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In this study, we ask whether economic factors that can be directly manipulated by public policy have important effects on the probability that women experience long-lasting unions. Using data from the 1979 National Longitudinal Survey of Youth, we estimate a five-stage sequential choice model for women's transitions between single with no prior unions, cohabiting, first-married, re-single (divorced or separated), and remarried. We control for expected income tax burdens, Aid to Families with Dependent Children (AFDC) or Temporary Assistance for Needy Families (TANF) benefits, Medicaid expenditures, and parameters of state divorce laws, along with an array of demographic, family background, and market factors. We simulate women's sequences of transitions from age 18 to 48 and use the simulated outcomes to predict the probability that a woman with given characteristics (a) forms a first union by age 24 and maintains the union for at least 12 years, and (b) forms a second union by age 36 and maintains it for at least 12 years. While non-policy factors such as race and schooling prove to have important effects on the predicted probabilities of long-term unions, the policy factors have small and/or imprecisely estimated effects; in short, we fail to identify policy mechanisms that could potentially be used to incentivize long-term unions.

The Austrian economist Ludwig Lachmann claimed that Keynes was a lifelong subjectivist. To evaluate this, we start by distinguishing Keynes’ writings on probability theory from his writings on economics. In the General Theory (1936), Keynes’ treatment of expectations provides the basis for Lachmann’s view that Keynes was a subjectivist at heart. In his Treatise on Probability (1921), Keynes refers explicitly to the subjectivism–objectivism divide in probability theory and pins his colors to the objectivist mast. In this essay, we present the objectivist slant in Keynes’ earlier writings on probability theory. Thereafter, we evaluate the criteria Lachmann employed to cast Keynes as a subjectivist.

Regulators can adjust penalties to compensate for incomplete monitoring of regulated parties that are subject to legal rules, but compensating penalty adjustments often are unavailable when regulated parties are subject to legal standards. Incomplete monitoring consequently invites greater noncompliance under standards than under rules. This chapter develops a model that quantifies some of the specific tradeoffs that regulators face in designing standards regimes under incomplete monitoring. The model also considers the extent to which suboptimal compliance due to incomplete monitoring is likely to result in deadweight loss in different settings.

The evaluation of competing hypotheses is an essential aspect of the audit process. The method of evaluation and re-evaluation may have implications for both efficiency and effectiveness. This paper presents the results of a field experiment using a case study set in the context of a fraud investigation in which practicing auditors were required to engage in multiple hypothesis probability estimation and revision regarding the perpetrator of the fraud. The experiment examined the effect of two different methods of facilitating multiple hypothesis probability estimation and revision consistent with the completeness and complementarity norms of probability theory as it applies to the independence versus dependence of competing hypotheses and with the prescriptions of Bayes' Theorem. The first method was to have participants use linear probability elicitation scales and receive prior tutoring in probability theory emphasizing the axioms of completeness and complementarity. The second method was to provide a graphical decision aid, without prior tutoring, to aid the participants in expressing their responses. A third condition in which participants used linear probability elicitation scales but received no tutoring in probability theory, provided a benchmark against which to assess the effects of the two treatments.

Participants receiving prior tutoring in probability theory and using linear probability elicitation scales complied in their estimations and revisions with the probability axioms of completeness and complementarity. However, they engaged in frequent violations of the normative probability model and of Bayes' Theorem. They did not distribute changes in the probability of the target hypothesis to the nontarget hypotheses, and they engaged in “eliminations and resuscitations” whereby they eliminated a suspect by assigning a zero probability to that suspect at an intermediate iteration and resuscitated that suspect by reassigning him or her a positive probability at a later iteration. The participants using the graphical decision aids, by construction, did not violate the probability axioms of completeness and complementarity. However, with no imposed constraints, the patterns of their revisions were different. When they revised the probability of the target hypothesis, they revised the probabilities of the nontarget hypotheses. They did not engage in eliminations and resuscitations. These patterns are more consistent with the norms of probability theory and with Bayes' Theorem. Possible explanations of this phenomenon are proposed and discussed, including implications for audit practice and future research.

Labor force transitions are empirically examined using Current Population Survey (CPS) data matched across months from 1996 to 2012 for Hispanics, African-Americans, and whites. Transition probabilities are contrasted prior to the Great Recession and afterward. Estimates indicate that minorities are more likely to be fired as business cycle conditions worsen. Estimates also show that minorities are usually more likely to be hired when business cycle conditions are weak. During the Great Recession, the odds of losing a job increased for minorities although cyclical sensitivity of the transition declined. Odds of becoming re-employed declined dramatically for blacks, by 2–4%, while the probability was unchanged for Hispanics.

