New insights on hidden Markov models for time series data analysis

The 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

The role of cryptocurrencies within the financial systems has been expanding rapidly in recent years among investors and institutions. It is therefore crucial to investigate the phenomena and develop statistical methods able to capture their interrelationships, the links with other global systems, and, at the same time, the serial heterogeneity. For these reasons, this paper introduces 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.Comment: 35 pages, 6 figures. arXiv admin note: text overlap with arXiv:2301.0972

The network of commodity risk

In 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

In 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

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

Using expectile regression with latent variables for digital assets

In this paper we introduce a linear expectile hidden Markov model with the goal of modeling the entire conditional distribution of asset returns and, at the same time, to grasp unobserved serial heterogeneity and rapid volatility jumps typical of financial time series. The temporal evolution of asset returns is captured by introducing time-dependent coefficients evolving according to a latent discrete homogeneous Markov chain. To implement the procedure, we consider the Asymmetric Normal distribution as a working likelihood for the estimation of model parameters and the estimation procedure is carried out using an efficient EM algorithm. The empirical application investigates the relationship between daily Bitcoin returns and major world market indices

Graphical Models for Commodities: A Quantile Approach

The high level of integration of international financial markets highlights the need to accurately assess contagion and systemic risk under different market conditions. To this end, we develop a quantile graphical model to identify the tail conditional dependence structure in multivariate data across different quantiles of the marginal distributions of the variables of interest. To implement the procedure, we consider the Multivariate Asymmetric Laplace distribution and exploit its location-scale mixture representation to build a penalized EM algorithm for estimating the sparse precision matrix of the distribution by means of an L1 penalty. The empirical application is performed on a large set of commodities representative of the energy, agricultural and metal sectors

Analyzing the Correlation Structure of Financial Markets Using a Quantile Graphical Model

In questo articolo sviluppiamo un modello grafico quantile per identificare la struttura di correlazione condizionata di coda attraverso lo studio dei quantili delle distribuzioni marginali delle variabili di interesse. Per implementare la procedura, consideriamo la distribuzione di Laplace asimmetrica multivariata e sfruttiamo la sua rappresentazione a mistura per costruire un algoritmo EM penalizzato per la stima della matrice di precisione sparsa della distribuzione mediante una penalita` L1. La metodologia presentata viene applicata sui rendimenti finanziari dei principali indici di mercato, criptovalute e materie prime.In this paper we develop a quantile graphical model to identify the tail conditional correlation structure in multivariate data across different quantiles of the marginal distributions of the variables of interest. To implement the procedure, we consider the Multivariate Asymmetric Laplace distribution and exploit its location-scale mixture representation to build a penalized EM algorithm for estimating the sparse precision matrix of the distribution by means of an L1 penalty. The empirical application is performed on a set of market indexes, cryptocurrencies and commodities

GLASSO Estimation of Commodity Risks

In 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

In Ph+BCR-ABL1P210+ acute lymphoblastic leukemia the e13a2 (B2A2) transcript is prevalent

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