179,073 research outputs found
Alternative distribution based GARCH models for Bitcoin volatility estimation
No full textKatsiampa (2017) shows that, among different GARCH models, the optimal conditional heteroskedasticity model regarding the goodness-of-fit to Bitcoin price data is the AR-Component GARCH (AR-CGARCH) model. However, in that paper the author does not take into account for statistical proprieties of Bitcoin’s return distribution, and even showing both skewness and non-normality of the data, we consider a standardized normal distribution for all studied GARCH models. This paper represents an improvement of the previous literature about GARCH model for Bitcoin. In particular, this paper examines different distributional assumptions about innovations distribution for some GARCH models, showing that it is possible to obtain better estimates through the AR(1)-APARCH(1,1) model assuming that innovations follow a t-student distribution
Multicollinearity in regression: an efficiency comparison between Lp-norm and least squares estimators
No full textMulticollinearity is one of the most important issues in regression analysis, as it produces unstable coefficients’ estimates and makes the standard errors severely inflated. The regression theory is based on specific assumptions concerning the set of error random variables. In particular, when errors are uncorrelated and have a constant variance, the ordinary least squares estimator produces the best estimates among all linear estimators. If, as often happens in reality, these assumptions are not met, other methods might give more efficient estimates and their use is therefore recommendable. In this paper, after reviewing and briefly describing the salient features of the methods, proposed in the literature, to determine and address the multicollinearity problem, we introduce the Lpmin method, based on Lp-norm estimation, an adaptive robust procedure that is used when the residual distribution has deviated from normality. The major advantage of this approach is that it produces more efficient estimates of the model parameters, for different degrees of multicollinearity, than those generated by the ordinary least squares method. A simulation study and a real-data application are also presented, in order to show the better results provided by the Lpmin method in the presence of multicollinearity
Sezione Islam e Mediterraneo: donne, migranti, diritti
No full textIl numero monografico della rivista tratta diverse tematiche relative all'islam nel Mediterraneo, con particolare riferimento alle migrazioni dei marocchini, ai matrimoni misti, al diritto islamico, con la partecipazione di antropologi italiani e stranieri
Model-based fuzzy time series clustering of conditional higher moments
No full textThis paper develops a new time series clustering procedure allowing for heteroskedasticity, non-normality and model's non-linearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelation-based fuzzy C-means (A-FCM) algorithm. The DCS parametric modeling is appealing because of its generality and computational feasibility. The usefulness of the proposed procedure is illustrated using an experiment with simulated data and several empirical applications with financial time series assuming both linear and nonlinear models' specification and under several assumptions about time series density function
Cyclodextrin-[60]fullerene conjugates: Synthesis, characterization and electrochemical behavior
No full textA Generalized Error Distribution Copula-based method for portfolios risk assessment
No full textIn this paper, we deal with the evaluation of Conditional Value-at-Risk in the framework of portfolio theory by using a modified Gaussian Copula – where the modification is obtained by introducing the Generalized Correlation Coefficient – and by assuming a GeneralizedErrorDistributionwithproperlyestimatedshapeparameterpforthereturns of the considered risky assets. In so doing, we add to the connection between standard Copula theory and financial risk assessment. A comparison analysis of our findings with those obtainable through a standard Gaussian Copula-based procedure in a set of real data is also presented
Premessa
No full textSi presentano le direzioni seguite dalla ricerca e i contributi presentati nel test
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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