Linear Programming model is an important tool used to solve constrained optimization problems. In fact, the real life problems are usually occurring in the presence of uncertainty. For instance, in managerial problems of assigning employees to different tasks with the aim of minimizing the total...

This paper proposes a decision-theoretic framework for experiment design. We model experimenters as ambiguity-averse decision-makers, who make trade-offs between subjective expected performance and robustness. This framework accounts for experimenters' preference for randomization, and clarifies...

Dealing with supply risks is one of the challenges of decision makers in supply chains as producing and sourcing become more and more complex. Theoretical research on different types of supply uncertainty as well as their management is well covered. Behavioral aspects in this context, however,...

This paper presents some new stochastic dominance (SD) criteria for ranking transformations on a random variable, which is the first time that this is done for transformations under the discrete framework. By using the expected utility theory, the authors first propose a sufficient condition for...

This paper develops some new stochastic dominance (SD) rules for ranking transformations on a random variable, which is the first time to study ranking approach for transformations on the discrete framework. By using the expected utility theory, the authors first present a sufficient condition...

Foster and Hart proposed an operational measure of riskiness for discrete random variables. We show that their defining equation has no solution for many common continuous distributions. We show how to extend consistently the definition of riskiness to continuous random variables. For many...

This article deals with approximation of empirical distribution to standard normal distribution using Johnson transformation. This transformation enables us to approximate wide spectrum of continuous distributions with a normal distribution. The estimation of parameters of transformation...

This paper presents an integrated framework for assessing systemic risk. The framework models banks’ capital asset ratios as a function of future losses and credit growth using a generalized method of moments to calibrate shocks to credit quality and credit growth. The analysis is...

This paper formulates a novel modeling framework that delivers: (a) forecasts of indicators of systemic real risk and systemic financial risk based on density forecasts of indicators of real activity and financial health; (b) stress-tests as measures of the dynamics of responses of systemic risk...

The level of a bank‘s capitalization can effectively transmit information about its riskiness and therefore support market discipline, but asymmetry information may induce exaggerated or distortionary behavior: banks may vie with one another to signal confidence in their prospects by...

I consider a situation, where the agent can acquire payoff-relevant information either before or after the contract is signed. To raise efficiency, the principal might solicit information; to retain all surplus, however, she must prevent precontractual information gathering. The following class...

This paper applies He's homotopy perturbation method to compute a large variety of integral transforms. As illustration, the paper gives special attention to the Esscher transform, the Fourier transform, the Hankel transform, the Mellin transform, the Stieljes transform and some applications.

We study a model where investment decisions are based on investors’ information about the unknown and endogenous return of the investment. The information of investors consists of endogenously determined messages sold by financial analysts who have access to both public and private...

We apply a hidden Markov model of the term structure to modeling the Brazilian swap rate curve. We examine the model's characteristics and its performance in describing the cross-sectional and time-series dynamics of the term structure. Two regimes are identified, a high level and a high...

The paper investigates how changes in industries' funding costs affect total factor productivity (TFP) growth. Based on panel regressions using 31 U.S. and Canadian industries between 1991 and 2007, and using industries' dependence on external funding as an identification mechanism, we show that...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation...

In this paper, several identities concerning expectation, variance, covariance, cumulative distribution functions, the coefficient of variation, and the Lorenz curve are obtained and they are used in establishing theoretical results. Furthermore, a graphical representation of the variance is...

Affiliation has been a prominent assumption in the study of economic models with statistical dependence. Despite its large number of applications, especially in auction theory, affiliation has limitations that are important to be aware of. This paper shows that affiliation is a restrictive...

We develop a new methodology to estimate the importance of herd behavior in financial markets: we build a structural model of informational herding that can be estimated with financial transaction data. In the model, rational herding arises because of information-event uncertainty. We estimate...

Building on the widely-used double-lognormal approach by Bahra (1997), this paper presents a multi-lognormal approach with restrictions to extract risk-neutral probability density functions (RNPs) for various asset classes. The contributions are twofold: first, on the technical side, the paper...