Abstract: On 14 May 2024, a short course with Jitka Machalova and KarelHron from "Palacký University Olomouc, Czech Republic" and Alessandra Menafoglio from "Politecnico di Milano, Italy" on the topic of Bayes spaces and their recent developments will be held in presence at the "Dipartimento di Studi per l'Economia e l'Impresa" of the "Università del Piemonte Orientale" and streamed online.

The analysis of distributional data (probability density functions or histogram data) has recently gained increasing attention in the applications. Distributional data are often observed by themselves, or as result of aggregation of large streams of data. The short course will provide an introduction to the analysis of these data using a Functional Data Analysis (FDA) approach, grounded on the perspective of Bayes spaces. These spaces are mathematical spaces whose points are densities (or, more generally, measures), which generalize to the FDA setting the Aitchison simplex for multivariate compositional data. The course will give an overview of the theory of Bayes spaces in their univariate and multivariate settings. All FDA methods, presented there, will be illustrated through examples from real case studies.

The short course, that will be streamed live, will provide an opportunity to gain new insight into FDA of univariate and multivariate density functions through lens of the Bayes spaces methodology.

• date: 14 May 2024

• time: 10.30 - 13.30 (time zone Europe/Rome)

• where: in-presence room 207, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale

• on-line: https://meet.google.com/qse-iafg-wim

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Abstract: TBA

- data e orario: TBA

- aula TBA

- on-line:

Abstract: The fuzzy approach to clustering arises to cope with situations where objects have not a clear assignment. Unlike the hard/standard approach where each object can only belong to exactly one cluster, in a fuzzy setting, the assignment is soft; that is, each object is assigned to all clusters with certain membership degrees varying in the unit interval. The best known fuzzy clustering algorithm is the fuzzy k-means (FkM), or fuzzy c-means. It is a generalization of the classical k-means method. Starting from the FkM algorithm, and in more than 40 years, several variants have been proposed. The peculiarity of such different proposals depends on the type of data to deal with, and on the cluster shape. The aim is to show fuzzy clustering alternatives to manage different kinds of data, ranging from numerical, categorical or mixed data to more complex data structures, such as interval-valued, fuzzy-valued or functional data, together with some robust methods. Furthermore, the case of two-mode clustering is illustrated in a fuzzy setting.

- data e orario: 8 Febbraio 2024 ore 11.30

- aula 206, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract: Calibration of option pricing models to the implied volatility surface is a complicated, yet fundamental task in the quantitative finance community. By exploiting Sequential Monte Carlo (SMC) methods we turn the standard calibration problem into a Bayesian estimation task. In this way we can construct a sequence of distributions from the prior to the posterior which allows to compute any statistic of the estimated parameters, to overcome the strong dependence on the starting point and to avoid troublesome local minima; all of which are typical plagues of the standard calibration. To highlight the strength of our approach we consider the calibration of the double jump stochastic volatility model of Duffie et. al (2000) both on simulated and real option data. From the results on both single dates and time series of implied volatilities we find that our Bayesian approach largely outperforms the benchmark in terms of run time-accuracy, option pricing errors and statistical fit. Finally, we show how to further speed up computations by leveraging delayed-acceptance MCMC methods and deep learning. This is a joint work with R. Brignone, S. Knaust and E. Lutkebohmert, University of Friburg. 

- data e orario: 6 Febbraio 2024 ore 14.00

- aula 206, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract: The talk will deal with the key challenge of creating prediction bands for a new observation in the functional data framework given a training set of observed functional data and possibly in presence of covariates, either scalar, categorical, or functional. Starting from the investigation of the literature concerning this topic, we propose an innovative approach building on top of Conformal Prediction and Functional Data Analysis able to overcome the main drawbacks associated to the existing approaches. Under minimal distributional assumptions (i.e., exchangeability of the random functions), we will show how the new proposed nonparametric method (i) is able to provide prediction regions which could visualized in the form of bands, (ii) is guaranteed with exact coverage probability also for finite sample sizes, and finally (iii) is computational efficient. Different specifications of the method will be compared in terms of efficiency in some simulated and real case scenarios also in the case of multi-dimensional domain and/or codomain.

- data e orario: 22 novembre 2023 ore 16.00

- Aula 206, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract:   This work analyzes how ESG ratings are aligned with climate risk as determined through various carbon indicators. In addition, the paper analyzes the effect of the level of sustainability and climate risk on the flows of funds and performance before and after the pandemic. The results show that there is an alignment between sustainability and climate change indicators when using the environmental pillar. Furthermore, it is found that the relationship between the level of sustainability, carbon indicators and investment flows is conditioned by the economic context, for example in the case of profitability. Our results lend support to the observation that investors invest in sustainability generally expecting higher returns and when these expectations are not met, they are willing to invest in unsustainable funds, with pecuniary motives predominating over non-pecuniary ones. 

