Estimation of k-Factor Gigarch Process: A Monte Carlo Study

In this paper, we discuss the parameter estimation for a k-factor generalized long memory processwith conditionally heteroskedastic noise. Two estimation methods are proposed. The first method is based on the conditional distribution of the process and the second is obtained as an extension of Whittle's estimation approach. For comparison purposes, Monte Carlo simulations are used to evaluate the finite sample performance of these estimation techniques, using four different conditional distribution functions.Long memory ; Gegenbauer polynomial ; heteroskedasticity ; Conditional Sum of Squares ; Whittle estimation

Measuring the contribution of extractive industries to local development : the case of oil companies in Nigeria

Extractive industries face two main challenges in terms of CSR and poverty reduction: 1) recognize that societal activity is part of their core business; 2) take part in socio-economic projects that contribute to their stakeholders' empowerment and not only to their living conditions. Based on surveys achieved in Nigeria in 2008, the paper presents two societal performance indices meant to be complementary: the Poverty Exit Index (PEI) and the Relational Capability Index (RCI). We show that, while they have fostered the PEI of the local communities, the development projects of the oil companies had a rather negative impact on their RCI. We then identify key variables that can influence positively the RCI and on which a sensible development policy should focus.development indices ; capability approach ; relational capability ; development ; poverty ; impact assessment

Measuring the contribution of extractive industries to local development : the case of oil companies in Nigeria

ESSEC Working paper. Document de Recherche ESSEC / Centre de recherche de l'ESSEC ISSN : 1291-9616 WP 1109Extractive industries face two main challenges in terms of CSR and poverty reduction: 1) recognize that societal activity is part of their core business; 2) take part in socio-economic projects that contribute to their stakeholders' empowerment and not only to their living conditions. Based on surveys achieved in Nigeria in 2008, the paper presents two societal performance indices meant to be complementary: the Poverty Exit Index (PEI) and the Relational Capability Index (RCI). We show that, while they have fostered the PEI of the local communities, the development projects of the oil companies had a rather negative impact on their RCI. We then identify key variables that can influence positively the RCI and on which a sensible development policy should focus

Modélisation longue mémoire multivariée : applications aux problématiques du producteur d'EDF dans le cadre de la libéralisation du marché européen de l'électricité

Certain crucial financial time series, such as the interconnected european electricity market spot prices, exhibit long memory, in the sense of slowly decaying correlations combined with heteroskedasticity and periodic or none cycles. In modeling such behavior, we consider on one hand, the k factor GIGARCH process and additionally propose two methods to address the related parameter estimation problem. In each method, we explore the asymptotic theory for estimation. Moreover, the asymptotic properties are validated and compared via Monte Carlo simulations. On the other hand, we introduce a new multivariate long memory generalized model (kfactor MVGARMA) in order to model interconnected european electricity market spot prices. We sugger a practical framework to address the parameter estimation problem. We investigate the analytical expressions of the least squares predictors for the two proposed models and their confidence intervals. To finish, we apply the two proposed models to the french and german electricity market spot prices and a comparison is made between their forecasting abilities.Plusieurs données de marchés financiers, telles que les prix spot de marchés européens de l'électricité interconnectés, présentent de la longue mémoire, au sens de la décroissance hyperbolique des autocorrélations combinée avec un phénomène d'hétéroskédasticité et de cycles périodiques ou non. Pour modéliser de tels comportements, nous introduisons d'une part les processus GIGARCH à k facteurs et nous proposons deux méthodes d'estimation des paramètres. Nous développons les propriétés asymptotiques des estimateurs de chacune des méthodes. De plus, afin de comparer les propriétés asymptotiques des estimateurs, des simulations de Monté Carlo sont effectuées. D'autre part, nous proposons un modèle longue mémoire généralisé multivarié (MVGARMA à k facteurs) pour modéliser conjointement deux marchés européens de l'électricité interconnectés. Nous donnons une procédure pratique d'estimation des paramètres. Pour la prévision, nous fournissons les expressions analytiques des prédicteurs de moindres carrés pour les modèles proposés et les intervalles de confiance des erreurs de prévision. Enfin, nous appliquons ces deux modèles sur les prix spot de l'électricité des marchés français et allemand et nous comparons leurs capacités prédictives

Adaptive Hyperbolic Asymmetric Power ARCH (A-HY-APARCH) model: Stability and Estimation

In this paper, a new asymmetric GARCH type model that generalizes the Hyperbolic Asymmetric Power ARCH (HY-APARCH) process is proposed. The proposed model takes into consideration some characteristics of financial time series data like volatility clustering, long memory and structural changes. The necessary and sufficient conditions for the asymptotic stability of the model are derived and parameter estimation methods are proposed. The Monte Carlo Simulations are done to prove the performance of the estimation method. Key words: long range dependence; structural changes; HYAPARCH.&nbsp

BL-GARCH models with elliptical distributed innovations

In this work, we discuss the class of bilinear GARCH (BL-GARCH) models that are capable of capturing simultaneously two key properties of non-linear time series: volatility clustering and leverage effects. It has often been observed that the marginal distributions of such time series have heavy tails; thus we examine the BL-GARCH model in a general setting under some non-normal distributions. We investigate some probabilistic properties of this model and we conduct a Monte Carlo experiment to evaluate the small-sample performance of the maximum likelihood estimation (MLE) methodology for various models. Finally, within-sample estimation properties were studied using S&P 500 daily returns, when the features of interest manifest as volatility clustering and leverage effects. The main results suggest that the Student-t BL-GARCH seems highly appropriate to describe the S&P 500 daily returns

A classification method for binary predictors combining similarity measures and mixture models

In this paper, a new supervised classification method dedicated to binary predictors is proposed. Its originality is to combine a model-based classification rule with similarity measures thanks to the introduction of new family of exponential kernels. Some links are established between existing similarity measures when applied to binary predictors. A new family of measures is also introduced to unify some of the existing literature. The performance of the new classification method is illustrated on two real datasets (verbal autopsy data and handwritten digit data) using 76 similarity measures

A classification method for binary predictors combining similarity measures and mixture models

International audienceIn this paper, a new supervised classification method dedicated to binary predictors is proposed. Its originality is to combine a model-based classification rule with similarity measures thanks to the introduction of new family of exponential kernels. Some links are established between existing similarity measures when applied to binary predictors. A new family of measures is also introduced to unify some of the existing literature.The performance of the new classification method is illustrated on two real datasets (verbal autopsy data and handwritten digit data) using 76 similarity measures

High-dimensional supervised classification in a context of non-independence of observations to identify the determining SNPs in a phenotype

This work addresses the problem of supervised classification for highly correlated high-dimensional data describing non-independent observations to identify SNPs related to a phenotype. We use a general penalized linear mixed model with a single random effect that performs simultaneous SNP selection and population structure adjustment in high-dimensional prediction models. Specifically, the model simultaneously selects variables and estimates their effects, taking into account correlations between individuals.Single nucleotide polymorphisms (SNPs) are a type of genetic variation and each SNP represents a difference in a single DNA building block, namely a nucleotide. Previous research has shown that SNPs can be used to identify the correct source population of an individual and can act in isolation or simultaneously to impact a phenotype. In this regard, the study of the contribution of genetics in infectious disease phenotypes is of great importance.In this study, we used uncorrelated variables from the construction of blocks of correlated variables done in a previous work to describe the most related observations of the dataset. The model was trained with 90% of the observations and tested with the remaining 10%. The best model obtained with the generalized information criterion (GIC) identified the SNP named rs2493311 located on the first chromosome of the gene called PRDM16 ((PR/SET domain 16)) as the most decisive factor in malaria attacks