A Hermite-spline model of post-retirement mortality

ABSTRACT

We present a model for post-retirement mortality where differentials automatically reduce with increasing age, but without the fitted mortality rates for subgroups crossing over. Selection effects are catered for, as are age-modulated time trends and seasonal variation in mortality. Central to the model are Hermite splines, which permit parsimonious modelling of complex risk factors in even modest-sized portfolios. The model is therefore suitable for the stand-alone analysis of experience data for reinsurance, bulk annuities and longevity swaps. We also illustrate the contrast between the statistical significance of a risk factor and its financial significance and discuss reasons why one might include risk factors like season that are not directly financially significant.     

Harmful events and misconducts in financial organizations: Human biases and root causes

We conduct a large scope field investigation of 19 major incidents in 19 large European insurance and banking institutions, based on 116 post-event interviews with managers and top executives over a two-year period. We demonstrate the power of the Root Cause Analysis (RCA) method for detecting human biases documented by the behavioral finance, the organizational behavior and occupational psychology literatures. These biases constitute key operational risk factors these organizations. We find that organizational biases (such as a breach of psychological contract) take center stage as root causes of incidents in these organizations. We also find that banks are more exposed to emotional biases (fear and greed) and insurance companies more subjected to cognitive conservatism as root cause biases. This research has direct implications regarding how banks and insurance companies may cope with regulations that put a greater emphasis on measuring and controlling operational risk and specifically misconduct risk.     

Talking aggressively about GMOs? Examining the effect of aggressive risk communication with communicator’s facial expression and gender

Abstract

Two experiments (N = 229 and N = 268) assessed the effect of aggressive risk communication about GMOs by a scientist on respondents’ perceptions of message quality and writer (the scientist communicator) likability. We also considered two factors from the communicator that may influence how individuals process aggressive messages – facial expression (study 1) and the gender (study 2). Both studies showed that aggressive communication has a negative effect on both perceived message quality and writer likability, which is explained by the level of negative expectancy violation individuals perceived. Moreover, study 1 showed that smiling appeared to be a negative influence on the outcomes and study 2 showed that gender did not influence how people perceive aggressive messages. The findings provided both scholarly and practical implications for science and risk communication.     

Shifting reactions to risks: a case study

Social science research in the field of risk analysis has emphasised evaluating the magnitude of individuals’ reactions to risks (e.g. strength and salience of concerns, frequency and forcefulness of behaviours that respond to risks). Fewer studies assess the factors that contribute to risk reactions or the types of risks to which reactions are directed (e.g. human health, wildlife health or ecosystem health risks; economic or aesthetic concerns). Theoretical and empirical research on amplification and attenuation of risk (e.g. the Social Amplification of Risk Framework) reveals that the strength of people’s reaction to risks can change markedly over time, as a function of new stimuli. We expand on this foundation to consider here how the types of risks members of the public react to may also shift over the course of their exposure to a hazard, particularly as more information about the hazard becomes available. This case study of risk reactions related to an outbreak of type E botulism in north-western Michigan, USA, demonstrates that the types of risks people react to can change substantially over time. We identify factors that contributed to changes in types of risk reactions, and then consider how these factors potentially distinguish the botulism outbreak from other outbreaks. This case study suggests that, under certain conditions, risk communication can meaningfully alter the types of risks that people react to in response to an environmental hazard. We discuss implications of these findings for risk research, risk communication and environmental conservation.     

Utilitarianism and risk

Abstract

In day-to-day life, we are continuously exposed to different kinds of risk. Unfortunately, avoiding risk can often come at societal or individual costs. Hence, an important task within risk management is deciding how much it can be justified to expose members of society to risk x in order to avoid societal and individual costs y – and vice versa. We can refer to this as the task of setting an acceptable risk threshold. Judging whether a risk threshold is justified requires normative reasoning about what levels of risk exposure that are permissible. One such prominent normative theory is utilitarianism. According to utilitarians, the preferred risk threshold is the one that yields more utility for the most people compared to alternative risk thresholds. In this paper, I investigate whether and the extent to which utilitarian theory can be used to normatively ground a particular risk threshold in this way. In particular, I argue that there are (at least) seven different utilitarian approaches to setting an acceptable risk threshold. I discuss each of these approaches in turn and argue that neither can satisfactorily ground an acceptable risk threshold.     

