Search

In the Fundamental Review of the Trading Book (FRTB), the latest regulation for minimum capital market risk requirements, one of the major changes, is replacing the Incremental Risk Charge (IRC) with the Default Risk Charge (DRC). The DRC measures only the default and does not consider the migration rating risk. The second new change in this approach was that the DRC now includes equity assets, contrary to the IRC. This paper studies DRC modeling under the Internal Model Approach (IMA) and the regulator conditions that every DRC component must respect. The FRTB presents the DRC measurement as Value at Risk (VaR) over a one-year horizon, with the quantile equal to 99.9%. We use multifactor adjustment to measure the DRC and compare it with the Monte Carlo Model to understand how the approach fits. We then define concentration in the DRC and propose two methods to quantify the concentration risk: the Ad Hoc and Add-On methods. Finally, we study the behavior of the DRC with respect to the concentration risk. [ABSTRACT FROM AUTHOR]

We offer some thoughts on the relation between theoretical and empirical accounting research in the context of causal inference, in response to two questions posed by Professor Ivan Marinovic, organizer of the 2014 Stanford University Graduate School of Business Causality conference. The two questions are: should causal inference be the objective of accounting research; and what is, and what should be, the relation between theory and empirical research in accounting? With regard to the latter, we point to two sources of difficulty: (1) confusion and disagreement about interpretation, advantages and disadvantages of various empirical identification strategies; and (2) a lack of progress on the part of empirical researchers in testing the implications of existing accounting theories and thereby providing discipline to those theories. We argue that published empirical accounting research relies too much on insufficiently precise verbal models or generic models that provide few or no new accounting-specific insights and tends to ignore recent advances made by theoretical researchers. As a result analytical models in accounting research are not sufficiently challenged by empirical research and analytical researchers have made slow progress in establishing a meaningful distinction between accounting information and other types of information provided by firms and their managers. Our concern is that accounting research is in danger of losing the healthy disciplining balance between theory and empirical research that is essential to any scientific field. Without this balance, the profession becomes a discipline of beliefs, rather than a discipline of scientific discovery. [ABSTRACT FROM AUTHOR]

(1) Background: The aim of the study is to develop a set of models for managing a fleet of complex technical systems with metrological support, allowing the simulation and management at all the stages of the life cycle of the complex technical systems, as well as to simulate the functioning of large fleets of complex technical systems, including up to several hundred thousand samples; (2) Methods: The authors use methods of mathematical modeling, methods of the theory of Markov and semi-Markov processes, methods of optimization, methods of reliability theory, and methods of probability theory and mathematical statistics; (3) Results: an interconnected set of mathematical models for managing a fleet of complex technical systems with metrological support was developed and the applied software was developed; (4) Conclusions: The set of models presented in the article allows for the adequate simulation of all the stages of the life cycle of large complex technical systems fleets, including up to several hundreds of thousands of samples, to optimize the functioning processes of a fleet of complex technical systems, to form strategies for fleet development, and to assess the risks associated with false and undetected failures, as well as the risks associated with the degradation of complex technical systems. [ABSTRACT FROM AUTHOR]

Matching estimators are commonly used to estimate causal effects in nonexperimental settings. Covariate measurement error can be problematic for matching estimators when observational treatment groups differ on latent quantities observed only through error-prone surrogates. We establish necessary and sufficient conditions for matching and weighting with functions of observed covariates to yield unconfounded causal effect estimators, generalizing results from the standard (i.e., no measurement error) case. We establish that in common covariate measurement error settings, including continuous variables with continuous measurement error, discrete variables with misclassification, and factor and item response theory models, no single function of the observed covariates computed for all units in a study is appropriate for matching. However, we demonstrate that in some circumstances, it is possible to create different functions of the observed covariates for treatment and control units to construct a variable appropriate for matching. We also demonstrate the counterintuitive result that in some settings, it is possible to selectively contaminate the covariates with additional measurement error to construct a variable appropriate for matching. We discuss the implications of our results for the choice between matching and weighting estimators with error-prone covariates. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

An essay is presented which deals with hypothesis testing in mathematical statistics. Topics discussed include the main reasons why it is easy to lie with statistics, the three kinds of lies, and an analysis of the frequentists' approach and the Bayesian method. Also explored are the Bayesian reference criterion (BRC) and Lindley's paradox.