Small Theories and Large Risks—Is Risk Analysis Relevant for Epistemology?

Ought we to take seriously large risks predicted by “exotic” or improbable theories? We routinely assess risks on the basis or either common sense, or some developed theoretical framework based on the best available scientific explanations. Recently, there has been a substantial increase of interest in the low‐probability “failure modes” of well‐established theories, which can involve global catastrophic risks. However, here I wish to discuss a partially antithetical situation: alternative, low‐probability (“small”) scientific theories predicting catastrophic outcomes with large probability. I argue that there is an important methodological issue (determining what counts as the best available explanation in cases where the theories involved describe possibilities of extremely destructive global catastrophes), which has been neglected thus far. There is no simple answer to the correct method for dealing with high‐probability high‐stakes risks following from low‐probability theories that still cannot be rejected outright, and much further work is required in this area. I further argue that cases like these are more numerous than usually assumed, for reasons including cognitive biases, sociological issues in science and the media image of science. If that is indeed so, it might lead to a greater weight of these cases in areas such as moral deliberation and policy making.     

INTRODUCTION TO THE SYMPOSIUM ON CONTEMPORARY BANKING RESEARCH: THE USE OF FIXED EFFECTS TO DISENTANGLE LOAN DEMAND FROM LOAN SUPPLY

With the onset of the financial crisis, disentangling the effects of loan demand and supply in contemporary banking research has become vital for a proper assessment of supply-related banking shocks. These shocks may negatively affect the real economy through many channels, such as the lending channel of monetary policy transmission, the bank risk-taking channel or the evaluation of macroprudential policy efficiency. All these rely on separating the two lending components. Empirical identification has largely relied on the use of demand-related fixed effects, which has also been applied in several analyses within this symposium. (JEL G21)     

A Bivariate Beta-Gamma Autoregressive Process (BVBGAR(1))

In this article, we introduce a bivariate autoregressive process with Gamma marginal distributions using the form of the BGAR(2) process (Risti?, 2005 Risti? , M. M. ( 2005 ). A Beta-Gamma autoregressive process of the second-order (BGAR(2)) . Statist. Probab. Lett. 73 : 403 – 410 . [Google Scholar]) and the Beta-Gamma transformation. Some properties of the process such as the autocovariance matrix, the autocorrelation matrix, and the spectral density matrix are derived. The unknown parameters of the process are estimated using the method of moments and the method of conditional least squares. Some numerical results of the estimators are given. We investigate nonparametric and parametric estimation of the spectral density matrix of this process.     

Testing Goodness of Fit for Parametric Families of Copulas—Application to Financial Data

ABSTRACT

This article suggests a chi-square test of fit for parametric families of bivariate copulas. The marginal distribution functions are assumed to be unknown and are estimated by their empirical counterparts. Therefore, the standard asymptotic theory of the test is not applicable, but we derive a rule for the determination of the appropriate degrees of freedom in the asymptotic chi-square distribution. The behavior of the test under H ₀ and for selected alternatives is investigated by Monte Carlo simulation. The test is applied to investigate the dependence structure of daily German asset returns. It turns out that the Gauss copula is inappropriate to describe the dependencies in the data. A t _ν-copula with low degrees of freedom performs better.     

A geometric time series model with a new dependent Bernoulli counting series

ABSTRACT

New generalized binomial thinning operator with dependent counting series is introduced. An integer valued time series model with geometric marginals based on this thinning operator is constructed. Main features of the process are analyzed and determined. Estimation of the parameters are presented and some asymptotic properties of the obtained estimators are discussed. Behavior of the estimators is described through the numerical results. Also, model is applied on the real data set and compared to some relevant INAR(1) models.     

Work stressors and cardiovascular disease

Over the past 20 years, an extensive body of research evidence has documented that psychosocial work stressors are risk factors for hypertension and cardiovascular disease. These stressors, which appear to be increasing in prevalence, include job strain (the combination of psychological job demands and low job control), imbalance between job efforts and rewards, threat-avoidant vigilant work, and long work hours. This article reviews the evidence linking these stressors with hypertension and CVD, and the physiological and social psychological mechanisms underlying the associations. Also described are methods for measuring work stressors and new, more accurate techniques for measuring blood pressure. Finally, strategies for reducing work stressors and preventing hypertension and CVD are reviewed. These include clinical assessment, worksite health promotion, work organization interventions, legal approaches and work site surveillance.     

Religiosity as a predictor of students' political attitudes

This paper analyzes the relationship between students' level of actual religiosity and their sociodemographic characteristics, political orientation, and political attitudes. The paper is based on an online survey conducted at the University of Rijeka (N = 624) in 2021. The vast majority of our respondents (90%) received the three holy sacraments of initiation in their early childhood and attended Catholic religious education at school. The explanation of the significantly less actual religiosity of students is approached from the position of Pickel's contextualized theory of secularization. Relying on Voas and Day, a composite variable distinguishes highly religious students, moderately religious students, weakly religious students, and non-religious students on the basis of the respondents' positioning toward religious self-identification, beliefs, Church attendance, and the importance of religion in one's life. Statistically significant correlations between actual religiosity and political orientation were established. Very religious Catholic students, who are the least numerous and the least homogenous category, are more inclined to right-wing political orientation and reject ethno-nationalism/anti-multiculturalism less and accept clericalism more than other categories of students. In a broader sense, this study reveals that a large number of respondents distance themselves from religion and the Church at student age despite their experience of formal religious socialization at school age. Furthermore, the results suggest that the synergy of the liberalizing effect of education and the tolerant sociocultural atmosphere of an area reduces the influence of religiosity on the political attitudes and orientation of students.