Currency total return swaps: valuation and risk factor analysis

Abstract

Currency total return swaps (CTRS) are hybrid derivative instruments that allow us to simultaneously hedge against credit and currency risks. We develop a structural credit risk model to evaluate CTRS premia. An empirical test on a sample of 23,005 price observations from 59 underlying issuers yields an average percentage error of around 10%. This indicates that, beyond interest rate risk, firm-specific factors are major drivers of the variations in the valuation of these instruments. Regression analysis of residuals shows that exchange rate determinants account for up to 40% of model pricing errors, indicating that a currency risk premium affects the CTRS price significantly but only marginally, which confirms the prevalence of credit risk in the pricing of CTRS.     

Light me up: power and expertise in risk communication and policy-making in the e-cigarette health debates

Abstract

This paper presents a detailed account of policy-making in a contemporary risk communication arena, where strong power dynamics are at play that have hitherto lacked theoretical analysis and empirical validation. Specifically, it expands on the understanding of how public health policy decisions are made when there is a weak evidential base and where multiple interpretations, power dynamics and values are brought to bear on issues of risk and uncertainty. The aim of the paper is to understand the role that power and expertise play in shaping public health risk communication within policy-related debates. By drawing on insight from a range of literatures, the paper argues that there several interacting factors that shape how a particular narrative gains prominence within a wider set of perspectives and how the arguments and findings associated with that perspective become amplified within the context of policy choices. These findings are conceptualised into a new model – a policy evaluation risk communication (PERC) framework – and are then tested using the Electronic cigarette debate as a case study.     

Collective efficacy and natural hazards: differing roles of social cohesion and task-specific efficacy in shaping risk and coping beliefs

Abstract

Previous research in non-disaster contexts has shown that the concept of collective efficacy, which is a group’s sense of its ability to achieve a specific objective, assists understanding of community readiness and households’ decisions to take preparedness actions. Collective efficacy expands the concept of social capital, which refers to social resources such as trust, norms and networks, by addressing how likely communities are to activate these resources for specific tasks. This paper empirically investigates the effect of three distinct collective efficacy components on risk perception, fear and self-efficacy regarding natural hazards in Austria. The three components have differing impacts on risk and coping beliefs: (1) Social cohesion decreases risk perception and fear but has no effect on self-efficacy; (2) Efficacy belief in social support increases self-efficacy; (3) Efficacy belief in citizen groups increases risk perception and fear. The combination of efficacy belief in social support and citizen groups seems to be most promising for stimulating protective action, as they together promote both risk and coping appraisal. However, overreliance on social support may have the undesirable effect of creating a false sense of safety among disaster-prone households. The findings demonstrate that collective efficacy provides a meaningful perspective from which to examine risk and coping beliefs but caution against treating it as an umbrella concept, given the differing effects of its components. Future studies are needed to investigate the impact of collective efficacy on other key explanatory factors of protective action, such as response efficacy or non-protective responses.     

Gibbs posterior inference on value-at-risk

ABSTRACT

Accurate estimation of value-at-risk (VaR) and assessment of associated uncertainty is crucial for both insurers and regulators, particularly in Europe. Existing approaches link data and VaR indirectly by first linking data to the parameter of a probability model, and then expressing VaR as a function of that parameter. This indirect approach exposes the insurer to model misspecification bias or estimation inefficiency, depending on whether the parameter is finite- or infinite-dimensional. In this paper, we link data and VaR directly via what we call a discrepancy function, and this leads naturally to a Gibbs posterior distribution for VaR that does not suffer from the aforementioned biases and inefficiencies. Asymptotic consistency and root-n concentration rate of the Gibbs posterior are established, and simulations highlight its superior finite-sample performance compared to other approaches.     

