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Purpose: Previous research identified a measurement gap in the individual assessment of social misconduct in the workplace related to gender. This gap implies that women respond to comparable self-reported acts of bullying or sexual discrimination slightly more often than men with the self-labeling as “bullied” or “sexually discriminated and/or harassed.” This study tests this hypothesis for women and men in the scientific workplace and explores patterns of gender-related differences in self-reporting behavior.

Basic design: The hypotheses on the connection between gender and the threshold for self-labeling as having been bullied or sexually discriminated against were tested based on a sample from a large German research organization. The sample includes 5,831 responses on bullying and 6,987 on sexual discrimination (coverage of 24.5 resp. 29.4 percentage of all employees). Due to a large number of cases and the associated high statistical power, this sample for the first time allows a detailed analysis of the “gender-related measurement gap.” The research questions formulated in this study were addressed using two hierarchical regression models to predict the mean values of persons who self-labeled as having been bullied or sexually discriminated against. The status of the respondents as scientific or non-scientific employees was included as a control variable.

Results: According to a self-labeling approach, women reported both bullying and sexual discrimination more frequently. This difference between women and men disappeared for sexual discrimination when, in addition to the gender of a person, self-reported behavioral items were considered in the prediction of self-labeling. For bullying, the difference between the two genders remained even in this extended prediction. No statistically significant relationship was found between the frequency of self-reported items and the effect size of their interaction with gender for either bullying or sexual discrimination. When comparing bullying and sexual discrimination, it should be emphasized that, on average, women report experiencing a larger number of different behavioral items than men.

Interpretation and relevance: The results of the study support the current state of research. However, they also show how volatile the measurement instruments for bullying and sexual discrimination are. For example, the gender-related measurement gap is considerably influenced by single items in the Negative Acts Questionnaire and Sexual Experience Questionnaire. The results suggest that women are generally more likely than men to report having experienced bullying and sexual discrimination. While an unexplained “gender gap” in the understanding of bullying was found for bullying, this was not the case for sexual discrimination.

The purpose of this paper is to investigate the role of financial advice on investor trading behavior by analyzing the influence of advisor personality.

The study utilized the Big Five personality framework from Costa and McCrae (1992) to measure personality traits of advisors and examined the data collected from 314 stock investor–advisor dyads. Personality traits of advisors were measured by the NEO-Five Factor Inventory (Costa and McCrae, 1989). Confirmatory factor analysis was conducted to assess the fitness of the Big Five model. We followed two-stage least square method for estimating endogenous covariate by employing instrumental variable analysis. Probit model was used to evaluate the moderating influence of advisor personality traits on the association between the usage of financial advice and trading behavior.

The authors found that financial advice positively impacts investors’ stock trading frequency. The authors also provide empirical evidence that financial advice is more likely to increase trading frequency when advisor personality tends to be openness, conscientiousness and agreeableness. On the other hand, information acquired from financial advisors causes fewer adjustments in investors’ portfolios when the personality of advisors is likely to be extraverted and neurotic.

The theoretical model in our study seeks to explain that a psychological factor, namely, advisor personality, influences the way an investor interprets information signals from financial advice, which, in turn, influences the investor’s decision to trade in securities.

This research suggests that characteristics of advisors other than those of investors can be of relevance for policy makers in their attempts to improve their business in the financial services industry.

Survey-based studies in finance are lacking. This study adds to the existing literature of behavioral finance that accounts for the observed variations in investors’ financial decision making explained by psychological factors. No previous study has been conducted so far exploring variations in the impact of financial advice on investors’ stock trading behavior by the Big Five advisor personality, and this paper strives to fill this research gap in Chinese stock market.

This paper aims to focus on the inclusion of the frequency behavior of grounding system effect on surge arrester (SA) model parameters’ estimation.

The grounding system impedance and its frequency behavior are the factors that have influence on the SA performance. Up to now, the grounding system impedance effect and the frequency behavior of the soil parameters have not been studied for the estimation of the parameters of the SA frequency-dependent model. In this paper, the grounding system’s influence on the SA dynamic model has been simulated for rod- and counterpoise-shaped electrodes. Particle swarm optimization with a grey wolf optimization algorithm has been implemented as an optimization algorithm to adjust the parameters of the SA dynamic model.

The results show that the frequency behavior of the grounding impedance and soil electrical parameters can impress the optimum parameters of the SA frequency-dependent model and should be considered for more reliable results. Also, the results evidence that the proposed optimization method provides more accurate results compared to other optimization methods.

To the best of the authors’ knowledge, this work is one of the first attempts to investigate the effect of frequency grounding system on SA frequency-dependent model parameters.

The main goal of this paper is to investigate whether there is long-memory behavior in the CBOE Brazil ETF volatility index (named here VIXBR). As structural breaks may create a spurious long-range dependence, the presence of structural breaks is also gauged.

The study considers the period from October 2011 to March 2021, using daily data. To test the long-memory behavior, three empirical approaches are adopted: GPH, ELW and robust GPH (RGPH) estimator. To estimate the structural break points adopted to date the subsamples, the ICSS algorithm is used.

