793 research outputs found
Bayesian Model Selection for Beta Autoregressive Processes
We deal with Bayesian inference for Beta autoregressive processes. We
restrict our attention to the class of conditionally linear processes. These
processes are particularly suitable for forecasting purposes, but are difficult
to estimate due to the constraints on the parameter space. We provide a full
Bayesian approach to the estimation and include the parameter restrictions in
the inference problem by a suitable specification of the prior distributions.
Moreover in a Bayesian framework parameter estimation and model choice can be
solved simultaneously. In particular we suggest a Markov-Chain Monte Carlo
(MCMC) procedure based on a Metropolis-Hastings within Gibbs algorithm and
solve the model selection problem following a reversible jump MCMC approach
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A Stochastic Volatility Model With Realized Measures for Option Pricing
Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence—the crucial parameter in pricing Standard and Poor’s 500 Index options
eWOM and growth strategies for the tourism industry in maritime museum networks. The case of the ARCA Adriatica tourist product
Museum networks are proliferating in the Mediterranean area showing new forms of collaboration between public and private institutions. Museums and heritage conservation play a fundamental role in tourism development. The purpose of the present working paper is to provide an analysis of the museum network experience in order to define a set of useful and viable marketing strategies to be adopted by the museum management with respect to the relative tourist context. The case of the Arca Adriatica maritime museum network - a network of eight maritime museums representing the core asset of an elaborated tourist product - has been analyzed and considered particularly relevant and of peculiar interest. After the analysis of the museum network and its most important related points of interest, managerial recommendations within strategic and tactical perspectives are hence presented
Hierarchical Species Sampling Models
This paper introduces a general class of hierarchical nonparametric prior distributions. The random probability measures are constructed by a hierarchy of generalized species sampling processes with possibly non-diffuse base measures. The proposed framework provides a general probabilistic foundation for hierarchical random measures with either atomic or mixed base measures and allows for studying their properties, such as the distribution of the marginal and total number of clusters. We show that hierarchical species sampling models have a Chinese Restaurants Franchise representation and can be used as prior distributions to undertake Bayesian nonparametric inference. We provide a method to sample from the posterior distribution together with some numerical illustrations. Our class of priors includes some new hierarchical mixture priors such as the hierarchical Gnedin measures, and other well-known prior distributions such as the hierarchical Pitman-Yor and the hierarchical normalized random measures
eWom and sentiment analysis to support decision processes within maritime heritage museum networks. The case of Arca Adriatica
Museum networks are proliferating in the Mediterranean area showing new forms of collaboration between public and private institutions. Museums and heritage conservation play a fundamental role in tourism development. The purpose of the present working paper is to provide an analysis of the museum network experience in order to define a set of useful and viable marketing strategies to be adopted by the museum management with respect to the relative tourist context. The case of the Arca Adriatica maritime museum network - a network of eight maritime museums representing the core asset of an elaborated tourist product - has been analyzed and considered particularly relevant and of peculiar interest. After the analysis of the museum network and its most important related points of interest, managerial recommendations within strategic and tactical perspectives are hence presented
Dynamic identification of the Qutb Minar, New Delhi, India
Eu-India Economic Cross
Cultural Programme “Improving the Seismic Resistance of Cultural Heritage Buildings” - Contract
ALA-95-23-2003-077-122.Central Building Research Institute, Roorkee, India.Technical
University of Catalonia, Barcelona, Spain.Archaeological Survey of India
Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model
News outlets are now more than ever incentivized to provide their audience
with slanted news, while the intrinsic homophilic nature of online social media
may exacerbate polarized opinions. Here, we propose a new dynamic latent space
model for time-varying online audience-duplication networks, which exploits
social media content to conduct inference on media bias and polarization of
news outlets. Our model contributes to the literature in several directions: 1)
we provide a model-embedded data-driven interpretation for the latent leaning
of news outlets in terms of media bias; 2) we endow our model with
Markov-switching dynamics to capture polarization regimes while maintaining a
parsimonious specification; 3) we contribute to the literature on the
statistical properties of latent space network models. The proposed model is
applied to a set of data on the online activity of national and local news
outlets from four European countries in the years 2015 and 2016. We find
evidence of a strong positive correlation between our media slant measure and a
well-grounded external source of media bias. In addition, we provide insight
into the polarization regimes across the four countries considered
Relating Group Size and Posting Activity of an Online Community of Financial Investors: Regularities and Seasonal Patterns
Group size can potentially affect collective activity and individual propensity to contribute to collective goods. Mancur Olson, in his Logic of Collective Action, argued that individual contribution to a collective good tends to be lower in groups of large size. Today, online communication platforms represent an interesting ground to study such collaborative dynamics under possibly different conditions (e.g., lower costs related to gather and share information). This paper examines the relationship between group size and activity in an online financial forum, where users invest time in sharing news, analysis and comments with other investors. We looked at about 24 million messages shared in more than ten years in the finanzaonline.com online forum. We found that the relationship between the number of active users and the number of posts shared by those users is of the power type (with exponent α\u3e1) and is subject to periodic fluctuations, mostly driven by hour-of-the-day and day-of-the-week effects. The daily patterns of the exponent showed a divergence between working week and weekend days. In general, the exponent was lower before noon, where investors are typically interested in market news, higher in the late afternoon, where markets are closing and investors need better understanding of the situation. Further research is needed, especially at the micro level, to dissect the mechanisms behind these regularities
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