Giovedì 20 Luglio  2023 ore 11.45

Aula 103, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract:  An important task in regression analysis is to choose the right specification of the link function that models the dependence structure between the random elements. A challenge arises in the framework of scalar on function regression as the link function is a real-valued operator acting on a functional space, and it is difficult to visualize and hence select a coherent specification. A specification test that uses a semi-parametric approach is proposed, in particular by exploiting the Single Functional Index Model. The test statistic is a special form of U-statistic; its asymptotic null distribution is derived under suitable assumptions, and consistency is proved for a specific class of alternatives. The finite sample performances of the test are evaluated through a simulation study using both asymptotic p-values and some bootstrap approaches. An application of the method developed to a problem commonly arises in the food industry is performed to demonstrate the potentialities of the method.

martedì 4 Luglio 2023 ore 11.00

aula 101, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract: The idea of this paper is to investigate differences in brown and green bonds from the lens of the trading activity. Our research exploits the idea that positive and negative jumps in the dynamics of returns have a specific memory nature that can be modelled through a self-exciting process. We investigate the microscopic structure and properties of high-frequency series of green and brown bonds using Hawkes type processes where the kernel is a CARMA(p,q) model. Empirical results suggest that the bid-ask spread of green bonds on average is larger for bonds issued by a financial institution while the opposite happens for bonds issued by a non financial company. Moreover, we observe that the intensities respectively of positive and negative jumps in the price dynamics are not stationary through time. Higher order bivariate Hawkes models provide better fitting results in our datasets, especially for the issuer that operates in the energy sector.  This presentation is based on a joint work with Lorenzo Mercuri (University of Milan) and Andrea Perchiazzo (Vrije Universiteit Brussel).

14 Giugno 2023 ore 11.00

aula 101, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract: Classical inference in statistics is typically carried out by means of standard (first-order) asymptotic theory. However, the asymptotic distribution of estimators and test statistics can provide a poor approximation of tail areas especially when the sample size is moderate to small. This can lead to inaccurate p-values and confidence intervals.
Several techniques, both parametric and nonparametric, have been devised to improve first-order asymptotic approximations, including e.g. Edgeworth expansions, Bartlett's corrections, and bootstrap methods. Here we focus on saddlepoint techniques, introduced into statistics by H. Daniels, and more generally on small sample asymptotic techniques, an expression coined by F. Hampel to express the spirit of these methods. Indeed they provide very accurate approximations of tail probabilities down to small sample sizes and /or out in the tails. Moreover, these approximations exhibit a relative error of order 1/n, an improvement with respect to other available approximations obtained by means of Edgeworth expansions and similar techniques.
We will review the basic ideas, show the link with other nonparametric methods such as empirical likelihood, and outline some connections to information theory and optimal transportation.

31 gennaio 2023 ore 16.30

aula 101, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract: Over recent years, text modeling techniques have been employed in several applications, including the detection of latent topics in text documents. A widespread statistical tool for topic modeling is the Latent Dirichlet Allocation (LDA), which allows for a document representation in terms of topic composition. A well-known limitation of the LDA is related to the stiffness of the Dirichlet prior imposed on the topic distributions. To consider a richer dependence structure, we propose a generalization of the Dirichlet distribution as an alternative distribution, namely the flexible Dirichlet (FD). The FD is a distribution defined on the simplex space allowing for a finite mixture structure. This choice introduces additional parameters in the LDA, and ensures more flexibility, still maintaining the model interpretability, as well as conjugacy to the multinomial model. The latter property allows for a Collapsed Gibbs Sampling-based estimation procedure. The generalization of the LDA based on the FD distribution is illustrated via an application to a real dataset.

14 dicembre 2022 ore 16.00

aula 203, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract: We model sovereign debt sustainability with optimal financing decisions under macroeconomic, financial, and fiscal uncertainty, with endogenous risk and term premia. Using a coherent risk measure we trade off debt stock and flow risks subject to sustainability constraints. We optimize static and dynamic financing strategies and demonstrate economically significant savings from optimal financing compared with simple rules and consols, and find that optimizing the trade-offs can be critical for sustainability.

6 dicembre 2022 ore 16.00

aula 101, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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Abstract: In this talk, we present a new framework of additive regression modelling for data in very generic settings. More precisely, we tackle the problem of estimating component functions of additive models where the regressors and/ or response variable belong to general Hilbert spaces and can be imperfectly observed. By this, we mean that some variables can be either measured incompletely or with errors. Smooth backfitting methods are used to estimate consistently the component functions and we provide explicit rates of convergence. We amply illustrate our methodology in various settings, including the functional, Riemannian and Hilbertian settings.

8 novembre 2022 ore 16.00

aula 201, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

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