustering prescreening technique has prospective economic advantages, as it assists in slashing the total simulated systemic costs.

Preliminary univariate and multivariate regressions, visual inspections, various relative entropy probes, and complementary Pearson correlation tests and Welch's t-tests all suggest that the copper-to-gold ratio often embeds rather short-termed (up to a few days) yet credible information about the 10-year U.S. Treasury yield. This phenomenon has been more noticeable in recent years and in times where no major economic shocks have developed. False signals in this leading indicator, however, are not uncommon, and they emerge mostly around global macroeconomic tremors. As declared by different market participants, in the absence of macroeconomic turbulences, the copper-to-gold ratio tends to be tightly coordinated (up to a few day lags) with the 10 -year U.S. Treasury yield, and as such, it can serve as a momentary leading indicator for the latter, although not always and not with complete precision.

In this study, we construct a compartmental model that tracks the different states and their respective hazards for typical mortgage loans. We consider that an active mortgage loan could become delinquent in light of either common systemic risks or idiosyncratic risks in the job market. These two groups of employment-related perils jeopardize the sources of income underlying the mortgage monthly payments to lenders and could hurt the ability of mortgage loan borrowers to retire their debt. We also contemplate ongoing risks of a collapse in the housing market, which might transform the mortgage loan to be "underwater" and consequently diminish borrowers' incentives to service the outstanding balance. We develop the necessary derivations, illustrate the functionality of the model over several hypothetical simulations and sensitivity analyses, suggest variable estimation specific guidelines, conclude, and discuss potential extensions for the proposed model.

We study patterns of spatial autocorrelations of four customary performance measures (return on asset, return on equity, net interest margin, and loan loss reserves to total assets) among 49 commercial banks spread in the continental US from the first quarter of 2000 until the fourth quarter of 2010, before and after the subprime mortgage crisis. We use Moran's I, Geary's C, and Conley's one-metric and two-metric spatial autocorrelation methodologies and discover mostly positive (negative) spatial autocorrelations before (during and after) the US subprime mortgage crisis. We also identify the unique influences of size and risk management practices in these commercial banks on the spatial autocorrelations within the two tested periods. Our findings suggest that among many US commercial banks, unifying economic features were more dominant before the crisis, but they were gradually replaced with dividing economic elements during and after this major financial calamity. Our robust evidence aim to highlight the influence of physical distances among commercial banks on their routine operations.

Purpose The purpose of this paper is to present a comprehensive framework for assisting lending banks in their current expected credit losses (CECL) forthcoming computations. Design/methodology/approach The bottom-up approach requires multiple steps including the spline method for identifying optimal segments in the lifetimes of loans, Poisson regressions for evaluating the explanatory variables and hazard rate probes for gaining inferences toward the expected credit losses and their projected schedule. Findings The CECL paradigm has both advantages and disadvantages, as discussed hereafter. Practical implications The model is practical, accurate in the sense that provisions are properly and timely allocated, it can be programmed and it relies on merely a few mild assumptions, thus it can be conveniently calibrated to fit broad macroeconomic scenarios. Originality/value This study provides background on the subject, motivate each module, construct the advised model, assemble a pseudo-database, demonstrate the functionality of the procedures and further draw conclusions on the effectiveness of the current strategy.

PurposeThis study empirically examines, from the first quarter of 1981 until the fourth quarter of 2017, the relations across customary domestic issuer credit ratings (long-term, short-term and subordinate) and three popular corporate risk-taking measurements (the variability of operating profitability, net profitability, and research and development expenses).Design/methodology/approachThe author deploys categorical regressions and robustness tests with control variables, interaction terms, fixed effect variables, lag variables and delta variables.FindingsThe author documents that both short-term and subordinate domestic credit ratings are key determinants of the volatility of operating profitability. The author also identifies long-term credit ratings as secondary factors, yet they do affect broader corporate risk-taking behavioral features (along all three measurements). Furthermore, the author finds that the higher (lower) the credit ratings assigned, i.e. the superior (inferior) the credit quality externally judged, the more (less) overall risk firms tend to undertake.Originality/valueIt is the first research to examine both the inclusive influence and the granular effects of credit ratings on corporate risk-taking (CRT) behavior. It is also the only enquiry to inspect the specific relationships along three types of domestic issuer credit ratings: long-term, short-term and subordinate ratings.

