A market-consistent framework for the fair evaluation of insurance contracts under Solvency II

Decisions in Economics and Finance - The entry into force of the Solvency II regulatory regime is pushing insurance companies in engaging into market consistence evaluation of their balance sheet,...     

Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).     

Electricity awareness and consumer demand for information

With the envisioned growth in the residential electricity demand and increased share of intermittent renewables in the supply mix, consumers will need to be better informed about their electricity consumption and to play an active role in managing their electricity use. However, consumer inattention and lack of information are ubiquitous, especially in household energy‐related settings. Thus, using a novel survey and actual monthly electricity consumption data, this study set out to measure the level of awareness about electricity bills, prices and costs among some Finnish households—as captured by the answers to six questions—and to investigate whether higher levels of “electricity awareness” are associated with electricity savings. In addition, this study analyses the willingness to receive extra information about energy consumption and savings and how it differs between “electricity aware” and “electricity unaware” respondents. The results indicate low levels of “electricity awareness” among the respondents of the survey. Compared to the respondents with little knowledge about electricity bills, prices and costs, the respondents with higher levels of “electricity awareness” tend to consume less electricity. Higher levels of awareness about electricity use and consumption might “materialize” inconspicuous consumption patterns, as opposed to more general facts about the largely invisible environmental consequences of everyday practices. More than two‐thirds of the total number of respondents would like to receive additional information about energy consumption and how to save energy. However, there exists a significant portion of “electricity unaware” respondents who are not only unwilling to receive such information, but are also unaware of their own knowledge deficits. To maximize the impact of any information strategy, decision makers should attempt to engage with this type of consumer; by becoming more aware of their knowledge deficits, people might become more receptive to information that can benefit them.     

Risk-type density diagrams by crash type on two-lane rural roads

The research presented here aims to plot density diagrams per road crash risk type to identify all possible scenarios where driving is less than safe. The starting point was the prediction of injury crash rate on horizontal homogeneous segments of two-lane rural roads for three main injurious crash types (head-on/side collisions, rear-end crashes, and single-vehicle run-off-road crashes) as observed on the network. A careful analysis of the database shows that a wide variety of factors appear to be influenced or associated with the crash dynamic, as follows: the road scenario (combination of infrastructure and environmental conditions found at the site at the time of the crash), mean lane width, the horizontal curvature indicator (measurement of the curvature change rate), and mean speed. Crashes recorded from 2003 to 2010, of which 1597 were injurious, and 645 resulted only in damage to property, were analyzed on more than 3700?km of road network in Southern Italy. Generalized estimating equations with a negative binomial distribution were implemented. Risk-type density charts were plotted to thoroughly identify all possible combinations of existing explicative variables producing hazardous conditions on the road. The different shades in the diagrams represent different ranges of injurious crash rates: the white band shows low levels, while a black band shows high values. It is not possible to consider working on an explanatory variable to reduce hazardous conditions on the road network without also considering how this variation might affect the influence of the remaining explanatory variables on crash phenomena and, consequently, on the predictive model. The risk maps make it possible to keep under control in a simple and immediate approach the way each variable as a result of variations of a part or of all.     

National Health Service Boards of Directors and Governance Models

Abstract

This article reports the findings of a year-long research project focused on the activity of boards of directors of twenty-two trusts from the British National Health Service (NHS). The evidence gathered through the use of semi-structured interviews, focus groups, workshops, feedback questionnaires and document analysis indicates that the behavioural dynamics of boards, affected by the dominance of the expert model, act as antecedents of their statutory functions and the implementation of different governance models. Only a portion of the boards involved has effectively incorporated in its modus operandi post-New Public Management (post-NPM) principles of governance.     

A medium-N approach to macroeconomic forecasting

This paper considers methods for forecasting macroeconomic time series in a framework where the number of predictors, N, is too large to apply traditional regression models but not sufficiently large to resort to statistical inference based on double asymptotics. Our interest is motivated by a body of empirical research suggesting that popular data-rich prediction methods perform best when N ranges from 20 to 40. In order to accomplish our goal, we resort to partial least squares and principal component regression to consistently estimate a stable dynamic regression model with many predictors as only the number of observations, T, diverges. We show both by simulations and empirical applications that the considered methods, especially partial least squares, compare well to models that are widely used in macroeconomic forecasting.     

The Power of the Euro–Sterling Rates in Explaining Asset Market Rates: A High‐frequency Analysis

This article makes use of high‐frequency asset market data to explain unexpected changes in interest rates using the methodology proposed by Cochrane and Piazzesi (2002) . This work departs from the existing literature because it uses UK market expectations to capture unexpected movements in the base rate, and explores its effect on a large number of asset market variables. Results indicate that the relation between asset market data and unexpected base rate changes is stronger and more consistent than the relation between asset market data and raw base rate changes. Results appear to be robust to extreme value changes.     

Consumer Welfare and the Loss Induced by Withholding Information: The Case of BSE in Italy

The paper develops a measure of consumer welfare losses associated with withholding information about a possible link between BSE and vCJD. The Cost of Ignorance (COI) is measured by comparing the utility of the informed choice with the utility of the uninformed choice, under conditions of improved information. Unlike previous work that is largely based on a single equation demand model, the measure is obtained retrieving a cost function from a dynamic Almost Ideal Demand System. The estimated perceived loss for Italian consumers due to delayed information ranges from 12 percent to 54 percent of total meat expenditure, depending on the month assumed to embody correct beliefs about the safety level of beef.     

Risk-sharing and Growth: The Role of Precautionary Savings in the "Education" Model

While the "risk amelioration" literature suggests that risk-sharing channels savings into risky but productive technologies and hence favours growth, models focused on precautionary savings reverse this conclusion. We solve, by means of numerical techniques, a model based on human capital accumulation through education, and find that the increase in precautionary savings makes labour more productive in the goods sector and draws resources from education, which is the "growth leading" activity. Hence, we establish a result favourable to financial integration, even in a model where precautionary savings play an important role.     

Credit Risk Models – Do They Deliver Their Promises? A Quantitative Assessment

We develop a framework to assess the statistical significance of expected default frequency calculated by credit risk models. This framework is then used to analyse the quality of two commercially available models that have become popular among practitioners: KMV Credit Monitor and RiskCalc from Moody's.
Using a unique database of expected default probability from both vendors, we study both the consistency of the prediction and its timeliness. We introduce the concept of cumulative accuracy profile (CAP) that allows to see in one curve the percentage of defaulting companies captured by the models one year in advance. We also use the Miller's information test to see if the models add information to the S&P rating.
The result of the analysis indicates that these models indeed add relevant information not accounted for by rating alone. Moreover, with respect to rating agencies, the models predict defaults more than ten months in advance on average.
(J.E.L.: C52).