Market Stress and Herding*We propose a new approach to detecting and measuring herding which is based on the cross-sectional dispersion of the factor sensitivity of assets within a given market. This method enables us to evaluate if there is herding towards particular sectors or styles in the market including the market index itself and critically we can also separate such herding from common movements in asset returns induced by movements in fundamentals. We apply the approach to an analysis of herding in the US and South Korean stock markets and find that herding towards the market shows significant movements and persistence independently from and given market conditions and macro factors. We find evidence of herding towards the market portfolio in both bull and bear markets. Contrary to common belief, the Asian Crisis and in particular the Russian Crisis reduced herding and are clearly identified as turning points in herding behaviour.JEL Classification: C12, C31, G12 and G14

Objective In 2018, the UK National Institute for Health and Care Excellence (NICE), in partnership with Public Health England, NHS England, NHS Improvement and others, developed an evidence standards framework (ESF) for digital health and care technologies (DHTs). The ESF was designed to provide a standardised approach to guide developers and commissioners on the levels of evidence needed for the clinical and economic evaluation of DHTs by health and care systems. Methods The framework was developed using an agile and iterative methodology that included a literature review of existing initiatives and comparison of these against the requirements set by NHS England; iterative consultation with stakeholders through an expert working group and workshops; and questionnaire-based stakeholder input on a publicly available draft document. Results The evidence standards framework has been well-received and to date the ESF has been viewed online over 55,000 times and downloaded over 19,000 times. Conclusions In April 2021 we published an update to the ESF. Here, we summarise the process through which the ESF was developed, reflect on its global impact to date, and describe NICE’s ongoing work to maintain and improve the framework in the context for a fast moving, innovative field.

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We introduce a general approach to nonlinear quantile regression modelling based on the copula function that defines the dependency structure between the variables of interest. Hence, we extend Koenker and Bassett's (1978. Regression quantiles. Econometrica, 46, no. 1: 33-50.) original statement of the quantile regression problem by determining a distribution for the dependent variable Y conditional on the regressors X, and hence the specification of the quantile regression functions. The approach exploits the fact that the joint distribution function can be split into two parts: the marginals and the dependence function (or copula). We then deduce the form of the (invariably nonlinear) conditional quantile relationship implied by the copula. This can be achieved with arbitrary distributions assumed for the marginals. Some properties of the copula-based quantiles or c-quantiles are derived. Finally, we examine the conditional quantile dependency in the foreign exchange market and compare our quantile approach with standard tail area dependency measures.Copula, Quantile, Regression, dependence, foreign exchange markets,