Multivariate Appoximations to Portfolio Return Distribution

This article proposes a three-step procedure to estimate portfolio return distributions under the multivariate Gram-Charlier (MGC) distribution. The method combines quasi maximum likelihood (QML) estimation for conditional means and variances and the method of moments (MM) estimation for the rest of the density parameters, including the correlation coefficients. The procedure involves consistent estimates even under density misspecification and solves the so-called ‘curse of dimensionality’ of multivariate modelling. Furthermore, the use of a MGC distribution represents a flexible and general approximation to the true distribution of portfolio returns and accounts for all its empirical regularities. An application of such procedure is performed for a portfolio composed of three European indices as an illustration. The MM estimation of the MGC (MGC-MM) is compared with the traditional maximum likelihood of both the MGC and multivariate Student’s t (benchmark) densities. A simulation on Value-at-Risk (VaR) performance for an equally weighted portfolio at 1% and 5% confidence indicates that the MGC-MM method provides reasonable approximations to the true empirical VaR. Therefore, the procedure seems to be a useful tool for risk managers and practitioners.

Risk and benefits in lifestyle sports: parkour, law and social value

This paper examines the interrelationship between law and lifestyle sports, viewed through the lens of parkour. We argue that the literature relating to legal approaches to lifestyle sport is currently underdeveloped and so seek to partially fill this lacuna. Hitherto, we argue, the law has been viewed as a largely negative presence, seen particularly in terms of the ways in which counter-cultural activities are policed and regulated, and where such activities are viewed as transgressive or undesirable. We argue that this is a somewhat unsophisticated take on how the law can operate, with law constructed as an outcome of constraints to behaviour (where the law authorises or prohibits), distinct from the legal contexts, environments and spaces in which these relationships occur. We argue that the distinctive settings in which lifestyle sports are practiced needs a more fine-grained analysis as they are settings which bear, and bring to life, laws and regulations that shape how space is to be experienced. We examine specifically the interrelationship between risk and benefit and how the law recognises issues of social utility or value, particularly within the context of lifestyle sport. We seek to move from user-centred constructions of law as an imposition, to a more nuanced position that looks at parkour at the intersections of law, space and lifestyle sport, in order to reveal how law can be used to support and extend claims to space.

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.

Board attributes that increase firm risk - evidence from the UK

Purpose – The aim of the paper is to identify the board attributes that significantly increase firm risk. The study aims to find if board size, percentage of non-executive directors, women on the board, a powerful CEO, equity ownership amongst executive board directors and institutional investor ownership, are associated with firm risk. This is the first study that examines which board attributes increase firm risk using a UK based sample. Design/methodology/approach – This empirical study collected secondary data from Bloomberg and Morningstar databases. The data sample is an unbalanced panel of 260 companies’ secondary data on FTSE 350 index in the UK, from 2005 to 2010. The data was statistically analysed using STATA. Findings – The study establishes the board attributes that were significantly related to firm risk. The results show that a board which can increase firm risk is one that is small in size,has high equity ownership amongst executive board directors and has high institutional investor ownership. Research limitations/implications – The governance culture and regulatory system in the UK is different from other countries. Since the data is a UK based sample, the results can lack generalisability. Practical implications – The results are useful for investors who invest in large firms, to have the knowledge about the board attributes that can increase firm risk. Regulators can also use the results to strengthen regulatory guidelines. Originality/value – This study fills the gap in knowledge in UK governance literature on the board attributes that can increase firm risk.

Do financial distress and liquidity crises affect value and size premiums?

This study investigates the impact of liquidity crises on the relationship between stock (value and size) premiums and default risk in the US market. It first examines whether financial distress can explain value and size premiums, and then, subsequently, aims to determine whether liquidity crises increase the risk of value and size premium investment strategies. The study employs a time-varying approach and a sample of US stock returns for the period between January 1982 and March 2011, a period which includes the current liquidity crisis, so as to examine the relationship between default risk, liquidity crises and value and size premiums. The findings indicate that the default premium has explanatory power for value and size and premiums, which affect firms with different characteristics. We also find that liquidity crises may actually increase the risks related to size and value premium strategies.

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.