In this study, we deploy various methods for analyses of digits and provide rigorous empirical evidence that most banks’ off-balance sheet items have partial conformity to Benford’s law in their first leading (significant) digits. The accounting records also show scarce compliance with Benford’s law in their second leading digits. Most of these banking activities emerge with values that are downward manipulated at several percentages and excessive rounding, with disproportionate usage of 0 and 5 in their last three digits, regardless of whether the items are traded on designated exchanges, handled only Over-the-Counter, or represent business relationships between commercial banks and customers. Overall, we expose here widespread though modest artificial deflation in the recorded values of banks’ OBS items and a unique phenomenon of significant overuse of the numbers 0 and 5 in different digits, with strong violations of Benford’s law. We further notice that, for the majority of banks’ off-balance sheet items, key regulatory developments, such as the three Basel Accords, present meaningful and continuous impact on the overall reduction in the anomalous appearances of the numbers 5 in the first and 0 and 5 in the second leading digits. At present, however, we still observe irregular spreads (well above the norm) of the numbers 0 and 5 in the second leading digits.

In this study, we develop a generic Markov model that tracks in real-time and forecasts the potential contagion of bank runs during severe contractionary economic cycles. The proposed model aims to assist regulatory bodies that supervise heterogeneous banking systems by offering an adaptable monitoring scheme. The model calibrates a few input variables including initial cash withdrawals, bank idiosyncratic initial failure rates, allocated proportions of cash withdrawals, and a contagion market factor of bank runs, and it concludes with designated measures for the overall strength of a banking system and for the expected time until a banking system failure, as predefined by policymakers. We also illustrate the functionality of the model along three hypothetical banking systems and further deploy numerical examples and sensitivity analyses, which reveal that initial bank failure rates play a bigger role in the propagation of bank runs than the contagion market factor. (c) 2021 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

We examine anomalies in the S&P500 index, an equity-based proxy for the U.S. economy, from January 1957 until December 2018. We use the LOcally wEighted Scatterplot Smoothing (LOESS) nonlinear regression model with various smoothing degrees and identify high and low extreme values in the S&P500 index upon contrasting it with nine U.S. macroeconomic indicators. We find that high and low anomalies occur with cyclicality patterns with respect to the production rate, the inflation rate, the U.S. workforce, and the private consumption rate. A sharp distinction between earlier low anomalies and later high anomalies arises with respect to the interest rate and the U.S. trade price balance. Unusual recent high anomalies appear, however, with respect to the U.S. currency, the market sentiment, and the unemployment rate. We detect robust concentration of high economic anomalies in the S&P500 index (42 in the year of 2017 and 74 in the year of 2018) along eight (out of the nine) macroeconomic indicators. This realization can serve as a warning sign for market participants. (C) 2019 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

The author presents an actuarial prognostic model that can disentangle and assess two complementary modules of longevity risk for death bonds. He tracks the probable paths over time of the transition rates for life settlement issuers across three states of nature: healthy, terminally ill, and dead. Through that, he is able to examine the influence of some varying assumptions on the likely course of the inclusive death intensity for an underlying pool of life settlements. The author is thus able to enhance transparency in this market by better predicting the overall yield curves of death bonds and their feasible trajectories. The proposed model can assist life settlement providers, brokers, investors, and credit raters in planning and preparing for possible evolutions in the overall death intensity among pools of life settlement issuances. TOPICS:Retirement, fixed income and structured finance, risk management

