The European used-car market at a glance: Hedonic resale price valuation in automotive leasing industry

In the leasing industry, the risk of loss on sales at the end of the contract term, as well as pricing are critically impacted by the forecasted resale price of the asset (residual value). We apply the Hedonic methodology to European auto lease portfolios, in order to estimate the resale price distribution. The Hedonic approach estimates the price of a good through the valuation of its attributes. Following a discussion on Hedonic prices, we propose an operational model for the automobile resale market. The model is applied to four European countries (France, Germany, Spain and Great Britain), and distributions are calculated on two vehicle versions (Audi A4 & Ford Focus) allowing a comparison of market depreciation patterns and residual value risks.Hedonic model, residual value, automotive market

Agricultural Risk Aversion Revisited: A Multicriteria Decision-Making Approach

In modelling farm systems it is widely accepted that risk plays a central role. Furthermore, farmers' risk aversion determines their decisions in both the short and the long run. This paper presents a methodology based on multiple criteria mathematical programming to obtain relative and absolute risk aversion coefficients. We rely on multiattribute utility theory (MAUT) to elicit a separable additive multiattribute utility function and then estimate the risk aversion coefficients and apply this methodology to an irrigated area of Northern Spain. The results show a wide variety of attitudes to risk among farmers, who mainly exhibit decreasing absolute risk aversion (DARA) and constant relative risk aversion (CRRA).Risk analysis, Agriculture, Utility theory, Multiple criteria analysis, Risk and Uncertainty,

The Five Factor Model of personality and evaluation of drug consumption risk

The problem of evaluating an individual's risk of drug consumption and misuse is highly important. An online survey methodology was employed to collect data including Big Five personality traits (NEO-FFI-R), impulsivity (BIS-11), sensation seeking (ImpSS), and demographic information. The data set contained information on the consumption of 18 central nervous system psychoactive drugs. Correlation analysis demonstrated the existence of groups of drugs with strongly correlated consumption patterns. Three correlation pleiades were identified, named by the central drug in the pleiade: ecstasy, heroin, and benzodiazepines pleiades. An exhaustive search was performed to select the most effective subset of input features and data mining methods to classify users and non-users for each drug and pleiad. A number of classification methods were employed (decision tree, random forest, $k$-nearest neighbors, linear discriminant analysis, Gaussian mixture, probability density function estimation, logistic regression and na{\"i}ve Bayes) and the most effective classifier was selected for each drug. The quality of classification was surprisingly high with sensitivity and specificity (evaluated by leave-one-out cross-validation) being greater than 70\% for almost all classification tasks. The best results with sensitivity and specificity being greater than 75\% were achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance abuse (VSA).Comment: Significantly extended report with 67 pages, 27 tables, 21 figure

Does Gender Affect Investors' Appetite for Risk?: Evidence from Peer-to-Peer Lending

This study investigates the role of gender in financial risk-taking. Specifically, I ask whether female investors tend to fund less risky investment projects than males. To answer this question, I use real-life investment data collected at the largest German market for peer-to-peer lending. Investors' utility is assumed to be a function of the projects expected return and its standard deviation, whereas standard deviation serves as a measure of risk. Gender differences regarding the responses to projects' risk are tested by estimating a random parameter regression model that allows for variation of risk preferences across investors. Estimation results provide no evidence of gender differences in investors' risk propensity: On average, male and female investors respond similarly to the changes in the standard deviation of expected return. Moreover, no differences between male and female investors are found with respect to other characteristics of projects that may serve as a proxy for projects' risk. Significant gender differences in investors' tastes are found only with respect to preferred investment duration, purpose of investment project and borrowers' age.gender, investment choice, risk preferences

A Revealed Preference Approach to the Measurement of Congestion in Travel Cost Models

Travel cost models are regularly used to determine the value of recreational sites or particular site characteristics, yet a key site attribute, congestion, is often excluded from such analyses. One of several reasons is that congestion (unlike many other site attributes) is determined in equilibrium by the process of individuals sorting across sites, and thus presents significant endogeneity problems. This paper illustrates this source of endogeneity, describes how previous research has dealt with it by way of stated preference techniques, and describes an instrumental variables approach to address it in a revealed preference context. We demonstrate that failing to address the endogeneity of congestion will likely lead to the understatement of its costs, and possibly to the mistaken recovery of agglomeration benefits. We apply our technique to the valuation of a large recreational fishing site in Wisconsin (Lake Winnebago) which, if eliminated, would induce significant re-sorting of anglers amongst remaining sites. In our application, ignoring congestion leads to an understatement of the lake’s value by more than 50 percent.Congestion, Random Utility Model, Site Valuation, Travel Cost, Discrete Choice, Instrumental Variables, Quantile Regression

Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model

Networks arising from social, technological and natural domains exhibit rich connectivity patterns and nodes in such networks are often labeled with attributes or features. We address the question of modeling the structure of networks where nodes have attribute information. We present a Multiplicative Attribute Graph (MAG) model that considers nodes with categorical attributes and models the probability of an edge as the product of individual attribute link formation affinities. We develop a scalable variational expectation maximization parameter estimation method. Experiments show that MAG model reliably captures network connectivity as well as provides insights into how different attributes shape the network structure.Comment: 15 pages, 7 figures, 7 table

Spatial Unemployment Differentials in Colombia

This paper studies the geographic distribution of unemployment rates in Colombian urban areas. It introduces measures of spatial correlation and spatial econometric techniques to analyze the dependence in local unemployment rates across municipalities. Results suggest that Colombian municipalities have experienced a polarization process between 1993 and 2005, as municipalities' unemployment rates have followed different evolutions relative to the National average. This process has been accompanied by the creation of unemployment clusters, that is to say, municipalities had very similar unemployment outcomes to those of their neighbors. This analysis uses a spatial Durbin model to explore the influence of various factors in determining differences in regional unemployment rates. According to our findings differences in labor demand, immigration rates, and urbanization are factors behind observed municipal unemployment disparities.local labor markets, unemployment di erential, polarization, clustering, spatial econometrics, spatial Durbin model