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Article
Publication date: 25 September 2019

Giulio Palomba and Luca Riccetti

This paper aims to perform an analytical analysis on portfolio allocation when a tracking error volatility (TEV) constraint holds, drawing specific attention to the portfolio…

Abstract

Purpose

This paper aims to perform an analytical analysis on portfolio allocation when a tracking error volatility (TEV) constraint holds, drawing specific attention to the portfolio efficiency issue. Indeed, it is well known that investors can assign part of their funds to asset managers who are given the task of beating a benchmark portfolio. However, the risk management office often imposes a TEV constraint to the asset managers’ activity to maintain the portfolio risk near to the risk of the benchmark. This situation could lead asset managers to select non efficient portfolios in the total return and absolute risk perspective. However, the risk management office can impose further constraints, such as on maximum variance or maximum value at risk (VaR) to maintain the overall portfolio risk under control.

Design/methodology/approach

First the authors define the TEV constrained-efficient frontier (ECTF), a set of TEV constrained portfolios that are mean–variance efficient. Second, they define two new portfolio frontiers analyzing how the imposition of a maximum variance or maximum VaR restriction can reduce the ECTF. Third, they investigate the feasibility of such portfolio frontiers and their relationships.

Findings

The authors find that variance or VaR constraint can force asset managers to pursue portfolio efficiency.

Originality/value

This is a practically important issue given that asset managers often receive a constraint on TEV from the risk management office, but the risk management office does not ask them to minimize the TEV as often assumed in the optimizations performed in the literature on this topic.

Article
Publication date: 4 November 2014

Gianni Brighetti, Caterina Lucarelli and Nicoletta Marinelli

The purpose of this paper is to explore how psychological variables are related to real-life insurance consumption. Specifically, the authors focus on whether emotions and…

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Abstract

Purpose

The purpose of this paper is to explore how psychological variables are related to real-life insurance consumption. Specifically, the authors focus on whether emotions and psychological traits can improve the predictability of insurance demand, taking traditional socioeconomic variables under control.

Design/methodology/approach

The approach used was in-person survey, based on a traditional questionnaire, the Barratt Impulsiveness Scale and a psycho-physiological task (Iowa Gambling Task (IGT)).

Findings

A selective role of emotions and psychological traits has been proven to exist when comparing different insurance policies. Life and casualty insurance are affected by emotional arousal to losses; indemnity insurance by fear of the unknown, whereas health insurance by impulsivity.

Research limitations/implications

The findings indicate that individual insurance consumption may be amplified by not cognitive components. Future research should concentrate on testing the effect of further psychological traits related to pure risk coverage.

Practical implications

The results may be of interest for insurers in order to know what drives insurance demand with respect to different kinds of pure risks.

Social implications

For policymakers, it is important to understand how psychological factors affect consumer behavior in order to incorporate such perspective into modern insurance policy measures. An analysis of such factors may also increase the self-consciousness of insurance consumers and enrich consumer self-protection.

Originality/value

The authors propose an interdisciplinary approach to analyze insurance demand and test different kinds of insurance coverage, suggesting not homogenous hedging behaviors in relation to specific ambiguous events.

Details

Review of Behavioral Finance, vol. 6 no. 2
Type: Research Article
ISSN: 1940-5979

Keywords

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