Search results for "Autore"

ESSAYS ON FINANCIAL STRESS: A MIXED FREQUENCY DATA ANALYSIS

"Corporate governance": approcci manageriali ed etici a confronto

2009

La crisi di fiducia che colpì il sistema delle imprese statunitense nei primi anni '70 ha promosso l'elaborazione di due distinte concezioni della corporate governance. La prima ha come referente privilegiato azionisti ed investitori ed elegge il mercato (azionario) come lo strumento principale per il governo dell'impresa. L'obiettivo di tale concezione è quello di riportare il management aziendale sotto il controllo degli azionisti. La seconda cerca invece di identificare una prospettiva che tiene nella dovuta considerazione preferenze, bisogni e diritti di tutti gli stakeholders coinvolti nel processo produttivo. Riprendendo la distinzione proposta da Hirschman, possiamo dire che mentre l…

Dynamic network identification from non-stationary vector autoregressive time series

2018

Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes. To cope with the time-varying nature of interactions, this paper develops an estimation criterion and a solver to learn the parameters of a time-varying vector autoregressive model supported on a network of time series. The notion of local breakpoint is proposed to accommodate changes at individu…

Online Topology Identification from Vector Autoregressive Time Series

2019

Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes an alternative to the well-known, yet usually intractable, Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Their constant complexity per update also renders these a…

Online Non-linear Topology Identification from Graph-connected Time Series

2021

Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and finance engineering. Inference of such causal dependencies, often know as topology identification, is not well studied for non-linear non-stationary systems, and most of the existing methods are batch-based which are not capable of handling streaming sensor signals. In this paper, we propose an online kernel-based algorithm for topology estimation of non-linear vector autoregressive time series by solving a sparse online optimization framework using the composite objective mirror descent…

Support Vector Machines Framework for Linear Signal Processing

2005

This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of I…

Panel Conditioning or SOCRATIC EFFECT REVISITED: 99 Citations, but is there Theoretical Progress?

2020

In a paper published as early as 1987 by Jagodzinski, Kuhnel and Schmidt on attitude measurement in a three wave panel study, we established empirically a general orientation toward foreign employees in Western Germany called “Gastarbeiter”. These items have been continuously used from 1980 till now in the ALLBUS studies (Wasmer and Hochman 2019). In this paper, we have analyzed how the citation, explanation and modeling of the Socratic effect for explaining changes in panel data developed over time starting with the original paper of Jagodzinski et al. (1987). According to Google Scholar retrieved at 24.1.2019, 99 citations were found, which are all listed in the Online Supplementary. From…

Joint Graph Learning and Signal Recovery via Kalman Filter for Multivariate Auto-Regressive Processes

2018

In this paper, an adaptive Kalman filter algorithm is proposed for simultaneous graph topology learning and graph signal recovery from noisy time series. Each time series corresponds to one node of the graph and underlying graph edges express the causality among nodes. We assume that graph signals are generated via a multivariate auto-regressive processes (MAR), generated by an innovation noise and graph weight matrices. Then we relate the state transition matrix of Kalman filter to the graph weight matrices since both of them can play the role of signal propagation and transition. Our proposed Kalman filter for MAR processes, called KF-MAR, runs three main steps; prediction, update, and le…

Weather Derivatives and Stochastic Modelling of Temperature

2011

We propose a continuous-time autoregressive model for the temperature dynamics with volatility being the product of a seasonal function and a stochastic process. We use the Barndorff-Nielsen and Shephard model for the stochastic volatility. The proposed temperature dynamics is flexible enough to model temperature data accurately, and at the same time being analytically tractable. Futures prices for commonly traded contracts at the Chicago Mercantile Exchange on indices like cooling- and heating-degree days and cumulative average temperatures are computed, as well as option prices on them.

On Independent Component Analysis with Stochastic Volatility Models

2017

Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to…