PurposeThis study aims to extend the literature by exploring the degrees of integration of both fixed and adjustable mortgage rates and diverse riskless (Treasury) and risky (corporate) interest rates in the capital markets from January 1, 2010, until November 7, 2018. This period is uniquely characterized by a sharp conversion on January 20, 2017, from enhanced financial regulation during the Obama administration to major deregulatory ambitions during the first 22 months of the Trump administration.Design/methodology/approachThe authors use the augmented Dickey and Fuller and the Phillips and Perron unit root tests to examine time series stationarity and the Johansen cointegration rank and the Stock-Watson common trends tests to inspect various cointegrations and regressions of time series pairs to explore different effects. The authors deploy these techniques over the entire time frame, as well as for distinct sub-periods of similar length.FindingsThe authors conclude that a deregulatory setting favors cointegration between mortgage and non-corporate capital markets. However, an enriched regulatory environment supports cointegration between mortgage and corporate capital markets. In addition, the Dodd-Frank Wall Street Reform and Consumer protection Act from July 21, 2010, created a unique though short-term effect on the relationships between Treasury and corporate bonds and fixed-rate mortgages.Practical implicationsThe journey contributes to the overall understanding of the interactions among US financial markets. They are considered efficient, competitive and fully developed if their prices quickly adjust to economic changes and regulatory transformations.Originality/valueThe authors study the degrees of integration of various conventional and adjustable mortgage rates and different fixed and floating interest rates in the US capital markets from January 1, 2010, until November 7, 2018. This recent time frame has yet to be examined in the economic literature. This period is also characterized by a sharp transformation on January 20, 2017, from enhanced financial regulation during the Obama administration to major deregulatory drives during the first 22 months of the Trump administration.

In this study, we develop and demonstrate a universal framework for supervisory stress tests of financial institutions that considers the probable dependencies among macroeconomic shocks and possible regulatory intervention. The proposed differential equations model can assess the combined influence of related shocks in various markets and economic attributes on banks’ excess capital beyond minimum regulatory ratios. The suggested model allows policy makers to implement sensitivity analyses, which reveal how an examined bank’s excess capital would react to diverse economic shocks with a wide range of varying intensities. Our model can further assess the likely impact of regulatory intervention at different magnitudes and at various points in time. It can therefore help regulators to select the optimal intervention in different economic settings.

In this study we explore the heterogeneous noncompliance behavior among ten member nations of the Organization of the Petroleum Countries (OPEC). Overall we find that the three top-oil-producers (Saudi Arabia, Iran, and Venezuela) and the four relatively low-oil-producers (Libya, Indonesia, Algeria, and Qatar) exhibit higher cheating frequencies and magnitudes, while they tend to offset their peers' conduct. Conversely, the three mid-oil-producers (UAE, Nigeria, and Kuwait) display much lower frequencies and magnitudes of noncompliance with OPEC's administered quotas, while they largely demonstrate herding behavior among their peers. The volatilities of the respective cheating magnitudes are rather similar across the three clusters though. This study contributes to the economic literature by further illuminating the operational mode of OPEC, and by exposing its three main subsets, its interdependent structure, and its members' customary behavioral patterns.

Credit agencies periodically change their ratings for corporate bonds. These rating modifications advance under different circumstances and occur at distinct rates. In this study, the author examines the volumes, frequencies, and likelihoods of credit rating changes as issued by Standard & Poor’s, Moody’s, and Fitch Ratings. He examines these subsequent rating modifications (both the first and the second recorded changes post the initial ratings) with a perspective of upgrades versus downgrades. Overall, he identifies a much greater tendency for rating downgrades than upgrades following new issuance of corporate debt, which suggests that the initial credit ratings are too lenient. This study, therefore, has potential inferences for fixed-income market participants, who should consider discounting reported credit ratings, at least to some degree.

