The Geneva papers on risk and insurance. Issues and practice最新文献

Microinsurance is a risk management tool for low-income households. However, its adoption is low in Ghana. This study examines the determinants of microinsurance adoption in Ghana, analysing primary data from 1453 households across six key markets and three regions. We also gathered secondary data from 14 microinsurance firms and 47 microinsurance products between 2017 and 2021. We estimate the critical factors influencing microinsurance uptake using robust probit, fixed-effects and panel-corrected standard error models. Our findings indicate that income levels, trust in financial institutions and participation in community risk management groups and the national health insurance scheme are the key determinants affecting microinsurance adoption. Firm- and product-specific factors such as affordability, outstanding claims, risk premiums and benefits paid to microinsurance participants also influence adoption. This study also highlights the crucial role of structural, social and economic factors in predicting demand for microinsurance, utilising Outreville's four-factor insurance demand framework.

For the life insurance industry and pension schemes, mortality projections are critical for accurately managing exposure to longevity risk in terms of both premium setting and reserving. Frailty has been identified as an important latent factor underpinning the evolution of mortality rates. It represents the comorbidities that drive the deterioration of the human body’s physiological capacity. In this paper, we propose a stochastic mortality model that incorporates the trend in frailty, and we analyse the gap between the actuarial evaluations of premiums and technical provisions calculated under frailty-based and traditional stochastic mortality models. We observe that the frailty-based model leads to higher levels of uncertainty in estimates and projections (compared to a traditional stochastic mortality model), which is attributed to the explicit modelling of the comorbidities. This leads to proposing a potentially important policy-oriented recommendation: the incorporation of frailty in mortality modelling would allow for the profiling of mortality according to the portfolio in force for the insurer (or pension scheme), thereby mitigating the problem of adverse selection.

Defined contribution retirement pension systems need to improve the level and stability of payments as pensioners age. Longevity risk pooling is key, but so is flexibility to satisfy members' individual needs and preferences. We propose a tontine construct as a flexible and cost-effective investment option for the Chilean pension system. Payouts are for life, but variable since there are no explicit guarantees. Our proposal provides transparency, investment flexibility, and higher expected income streams than any of the existing options available to the country’s pensioners. Importantly, it does not distort the existing investment and annuity markets; on the contrary, it complements them. Additionally, it provides a means to offer a form of longevity insurance even if insurers are unwilling to supply it. Furthermore, it is in line with the movement of many countries toward promoting longevity-risk sharing within their defined-contribution systems.

We propose a new machine learning-based framework for long-term mortality forecasting. Based on ideas of neighboring prediction, model ensembling, and tree boosting, this framework can significantly improve the prediction accuracy of long-term mortality. In addition, the proposed framework addresses the challenge of a shrinking pattern in long-term forecasting with information from neighboring ages and cohorts. An extensive empirical analysis is conducted using various countries and regions in the Human Mortality Database. Results show that this framework reduces the mean absolute percentage error (MAPE) of the 20-year forecasting by almost 50% compared to classic stochastic mortality models, and it also outperforms deep learning-based benchmarks. Moreover, including mortality data from multiple populations can further enhance the long-term prediction performance of this framework.

The concept of CBD mortality indexes was proposed in 2014. While it has been shown that the use of CBD mortality indexes can effectively reduce longevity risk exposures in idealized settings, the risk mitigation potential of such indexes in a more realistic environment, whereby, for example, population basis risk exists, is yet to be investigated. This research gap is addressed in this paper through the development of a generalized CBD model with stochastic socioeconomic differentials in mortality improvements. The proposed model incorporates possible co-integration effects between mortality dynamics of socioeconomic subgroups and the general population, and features a form of coherence that is less restrictive than the typically assumed full coherence. This paper is concluded with various numerical experiments that are conducted to demonstrate the possible bias in hedge effectiveness that may be resulted if the key features of the proposed model are altered.

Abstract

The aim of this paper is to investigate the relevance of sustainable product attributes as compared to ongoing costs and risk–return profiles when individuals choose funds underlying unit-linked life insurances. Regarding sustainability attributes, we focus on the product classification according to the Sustainable Finance Disclosure Regulation as a European regulatory transparency standard, and on sustainable investment strategies. We conduct two choice-based conjoint analyses using a German panel for unit-linked life insurances as well as fund savings plans as a financial product comparison. We estimate the relative importance, part-worth utilities, and the marginal willingness to pay for changes in product attributes. Our results suggest that private investors of unit-linked life insurances value sustainable product attributes and that they result in a slightly higher marginal willingness to pay, but risk–return indicators and especially ongoing costs are currently more relevant. We find further indications that sustainability attributes are less relevant in the setting of a unit-linked life insurance as compared to a fund savings plans setting.

In this paper, we propose a neural network (NN) architecture of mortality improvement model with cohort effect. We then extend the mortality improvement NN model to consider autoregressive effects, which allows mortality improvement to depend on the lagged mortality rates. The advantage of our NN model setup is that the parameters of period and cohort effects are implicitly estimated by the NN models, and hence, the mortality projection can be obtained without taking the extra steps of selecting and estimating the suitable time-series model for period and cohort effects. Our empirical results suggests that, based on 48 populations in the Human Mortality Database with complete sets of observations from 1950 with the age span of 55–90, the NN models with cohort and autoregressive effects improve the forecast accuracy of mortality rate projections and provide better prediction performance.

Abstract

This article examines the claim that equity release mortgages, the U.K. equivalent of reverse mortgages in the U.S., are suitable investments for pension funds. We present valuation, stress test and scenario analysis results that suggest that equity release mortgages are unsuitable for pension funds because: (i) they bear returns that are typically below the risk-free rate; (ii) they are not hedges for annuity books, let alone good hedges; and (iii) they are heavily exposed to house price risk, which annuity books are not. Our results suggest that equity release mortgages meet none of these criteria to be suitable for pension funds and are almost entirely dominated by risk-free government bonds. We offer an explanation for why investors appear to be unaware of the low returns on equity release mortgages.

This article examines the relationship between financial literacy and business interruption (BI) insurance among Italian entrepreneurs. Following an increase in unexpected shocks, such as COVID-19 and geopolitical conflicts, a high level of BI risk is expected to persist, especially among small firms, which play a key role in the Italian economy. Using a Bank of Italy 2021 survey of 1998 non-financial firms with fewer than 10 employees, we show a significant positive association between the level of the entrepreneur’s financial literacy and the purchase of BI insurance. Our results highlight the key role of financial literacy in shaping risk management strategies and are robust to different model specifications, also addressing endogeneity concerns.

Abstract

The objective of this paper is to assess the contribution of insurance to infrastructure development in Sub-Saharan Africa. The researchers used a sample of 31 Sub-Saharan African countries from a panel of data observed over the period 2003–2020. The methodologies used in this paper are the Driscoll-Kraay and Panels Corrected Standard Error. The results show that insurance (life and non-life) positively explains the level of infrastructure in Sub-Saharan Africa. Furthermore, using disaggregated infrastructure indices, the effect remains the same for transport, electricity, and water and sanitation infrastructure. However, insurance has no significant effect on telecommunications infrastructure. Our results remain robust using an instrumental variable technique, two stage least square. The economic policy suggestions concern the improvement of the regulatory framework for the insurance business so that it can participate effectively in financing the economy. In addition, a strengthening of the public–private partnership is commendable in order to provide governments with an alternative source of infrastructure financing, different from the generally used public financing, whose capacity is likely to be insufficient.