Model risk – daring to open up the black box

With the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. Such risk is generated by the potential inaccuracy and inappropriate use of models in business applications, which can lead to substantial financial losses and reputational damage. In this paper we deal with the management and measurement of model risk. First, a model risk framework is developed, adapting concepts such as risk appetite, monitoring, and mitigation to the particular case of model risk. The usefulness of such a framework for preventing losses associated with model risk is demonstrated through case studies. Second, we investigate the ways in which different ways of using and perceiving models within an organisation both lead to different model risks. We identify four distinct model cultures and argue that in conditions of deep model uncertainty, each of those cultures makes a valuable contribution to model risk governance. Thus the space of legitimate challenges to models is expanded, such that, in addition to a technical critique, operational and commercial concerns are also addressed. Third, we discuss through the examples of proxy modelling, longevity risk and investment advice, common methods and challenges for quantifying model risk. Difficulties arise in mapping model errors to actual financial impact. In the case of irreducible model uncertainty, it is necessary to employ a variety of measurement approaches, based on statistical inference, fitting multiple models, and stress and scenario analysis

Model risk: illuminating the black box

This paper presents latest thinking from the Institute and Faculty of Actuaries’ Model Risk Working Party and follows on from their Phase I work, Model Risk: Daring to Open the Black Box. This is a more practical paper and presents the contributors’ experiences of model risk gained from a wide range of financial and non-financial organisations with suggestions for good practice and proven methods to reduce model risk. After a recap of the Phase I work, examples of model risk communication are given covering communication: to the Board; to the regulator; and to external stakeholders. We present a practical framework for model risk management and quantification with examples of the key actors, processes and cultural challenge. Lessons learned are then presented from other industries that make extensive use of models and include the weather forecasting, software and aerospace industries. Finally, a series of case studies in practical model risk management and mitigation are presented from the contributors’ own experiences covering primarily financial services

Accurate microRNA target prediction correlates with protein repression levels

MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and diseas

The mechanical effects of short-circuit currents in open air substations.

SHort-circuit mechanical effects in substation is investigated by tests and simulations. Simplified equations are deduced to prepare standardisation of a procedure forthe design of substation against short-circuit mechanicle effects

Can environment or allergy explain international variation in prevalence of wheeze in childhood?

Asthma prevalence in children varies substantially around the world, but the contribution of known risk factors to this international variation is uncertain. The International Study of Asthma and Allergies in Childhood (ISAAC) Phase Two studied 8–12 year old children in 30 centres worldwide with parent-completed symptom and risk factor questionnaires and aeroallergen skin prick testing. We used multilevel logistic regression modelling to investigate the effect of adjustment for individual and ecological risk factors on the between-centre variation in prevalence of recent wheeze. Adjustment for single individual-level risk factors changed the centre-level variation from a reduction of up to 8.4% (and 8.5% for atopy) to an increase of up to 6.8%. Modelling the 11 most influential environmental factors among all children simultaneously, the centre-level variation changed little overall (2.4% increase). Modelling only factors that decreased the variance, the 6 most influential factors (synthetic and feather quilt, mother’s smoking, heating stoves, dampness and foam pillows) in combination resulted in a 21% reduction in variance. Ecological (centre-level) risk factors generally explained higher proportions of the variation than did individual risk factors. Single environmental factors and aeroallergen sensitisation measured at the individual (child) level did not explain much of the between-centre variation in wheeze prevalence