Sociology,risk and the environment: a material-semiotic approach

Abstract

Sociology has made significant contributions to the conceptualisation of risk and critique of technical risk analysis. It has, however, unintentionally reinforced the division of labour between the natural/technical and social sciences in risk analysis. This paper argues that the problem with conceptualisations of risk is not a misplaced emphasis on calculation. Rather, it is that we have not adequately dealt with ontological distinctions implicit in both sociological and technical work on risk between material or objective risks and our socially mediated understandings and interpretations of those risks. While acknowledging that risks are simultaneously social and technical, sociologists have not, in practice, provided the conceptual and methodological tools to apprehend risk in a less dualistic manner. This limits our ability both to analyse actors and processes outside the social domain and to explore the recursive relationships between risk calculus, social action and the material outcomes of risk. In response, this paper develops a material-semiotic conceptualisation of risk and provides an assessment of its relevance to more sociologically informed risk governance. It introduces the ideas of co-constitution, emergent entities and enactment as instruments for reconciling the material and social worlds in a sociological study of risk. It further illustrates the application of a material-semiotic approach using these concepts in the nuclear industry. In deconstructing social–material dualisms in the sociology of risk, this paper argues that a material-semiotic conceptualisation of risk enables both technical and social perspectives on risk not only to coexist but to collaborate, widening the scope for interdisciplinary research.     

Linear beta pricing with inefficient benchmarks in a given factor structure*

ABSTRACT

We show the equivalence between the zero-beta version of a multi-factor arbitrage pricing model and a linear pricing model utilizing undiversified inefficient benchmarks in a given factor structure. The resulting linear model is a two-beta model, with one beta related to the inefficient benchmark and another adjusting for its inefficiency. This linear model shows that there are only two distinctive and computable sources of risk, affecting security expected returns, despite the existence of several risk factors. In a short empirical example we demonstrate that the model can be employed to provide guidance and allow researchers to test for the validity of their selection of the underlying risk factors driving variations in security returns.     

The estimation of mortality and other decremental probabilities

Abstract

The standard actuarial methods of estimating the age-specific one-year probabilities of death in a given community were developed—for the most part, many years ago-with large bodies of observations in mind. Although the familiar “exposed to risk” procedure is known to provide unbiased estimates only when a rather dubious assumption is made about the progression of the instantaneous death-rate (the force of mortality) over the year of age (Cantelli, 1914) it is still the most widely used method of estimation. This is partly because the age-to-age increment in human mortality is relatively small—so that assumptions about its mathematical form are unimportant—and partly because suggested methods of estimation based on more “realistic” assumptions are usually laborious to apply to thousands of observations.     

The role of numeric and statistical content on risk perception in infographics about road safety

Abstract

Numbers saturate news coverage and health and risk messaging. But as our expertise in the creation of statistical information increases, the ability to use those statistics in decision making remains frustratingly inadequate. There has been a wealth of research related to how to train people to better use the numbers they interact with on a daily basis. Far less research, however, explores the appropriate way to use numbers in communication. Two experiments explored the role of numbers in risk communication infographics related to road safety while driving. Experiment 1 found that the presence of numbers influence risk perception, but whether those numbers reflect accurate statistics or random numbers does not change their influence. Experiment 2 found that removing all statistics entirely from infographics and replacing them with linguistic gist representations of the numbers (i.e. words like ‘some’, ‘many’, ‘none’) increased risk perception even though people found the infographics to be less informative than the ones containing numbers. The results suggest that the gist representations of the numbers in the context of the infographics are equivalent regardless of their value, such that the very presence of statistics influences judgment and risk perception but not their meaning. They also suggest that people do not always realize how they are using statistical information in their judgement and decision making process.     