PurposeThe purpose of this paper is to analyze the differences between the actual mortgage prompt and late payments and their respective expected measures from 2004 to 2010 to spot early symptoms of housing crisis.Design/methodology/approachThis paper explores these discrepancies across the entire US market and along various delinquency lengths of 30, 60 and 90 days. This paper constructs a Bayesian forecasting model that relies on prior distributional properties of diverse time horizons.FindingsAbnormal mortgage delinquency rates are identified in real time and can be served as early symptoms for housing crisis.Practical implicationsThe statistical scheme proposed in this paper can function as a valuable predictive tool for lending institutions, bank audit companies, regulatory bodies and real estate professional investors who examine changes in economic settings and trends in short sale leads.Social implicationsThe abnormal mortgage delinquencies can serve as indicators of changes in economic fundamentals and early signs of a mounting housing crisis.Originality/valueThis paper presents a unique statistical technique in the context of mortgage delinquencies.

Purpose - This paper aims to analyze how political brinkmanship impacted Treasury yields during the debt ceiling debate in 2015. The results show that the resignation of the House Speaker John A. Boehner caused a significant decrease in Treasury bill yields of one- and three-month maturities. The authors robust analysis indicates that these lower yields have saved US taxpayers several billion dollars in extra tax expenses. This paper provides evidence that lack of political brinkmanship can be very advantageous for the taxpayers. This has considerable implications for lawmakers in this post-election year.Design/methodology/approach - The authors examine the differences in yields between equal maturity short-term Treasury securities and commercial paper using t- tests, non- parametric tests and a robust regression model based on earlier empirical studies.Findings - This study provides evidence indicating that between September 25, 2015, and up to October 30, 2015, relatively lower Treasury yields resulted from the lack of political brinkmanship, and this has saved the US taxpayers several billion dollars in interest expenses in 2015. Research limitations/implications - The study showed that lower yields will result from a lack of political brinkmanship, and this resulted in savings of several billions of dollars in interest payments. Considering that both the White House and Congress will be controlled by the same political party, this gives lawmakers a unique opportunity to have less acrimonious debt ceiling debates. The limitation of the study is that it does not consider the impact on foreign exchange markets and other factors which could play a major role.Practical/implications - Unlike earlier scenarios where default risk increased, followed by credit rating downgrades, there was a quiet confidence this time about a quick resolution. Markets were stable, and this allowed money market participants to invest more confidently even when an upcoming debt ceiling debate is on. Corporations that invested additional cash in money markets for short- term could do it more confidently at that time without fear of default or interest rate risk which could potentially harm the market value of their investments.Practical/implications - It implies that there will be lower taxpayer costs because of debt ceilings and avoidance of shutdowns of the federal government. It also implies that there could be more confidence in the dollar.Originality/value - Several earlier studies have examined Treasury default caused by political brinkmanship. This is the first study to examine an event where political brinkmanship appeared possible and then suddenly dissipated in a single day. Political brinkmanship is bad news because it increases taxpayer interest burden as seen from several of the studies above. Therefore, it should be considered good news if no disagreement is evident. This argument serves as our motivation for this paper. As an increase in the chances of default causes an increase in the yield of Treasury bills as earlier studies showed, a decrease in the chance of default caused Treasury bill yields to be that much lower based on the results of this study.

In this study, we develop analytic derivations for the projected mean and median times to default for high-yield bonds that have been previously downgraded from investment-grade ratings. These predictive measures are calibrated for both the respective times already spent in the group of high credit grades and the inherent dependency between the observed times in the investment and the speculative classes. We also document an inverse historical relation between the time spent in the group of investment-grade ratings and the noninvestment class. Furthermore, we calculate both the mean and the median times to default for numerous junk bonds in their diverse life cycles. Finally, we ide.tify the most influential accounting and market ratios that can explain the life expectancies of fallen angels. The framework presented hereafter aims to benefit high-yield debt investors. TOPICS:Fixed income and structured finance, statistical methods