Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada     

The Measurement of Multidimensional Poverty

Many authors have insisted on the necessity of defining poverty as a multidimensional concept rather than relying on income or consumption expenditures per capita. Yet, not much has actually been done to include the various dimensions of deprivation into the practical definition and measurement of poverty. Existing attempts along that direction consist of aggregating various attributes into a single index through some arbitrary function and defining a poverty line and associated poverty measures on the basis of that index. This is merely redefining more generally the concept of poverty, which then essentially remains a one dimensional concept. The present paper suggests that an alternative way to take into account the multi-dimensionality of poverty is to specify a poverty line for each dimension of poverty and to consider that a person is poor if he/she falls below at least one of these various lines. The paper then explores how to combine these various poverty lines and associated one-dimensional gaps into multidimensional poverty measures. An application of these measures to the rural population in Brazil is also given with poverty defined on income and education.     

A bayesian estimator for the dependence function of a bivariate extreme‐value distribution

Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme‐value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set.     

Intertemporal choice under timing risk: An experimental approach

This paper investigates how individuals evaluate delayed outcomes with risky realization times. Under the discounted expected utility (DEU) model, such evaluations depend only on intertemporal preferences. We obtain several testable hypotheses using the DEU model as a benchmark and test these hypotheses in three experiments. In general, our results show that the DEU model is a poor predictor of intertemporal choice behavior under timing risk. We found that individuals are averse to timing risk and that they evaluate timing lotteries in a rank-dependent fashion. The main driver of timing risk aversion is nothing but probabilistic risk aversion that stems from the nonlinear treatment of probabilities.     

Composition-consistent tournament solutions and social choice functions

This paper introduces a new axiom for choice in preference profiles and tournaments, called composition-consistency. A social choice function is composition-consistent if it is non-sensitive to the cloning of one or several outcomes. The key feature of the composition consistency property is an operation concept called multiple composition product of profiles. The paper provides a brief overview of some social choice functions studied in the literature. Concerning the tournament solutions, it is proved that the Top Cycle, the Slater and the Copeland solutions are not composition-consistent, whereas the Banks, Uncovered Set, TEQ, Minimal Covering Set are composition-consistent. Moreover, we define the composition-consistent hull of a solution as the smallest composition-consistent solution containing . The composition-consistent hulls of the Top cycle and Copeland solutions are specified, and we give some hints about the location of the hull of the Slater set. Concerning social choice functions, it is shown that Kemeny, Borda and Minimax social choice functions are not composition-consistent, whereas the Paretian one is composition-consistent. Moreover, we prove that the latter is the composition-consistent hull of the Borda and Minimax functions.     

Industry and the environment: a case study of cleaner technologies in selected European countries

The aim of this case study is to discuss the role of technology in addressing environmental problems. The paper tries to scratch beneath the surface of the increasingly frequent ‘quick-fix’ solutions to the present environmental problems, based on such beguiling catchwords as Cleaner Technologies, Best Available Technologies, and Best Available Technologies Not Exceeding Excessive Costs, etc., in an attempt to discover whether there is any substance in them, or whether they are just full of hot air. Recent data from case studies performed by the author in Germany and Finland as well as a postal questionnaire in Denmark are presented. The paper analyses and discusses the roles and responsibilities of designers, industrialists, and government policy-makers. It is argued that existing regulatory regimes, supranational industrial structures, and market mechanisms do not favour the development of cleaner technologies, nor do they promote a reduction in consumption patterns. Evidence from ongoing empirical research in Northwest Europe suggests that industry is far from developing and/or implementing cleaner technologies. The paper closes with a discussion of some of the policy implications involved and some examples of urgently needed further research.     

Workgroup interdependence and satisfaction from a developmental perspective: the moderating role of the group’s life cycle stage / Interdependencia y satisfacción en el grupo de trabajo desde la perspectiva del desarrollo: el rol moderador de la fase del ciclo de vida grupal

From a developmental perspective, workgroups adjust and modify the way they function over time. The present study aims to test, at the group level, the moderating role of the group’s life phase in the relationship between workgroup interdependence ties in terms of task, outcome and functions, assessed by social network density and group satisfaction. A sample of 84 teams from various companies participated in this research. The results showed that the group’s development phase determines the way that outcome interdependence density in teams is associated with satisfaction and reveal the existence of differences between the second and fourth development stages. Therefore, the group’s life cycle stage should be considered as a relevant variable in research and intervention with groups in the workplace.     

MIMCA: multiple imputation for categorical variables with multiple correspondence analysis

We propose a multiple imputation method to deal with incomplete categorical data. This method imputes the missing entries using the principal component method dedicated to categorical data: multiple correspondence analysis (MCA). The uncertainty concerning the parameters of the imputation model is reflected using a non-parametric bootstrap. Multiple imputation using MCA (MIMCA) requires estimating a small number of parameters due to the dimensionality reduction property of MCA. It allows the user to impute a large range of data sets. In particular, a high number of categories per variable, a high number of variables or a small number of individuals are not an issue for MIMCA. Through a simulation study based on real data sets, the method is assessed and compared to the reference methods (multiple imputation using the loglinear model, multiple imputation by logistic regressions) as well to the latest works on the topic (multiple imputation by random forests or by the Dirichlet process mixture of products of multinomial distributions model). The proposed method provides a good point estimate of the parameters of the analysis model considered, such as the coefficients of a main effects logistic regression model, and a reliable estimate of the variability of the estimators. In addition, MIMCA has the great advantage that it is substantially less time consuming on data sets of high dimensions than the other multiple imputation methods.     

Invisible computer for collaborative design: evaluation of a multimodal sketch-based environment

In the areas of design, especially in architectural design, collaboration has become an important challenge. The specialization of skills increase, work teams are more and more extensive and the geographic distance between them increases too. The economic and ecological stakes related to remote collaboration are an evidence. This context involves the need to support most efficiently possible remote working meetings. We present the Distributed Collaborative Digital Studio (DSDC), a tool designed to recreate, in distant situations, the context of copresence meetings. This shared environment is created in the "invisible computer" approach [11]. The idea is that the tool should disappear from user's consciousness. Indeed, creative design activities require some fluidity in their process. Therefore, any involuntary interruption created by the system can potentially brake creativity. In this perspective, we investigate specifically the "invisibility" of our environment. To do this, we propose a framework for the operationalization of the concept and a methodology to test the system invisibility. This methodology was applied through a case study consisting of a corpus of 12 hours of remote collaborative design sessions with the DSDC. We highlight the learning effects while using our system, conclude on its effectiveness and discuss our methodology.     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.