Links between Aggressive Sexual Fantasies and Sexual Coercion: A Replication and Extension of a Multifactorial Model

Current research indicates that aggressive sexual fantasies (ASF) are related to sexual aggression, above and beyond other risk factors for this behavior. There have, however, rarely been explicitly considered in multifactor models aiming to explain sexual aggression. One exception is the multifactorial Revised Confluence Model of Sexual Aggression that was replicated in two samples of male individuals who were convicted of sexual offenses and a small sample of men from the general population and evidenced a high relevance of ASF, respectively. There were, however, no further attempts to replicate the model in larger samples from the general population. We, therefore, used a subsample from the Finnish Genetics of Sexuality and Aggression project including 3269 men (age: M = 26.17 years, SD = 4.76) to do so. Cross-sectional latent structural equation models corroborated previous research and the assumption that ASF are a central component in multifactor models that aim to explain sexual aggression: ASF and antisocial behavior/aggression were equally important associates of sexual coercion when also considering adverse childhood experiences, hypersexuality, and callous-unemotional traits. Additionally, ASF mediated the links between hypersexuality, callous-unemotional traits, as well as childhood sexual abuse and sexual coercion. These links held stable when entering further risk factors, that is, distorted perceptions, rape-supportive attitudes, and violent pornography consumption into the model. Contrasting assumptions, alcohol consumption and antisocial behavior/aggression did not interact. These results illustrate the potential importance of ASF for sexual aggression. They indicate that ASF require consideration by research on sexual aggression as well as in the treatment and risk assessment of sexual perpetrators.

A fuzzy multifactor asset pricing model

This paper introduces a new approach of multifactor asset pricing model estimation. This approach assumes that the monthly returns of financial assets are fuzzy random variables and estimates the multifactor asset pricing model as a fuzzy linear model. The fuzzy random representations allows us to incorporate bias on prices induced by the market microstructure noise and to reflect the intra-period activity in the analysis. The application of fuzzy linear regression enables the uncertainty assessment in an alternative way to confidence interval or hypothesis testing, which is subjected the binding assumption of normal distribution of returns. However, it is well known that the distribution of many asset returns deviates significantly from the normal assumption. We illustrate this estimation in the particular case of the Fama and French's (J Financ Econ 33:3-56, 1993) three factor model. Finally, empirical studies based on Fama and French's portfolios and risk factors, historical dataset highlight the effectiveness of our estimation method and a comparative analysis with the ordinary least square estimation shows its ability to be applied for an optimal decision decision making in the financial market.