1,720,962 research outputs found
New insights on hidden Markov models for time series data analysis
Full text linkThe goal of this thesis is to develop novel methods for the analysis of financial data by using hidden Markov models based approaches. The analysis focuses on univariate and multivariate financial time series, modeling interrelationships between financial returns throughout different statistical methods, such as graphical models, quantile and expectile regressions. The dissertation is divided into three chapters, each of them examining different classes of assets returns for a comprehensive risk analysis. The methodologies we propose are illustrated using real-world data and simulation studies
Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market
No full textThe role of cryptocurrencies within the financial systems has been expanding rapidly in recent
years among investors and institutions. It is therefore crucial to investigate this phenomenon and develop
statistical methods able to capture their interrelationships, the links with other global systems and,
at the same time, the serial heterogeneity. Here we introduce hidden Markov regression models for
jointly estimating quantiles and expectiles of cryptocurrency returns using regime-switching copulas.
The proposed approach allows us to focus on extreme returns and describe their temporal evolution by
introducing time-dependent coefficients, evolving according to a latent Markov chain. Moreover to model
their time-varying dependence structure, we consider elliptical copula functions defined by state-specific
parameters. Maximum likelihood estimates are obtained via an expectation-maximization algorithm.
The empirical analysis investigates the relationship between daily returns of five cryptocurrencies and
major world market indices
The network of commodity risk
Full text linkIn this paper, we investigate the interconnections among and within the Energy, Agricultural, and Metal commodities, operating in a risk management framework with a twofold goal. First, we estimate the Value-at-Risk (VaR) employing GARCH and Markov-switching GARCH models with different error term distributions. The use of such models allows us to take into account well-known stylized facts shown in the time series of commodities as well as possible regime changes in their conditional variance dynamics. We rely on backtesting procedures to select the best model for each commodity. Second, we estimate the sparse Gaussian Graphical model of commodities exploiting the Graphical LASSO (GLASSO) methodology to detect the most relevant conditional dependence structure among and within the sectors. A novel feature of our framework is that GLASSO estimation is achieved exploring the precision matrix of the multivariate Gaussian distribution obtained using a Gaussian copula with marginals given by the residuals of the aforementioned selected models. We apply our approach to the sample of twenty-four series of commodity futures prices over the years 2005–2022. We find that Soybean Oil, Cotton, and Coffee represent the major sources of propagation of financial distress in commodity markets while Gold, Natural Gas UK, and Heating Oil are depicted as safe-haven commodities. The impact of Covid-19 is reflected in increased heterogeneity, as captured by the strongest relationships between commodities belonging to the same commodity sector and by weakened inter-sectorial connections. This finding suggests that connectedness does not always increase in response to crisis events
Expectile hidden Markov regression models for analyzing cryptocurrency returns
Full text linkIn this paper we develop a linear expectile hidden Markov model for the
analysis of cryptocurrency time series in a risk management framework. The
methodology proposed allows to focus on extreme returns and describe their
temporal evolution by introducing in the model time-dependent coefficients
evolving according to a latent discrete homogeneous Markov chain. As it is
often used in the expectile literature, estimation of the model parameters is
based on the asymmetric normal distribution. Maximum likelihood estimates are
obtained via an Expectation-Maximization algorithm using efficient M-step
update formulas for all parameters. We evaluate the introduced method with both
artificial data under several experimental settings and real data investigating
the relationship between daily Bitcoin returns and major world market indices
GLASSO Estimation of Commodity Risks
No full textIn this paper we apply the Graphical LASSO (GLASSO) procedure to
estimate the network of twenty-four commodities divided in energy, agricultural
and metal sector. We follow a risk management perspective. We use GARCH and
Markov-Switching GARCH classes of models with different specifications for the
error terms, and we select those that best estimate Value-at-Risk for each commodity. We achieve GLASSO estimation exploring the precision matrix of the multivariate Gaussian distribution obtained from a Gaussian Copula, with marginals given
by the residuals of the models, selected via backtesting procedure. The analysis of
interdependences in the resulting network is carried out by using the eigenvector
centrality metric
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe 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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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