Extrapolating understanding of food risk perceptions to emerging food safety cases

Important determinants of risk perceptions associated with foods are the extent to which the potential hazards are perceived to have technological or naturally occurring origins, together with the acute vs. chronic dimension in which the potential hazard is presented (acute or chronic). This study presents a case study analysis based on an extensive literature review examining how these hazard characteristics affect people’s risk and benefit perceptions, and associated attitudes and behaviors. The cases include E. coli incidences (outbreaks linked to fresh spinach and fenugreek sprouts), contamination of fish by environmental pollutants, (organochlorine contaminants in farmed salmon), radioactive contamination of food following a nuclear accident (the Fukushima accident in Japan), and GM salmon destined for the human food chain. The analysis of the cases over the acute vs. chronic dimension suggests that longitudinal quantification of the relationship between risk perceptions and impacts is important for both acute and chronic food safety, but this has infrequently been applied to chronic hazards. Technologies applied to food production tend to potentially be associated with higher levels of risk perception, linked to perceptions that the risk is unnatural. However, for some risks (e.g. those involving biological irreversibility), moral or ethical concerns may be more important determinants of consumer responses than risk or benefit perceptions. (Lack of) trust has been highlighted in all of the cases suggesting transparent and honest risk–benefit communications following the occurrence of a food safety incident. Implications for optimizing associated risk communication strategies, additional research linking risk perception, and other quantitative measures, including comparisons in time and space, are suggested.     

Relative Importance of Risk Sources in Insurance Systems

Abstract

Actuaries, and other managers of uncertainty, identify factors in modeling insurance risks because they believe (1) that these factors affect the outcome of a risk or (2) that the factors can be managed, thus allowing analysts a degree of control over the insurance system. This paper shows how to use a statistical measure, the coefficient of determination, for quantifying the relative importance of a source of uncertainty. With a quantitative measure of relative importance, risk managers can sharpen their intuition about the relative importance of risk factors and become better custodians of financial security systems.

This paper shows that the coefficient of determination is intuitively appealing in assessing the effectiveness of basic risk management techniques including risk exchange, pooling, and financial risk management. A single source common to all risks reduces the effectiveness of a pool; the risk measure quantifies the relative importance of this common source. The coefficient of determination is shown to have roots in the economics as well as the statistics literature. This connection provides further motivation for using the coefficient of determination and also suggests alternative measures for quantifying relative importance. The risk measure is useful in multivariate situations in which several factors affect a risk simultaneously. The paper illustrates this usefulness by considering a pool of policies that is subject to mortality, a common disaster, and a common investment environment.     

Olav Aabakken

Abstract

An empirical linear Bayes estimator is asymptotically optimal in the usual sense if its average risk converges to the risk of the corresponding linear Bayes estimator. The present paper demonstrates that the following result holds for the most commonly used models: If the unknown (structural) parameters are estimated in such a way that their mean square error converges at a certain rate, then the corresponding empirical linear Bayes estimator is asymptotically optimal with the same rate of risk convergence. In particular, this is the case for the random coefficient regression model, and for hierarchical models in the univariate case.     

A nested factor model for non-linear dependencies in stock returns

The aim of our work is to propose a natural framework to account for all the empirically known properties of the multivariate distribution of stock returns. We define and study a ‘nested factor model’, where the linear factors part is standard, but where the log-volatility of the linear factors and of the residuals are themselves endowed with a factor structure and residuals. We propose a calibration procedure to estimate these log-vol factors and the residuals. We find that whereas the number of relevant linear factors is relatively large (10 or more), only two or three log-vol factors emerge in our analysis of the data. In fact, a minimal model where only one log-vol factor is considered is already very satisfactory, as it accurately reproduces the properties of bivariate copulas, in particular, the dependence of the medial point on the linear correlation coefficient, as reported in Chicheportiche and Bouchaud [Int. J. Theor. Appl. Finance, 2012, 15]. We have tested the ability of the model to predict out-of-sample the risk of non-linear portfolios, and found that it performs significantly better than other schemes.     

