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

Market Mood, Adaptive Beliefs and Asset Price Dynamics

Empirical evidence has suggested that, facing different trading strategies and complicated decision, the proportions of agents relying on particular strategies may stay at constant level or vary over time. This paper presents a simple "dynamic market fraction" model of two groups of traders, fundamentalists and trend followers, under a market maker scenario. Market mood and evolutionary adaption are characterized by fixed and adaptive switching fraction among two groups, respectively. Using local stability and bifurcation analysis, as well as numerical simulation, the role played by the key parameters in the market behaviour is examined. Particular attention is payed to the impact of the market fraction, determined by the fixed proportions of confident fundamentalists and trend followers, and by the proportion of adaptively rational agents, who adopt different strategies over time depending on realized profits.

Revisiting the Kantian legacy in Habermas: the philosophical project of modernity and decolonial critiques to rationality and cosmopolitanism

This paper deals with Amy Allen’s critique of Habermas's theory of modernity, democracy, and cosmopolitanism. I will focus on her arguments that touch on the role of rationality. Allen's critique of Habermas will be presented, especially where she argues that focusing on rationality is ethnocentric and promotes the political exclusion of subaltern groups. The extent to which Allen's critiques affect the emancipatory potential of Habermasian theory will next be assessed. It is finally argued that Allen's position leads to a denial of the distinction between social rationality and irrationality as legitimate criteria. The consequences of such a position for the political sphere will then be analyzed

Acute Arterial Embolism of the Lower Limb

Despite advances in the management of peripheral arterial occlusive disease, acute embolism of the lower extremities is still characterized by an important limb threat, morbidity, mortality, and continues to pose a challenge to the vascular surgeon. Atrial fibrillation, left ventricular aneurysm, penetrating ulcers or aneurysms of the aorta and common iliac arteries are the common sources of emboli. The presence of occlusion can be determined noninvasively with the use of duplex Doppler ultrasonography. Arteriography, Computed Tomographic Angiography and Magnetic Resonance Angiography can also be employed. Embolectomy is the standard for acute leg ischemia in patients with a strong clinical suspicion of an embolus, but alternative techniques, such as catheter-directed thrombolysis or percutaneous aspiration thrombolectomy, expand the role of radiologic percutaneous therapy of the acutely ischemic limb

Explaining Deviations from Okun’s Law

Despite its stability over time, as for any statistical relationship, Okun’s law is subject to deviations that can be large at times. In this paper, we provide a mapping between residuals in Okun’s regressions and structural shocks identified using a SVAR model by inspecting how unemployment responds to the state of the economy. We show that deviations from Okun’s law are a natural and expected outcome once one takes a multi-shock perspective, as long as shocks to automation, labor supply and structural factors in the labor market are taken into account. Our simple recipe for policy makers is that, if a positive deviation from Okun’s law arises, it is likely to be generated by either positive labor supply or automation shocks or by negative structural factors shocks.publishedVersio

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

Are low frequency macroeconomic variables important for high frequency electricity prices?

We analyse the importance of low frequency hard and soft macroeconomic information, respectively the industrial production index and the manufacturing Purchasing Managers' Index surveys, for forecasting high-frequency daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse unrestricted MIDAS models (RU-MIDAS). Despite the general parsimonious structure of standard MIDAS models, the RU-MIDAS has a large set of parameters when several predictors are considered simultaneously and Bayesian inference is useful for imposing parameter restrictions. We study the forecasting accuracy for different horizons (from $1$ day ahead to $28$ days ahead) and by considering different specifications of the models. Results indicate that the macroeconomic low frequency variables are more important for short horizons than for longer horizons. Moreover, accuracy increases by combining hard and soft information, and using only surveys gives less accurate forecasts than using only industrial production data.Comment: This paper has previously circulated with the title: "Forecasting daily electricity prices with monthly macroeconomic variables" (ECB Working paper Series No. 2250

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