In fraudulent conveyance cases, plaintiffs allege that by entering into a complex leverage transaction, such as an LBO, a firm’s former owners ensured its subsequent collapse. Proving that the transaction rendered the firm insolvent may allow debtors (or their proxies) to claw back transfers made to former shareholders and others as part of the transaction.

Courts have recently questioned the robustness of the solvency evidence traditionally provided in such cases, claiming that traditional expert analyses (e.g., a discounted flow analysis) may suffer from hindsight (and other forms of) bias, and thus not reflect an accurate view of the firm’s insolvency prospects at the time of the challenged transfers. To address the issue, courts have recently suggested that experts should consider market evidence, such as the firm’s stock, bond, or credit default swap prices at the time of the challenged transaction. We review market-evidence-based approaches for determination of solvency in fraudulent conveyance cases.

We compare different methods of solvency determination that rely on market data. We discuss the pros and cons of these methods and illustrate the use of credit default swap spreads with a numerical example. Finally, we highlight the limitations of these methods.

If securities trade in efficient markets in which security prices quickly impound all available information, then such security prices provide an objective assessment of investors’ views of the firm’s future insolvency prospects at the time of challenged transfer, given contemporaneously available information. As we explain, using market data to analyze fraudulent conveyance claims or assess a firm’s solvency prospects is not as straightforward as some courts argue. To do so, an expert must first pick a particular credit risk model from a host of choices which links the market evidence (or security price) to the likelihood of future default. Then, to implement his chosen model, the expert must estimate various parameter input values at the time of the alleged fraudulent transfer. In this connection, it is important to note that each credit risk model rests on particular assumptions, and there are typically several ways in which a model’s key parameters may be empirically estimated. Such choices critically affect any conclusion about a firm’s future default prospects as of the date of an alleged fraudulent conveyance.

Simply using market evidence does not necessarily eliminate the question of bias in any analysis. The reliability of a plaintiff’s claims regarding fraudulent conveyance will depend on the reasonableness of the analysis used to tie the observed market evidence at the time of the alleged fraudulent transfer to default prospects of the firm.

There is a large body of literature in financial economics that examines the relationship between market data and the prospects of a firm’s future default. However, there is surprisingly little research tying that literature to the analysis of fraudulent conveyance claims. Our paper, in part, attempts to do so. We show that while market-based methods use the information contained in market prices, this information must be supplemented with assumptions and the conclusions of these methods critically depend on the assumption made.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

Probability of Default (PD) is a crucial credit risk parameter. International accords have motivated banks and credit institutions to adopt objective systems of evaluating and monitoring the PD. This study examines retail unsecured loans of a major Greek bank during the period of the financial crisis. It focusses on the stochastic behaviour of the financial states of the loans. It is tested whether a first-order Markov chain (MC) model describes sufficiently the transitions from one state to another. Moreover, Poisson regression models are estimated in order to calculate the limiting transition matrix, the limiting state probabilities and the PD. It is proved that the MC of the financial states of loans is non-homogeneous suggesting that the transition probabilities from one financial state to another are not constant across time. From the Poisson regression models, the transition probability matrix is estimated from one state to another in alternative time periods. From the limiting transition matrix, it is shown that if a loan is delayed then it is very likely to move towards the next worst case. The findings of this research could be useful for bank management.

When the probability of each model is known, a natural idea is to select the most probable model. However, in many practical situations, the exact values of these probabilities are not known; only the intervals that contain these values are known. In such situations, a natural idea is to select some probabilities from these intervals and to select a model with the largest selected probabilities. The purpose of this study is to decide how to most adequately select these probabilities.

It is desirable to have a probability-selection method that preserves independence. If, according to the probability intervals, the two events were independent, then the selection of probabilities within the intervals should preserve this independence.

The paper describes all techniques for decision making under interval uncertainty about probabilities that are consistent with independence. It is proved that these techniques form a 1-parametric family, a family that has already been successfully used in such decision problems.

This study provides a theoretical explanation of an empirically successful technique for decision-making under interval uncertainty about probabilities. This explanation is based on the natural idea that the method for selecting probabilities from the corresponding intervals should preserve independence.

The aim of this paper is to document the effect of analyst coverage on the ex ante probability of stock price crash and the ex ante probability stock price jump.

This paper uses the data of non-financial firms from France to test the arguments presented in this paper during the period between 1997 and 2019. The paper also uses flexible quadrants copulas to compute the ex ante probabilities of crashes and jumps.

The results show that the extent of analyst coverage is positively associated with the ex ante probability of crash and negatively associated with the ex ante probability of jump. The results remain qualitatively the same after several sensitivity checks. The results also show that the relationship between the extent of analyst coverage and the probability of cash and the probability of jump holds when ex post probability of stock price crash and stock price jump is used.

Unlike most of the earlier papers on this topic, this paper uses the ex ante probability of crash and jump. This proxy is better suited than the ones used in the prior literature because it is a forward-looking measure.