Processes and Accuracy of Cash Flow Forecasting: A Case Study of a Multinational Corporation

Despite its pivotal importance in enterprise management, cash flow forecasting gets little attention from academics perhaps because few of them have access to internal processes and data. In this article, however, the authors explain how cash flow forecasting is organized at Bayer, a large multinational company headquartered in Germany, and which factors influence the accuracy of its forecasts. The research focuses on cash flow forecasts based on the direct method, prepared three times a yearat Bayer, involving about 62,000 individual forecasting items each time. These forecasts form the basis of the company's liquidity and financial risk management, in particular, its foreign exchange risk hedging. The authors explain how local managers in Bayer's entities across the world derive the forecasts, i.e., what information they use as input, how they validate it, and how they deal with potential bias caused by managerial incentive systems. They also analyze whether forecasting processes are affected by characteristics such as business area, size, region, or specific local conditions, and ultimately whether forecasting practices and entity characteristics affect forecast accuracy. The findings show that cash flow forecasting procedures vary substantially across Bayer. While the central finance department gives general guidance on the required cash flow forecasting output and provides direction on the input to be used, there are no detailed instructions on how forecasts are to be prepared. Instead, local managers are free to determine their own forecasting practices. They use different forecasting inputs and validate forecasting inputs and output with different intensities, and they also differ in how they treat possible biases in input data. These findings document the limits of standardization and central control in large multinational corporations resulting from local managers’ need for flexibility to cope with the heterogeneity and dynamism of their environments. At the same time, however, local differentiation increases complexity and may increase errors. Quantitative analysis of forecasting errors shows that forecasts of receipts from customers (cash inflows) are more accurate than forecasts of payments to suppliers (cash outflows). Moreover, forecasting practices affect forecast accuracy. Outflow forecasts are more accurate if managers intensively validate forecasting input; inflow forecasts, if they eliminate input biases that may result from internal target setting or from other managerial incentives, and if they carefully validate their forecasting output. The study provides several insights.

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Screening potential elderly preferred markers: exploratory analysis of Cardiovascular Health Study (CHS) data

The Cardiovascular Health Study (CHS) analyzes risk factors for coronary heart disease and stroke in people age 65 and older. Since CHS is designed to comprehensively study cardiovascular risk factors in an elderly population, it provides a unique opportunity to study the association of risk factors with mortality, as well as morbidity risk. With the growth of the elderly as population and life insurance market segments, the need to more precisely stratify mortality within a standard risk group of the elderly has grown as well. This exploratory analysis assesses medical factors that could be used to improve mortality risk stratification within a "standard" mortality population, using the CHS public use data set. Participants with a personal history of cardiovascular disease, diabetes, or major electrocardiographic abnormalities were excluded from the analysis in order to mimic a standard life insurance selection process. Then, Cox proportional hazards regression was used to study 10 medical risk factors. This model suggested that forced vital capacity >80% predicted, serum creatinine <1.5 mg/dL (133 mcmol/L), hemoglobin >11 g/dL (110 g/L), and serum albumin >3.5 mg/L (35 mmol/ L) are significantly associated (p = 0.05) with favorable mortality. C-reactive protein <1 mg/L is associated with favorable mortality at borderline significance levels (p = 0.09). On the other hand, a family history of cardiovascular disease (MI and/or stroke) and low BMI (<26 kg/m2) are associated with unfavorable mortality in the analysis. Total to HDL cholesterol ratio of <6, presence of supine systolic blood pressure < or = 140 mmHg, and the presence of minor rest electrocardiographic findings were not statistically significant factors in the multivariate model. Further assessment of the predictive value of the "significant" medical factors identified is required in insured lives.