Impact of energy-using product policies

Energy-using products (EuPs), such as domestic appliances, audio-visual and ICT equipment contribute significantly to CO2 emissions, both in the domestic and non-domestic sectors. Policies that encourage the use of more energy efficient products can therefore generate significant reductions in overall energy consumption and hence, CO2 emissions. To the extent that these policies cause an increase the average production cost of EuPs, they may impose economic costs on producers, or on consumers, or on both. In this theoretical paper, an adaptation of a simple vertical product differentiation model – in which products are characterised in terms of their quality and their energy consumption – is used to analyse the impact of the different EuP polices on product innovation and to assess the resultant economic impacts on producers and consumers. It is shown that whereas the imposition of a binding product standard for energy efficiency unambiguously reduces aggregate profit and increases the average market price in the absence of any learning effects, the introduction or strengthening of demand-side measures (such as energy labelling) may reduce, or increase, aggregate profit. Even in the case where the overall impact is unambiguously negative, the effects of product innovation and learning can be in either direction.

Cross-Industry Product Diversification and Contagion in Risk and Return: The Case of Bank-Insurance and Insurance-Bank Takeovers

We investigate the impact of domestic/international bancassurance deals on the risk-return profiles of announcing and non-announcing banks and insurers within a GARCH model. Bank-insurance deals produce intra- and inter-industry contagion in both risk and return, with larger deals producing greater contagion. Bidder banks and peers experience positive abnormal returns, with the effects on insurer peers being stronger than those on bank peers. Insurance-bank deals produce insignificant excess returns for bidder and peer insurers and positive valuations for peer banks. Following the deal, the bank bidders’ idiosyncratic (systematic) risk falls (increases), while insurance bidders exhibit a lower systematic risk and maintain their idiosyncratic risk.

Diversification, size and risk: The case of bank acquisitions of nonbank financial firms

We investigate the risk effects of bank acquisitions of insurance companies and securities firms between 1991 and 2012 using a newly constructed dataset of M&A deals. We examine risk changes before and after deal announcements by decomposing risk into systematic and idiosyncratic components. Subsequently, we investigate the relationship between risk and diversification by modelling the determinants of risks. We find that bank combinations with securities firms yield higher risks than combinations with insurance companies. Bank size is an important and consistent determinant of risk whereas diversification is not. Our results inform the continuing debate on diversification versus functional separation of bank activities.

Prediction of breast cancer risk based on profiling with common genetic variants

BACKGROUND: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. METHODS: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. RESULTS: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. CONCLUSIONS: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.