Results considering the total period (TP) and subsamples show that the breaks did not create a spurious long-memory behavior and together with the rolling estimation, reveal strong evidence of the long-range dependence in the CBOE Brazil ETF volatility index. The higher degree of persistent of the VIXBR series suggests an extended period of increased uncertainty that agents need consider when making their investment decision.

As possible extension of this study is to investigate the behavior of long memory and structural breaks for different frequencies (weekly, monthly, among others).

The presence of long-range dependence in the CBOE Brazil ETF volatility index reveals that the past information is important for the predictability of risks, and therefore, can help to protect against market risks, which has important implications regarding the future decisions of economic agents (for example, policy makers and investors).

Brazil is an emerging capital market (ECM) that has attracted a great deal of attention from investors and investment funds seeking to diversify its assets. This paper contributes to the empirical financial literature, by studying the long-memory behavior of the CBOE Brazil ETF volatility index, considering possible structural breaks. To the best of knowledge, this has not been done so far.

Classroom management is a prerequisite for effective instruction, yet research indicates that not all teachers implement evidence-based classroom management skills (CMS) in their classroom. Therefore, efficient professional development models are necessary to increase teachers’ use of CMS, but those models are predicated on valid and reliable screening tools to identify teachers CMS performance. This study is a psychometric evaluation of a direct observation CMS screening tool for elementary school teachers that can be used as part of a targeted CMS professional development model. Based on a three-facet generalizability study, the primary source of variance across observations was differences among teachers and differences across observations. A decision study was conducted and indicates that a generalizable estimate from the CMS screening tool requires four 30-min observations. These results are compared with prior research and recommendations for future research are discussed.

The chapter considers parameter identification, estimation, and model diagnostics in medium scale DSGE models from a frequency domain perspective using the framework developed in Qu and Tkachenko (2012). The analysis uses Smets and Wouters (2007) as an illustrative example, motivated by the fact that it has become a workhorse model in the DSGE literature. For identification, in addition to checking parameter identifiability, we derive the non-identification curve to depict parameter values that yield observational equivalence, revealing which and how many parameters need to be fixed to achieve local identification. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably different parameter values and impulse response functions. A further comparison between the nonparametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture. Overall, the results suggest that the frequency domain based approach, in part due to its ability to handle subsets of frequencies, constitutes a flexible framework for studying medium scale DSGE models.

To investigate if online TV content platforms create value for consumers (and increase use) by offering its users the possibility to self-invest in the service (by giving personal content preferences). We link demographic and attitudinal antecedents to the relation between self-investment and use.

Data were collected together with a Belgian media company (N = 4,136). To test the effects a latent growth model was composed in a multigroup setting with gender as the grouping variable. The model is analyzed through structural equation modeling in Mplus 8.0.

In general, strong relations between self-investment and increased use were found, although the effect of self-investment on use was stronger for female consumers. Furthermore, we established strong hedonic effects on using and investing in the service. For men, easy to use platforms lead to less self-investment.

Our findings are restricted to free services. Furthermore, attitudinal variables are antecedents of behaviors. However, a more complex interplay between behavioral and attitudinal variables is possible. Further research could use repeatedly measured attitudinal measures and link these to behaviors over time.

Service developers could offer different platform interactions to different segments to create consumer value. Women seem more receptive for extra functionalities, such as the possibility to indicate preferences. Men mainly focus on the content offered.

This study focuses on a new form of media distribution, online TV content platforms, where we investigate two related behaviors of users over time (self-investment and use) instead of a general approximation of use. Multi-source data were used.

The purpose of this paper is to investigate and compare the extreme behavior of securitized real estate and stock market returns as well as their value‐at‐risk (VaR) dynamics in international investing. Extreme value theory using the block maxima method is applied to ten securitized real estate and equity market indices representing Asian, European and North American markets.

The paper models the maxima and minima of all return series within the extreme value theory (EVT) framework and derive the VaR estimates. It then compares the VaR estimates derived from the EVT and the normal distribution and investigates the impact of clustered returns on the VaR estimates. Finally, both the conventional standard deviation measure and VaR method are conducted to evaluate and compare the impact of the Asian financial turmoil on the real estate and stock market risk profiles.

Evidence shows that Asian real estate and equity maxima and minima return series are characterized by a fat‐tailed Fréchet distribution. The frequency and severity of extreme Asian real estate returns are greater than their European and North American counterparts. Securitized real estate markets are riskier than the broader stock markets before and during the Asian financial turmoil. In contrast, many stock markets become riskier after the financial crisis with their VaRs higher than the equivalent VaR estimates for the real estate series.

Knowledge about real estate market returns exhibit extreme behavior can help investors and fund managers understand the distribution of real estate market returns better and obtain potentially more accurate real estate return forecasts.

International real estate portfolio risk management should include both extreme risks and standard deviations. Accordingly, global investors should be even more cautious in formulating their diversification strategies since gains from diversification can be reduced significantly by the severity of extreme return levels.

The paper characterizes the distribution of extreme returns for a broad spectrum of international securitized real estate markets from three continents. The extreme value investigation is also conducted for broader stock markets corresponding to the individual real estate markets. The July 1997 turmoil that occurred in Asian financial markets provides interesting exploratory opportunities within which this paper estimates and compares the extreme market risk with the conventional standard deviation measure.