1,721,032 research outputs found
Stylized facts of financial time series and hidden semi-Markov models
No full textHidden Markov models reproduce most of the stylized facts about daily series of returns. A notable exception is the inability of the models to reproduce one ubiquitous feature of such time series, namely the slow decay in the autocorrelation function of the squared returns. It is shown that this stylized fact can be described much better by means of hidden semi-Markov models. This is illustrated by examining the fit of two such models to 18 series of daily sector returns. (c) 2006 Elsevier B.V. All rights reserved
Computational issues in parameter estimation for stationary hidden Markov models
No full textThe parameters of a hidden Markov model (HMM) can be estimated by numerical maximization of the log-likelihood function or, more popularly, using the expectation-maximization (EM) algorithm. In its standard implementation the latter is unsuitable for fitting stationary hidden Markov models (HMMs). We show how it can be modified to achieve this. We propose a hybrid algorithm that is designed to combine the advantageous features of the two algorithms and compare the performance of the three algorithms using simulated data from a designed experiment, and a real data set. The properties investigated are speed of convergence, stability, dependence on initial values, different parameterizations. We also describe the results of an experiment to assess the true coverage probability of bootstrap-based confidence intervals for the parameters
Time-varying beta risk of Pan-European industry portfolios: A comparison of alternative modeling techniques
No full textThis paper investigates the time-varying behavior of systematic risk for 18 pan-European sectors. Using weekly data over the period 1987-2005, six different modeling techniques in addition to the standard constant coefficient model are employed: a bivariate t-GARCH(1,1) model, two Kalman filter (KF)-based approaches, a bivariate stochastic volatility model estimated via the efficient Monte Carlo likelihood technique as well as two Markov switching models. A comparison of ex-ante forecast performances of the different models indicate that the random walk process in connection with the KF is the preferred model to describe and forecast the time-varying behavior of sector betas in a European context
Estimation of the stationary distribution of a semi-Markov chain
No full textThis article is concerned with the estimation of the stationary distribution of a discretetime semi-Markov process. After briefly presenting the discrete-time semi-Markov setting, wepropose an estimator of the associated stationary distribution. The main results concern theasymptotic properties of this estimator, as the sample size becomes large. A numerical exampleillustrates the asymptotic properties of the estimators
hsmm - An R package for analyzing hidden semi-Markov models
No full textHidden semi-Markov models are a generalization of the well-known hidden Markov model. They allow for a greater flexibility of sojourn time distributions, which implicitly follow a geometric distribution in the case of a hidden Markov chain. The aim of this paper is to describe hsmm, a new software package for the statistical computing environment R. This package allows for the simulation and maximum likelihood estimation of hidden semi-Markov models. The implemented Expectation Maximization algorithm assumes that the time spent in the last visited state is subject to right-censoring. it is therefore not subject to the common limitation that the last visited state terminates at the last observation. Additionally, hsmm permits the user to make inferences about the underlying state sequence via the Viterbi algorithm and smoothing probabilities. (C) 2008 Elsevier B.V. All rights reserved.German Research Foundation (DFG
Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer
No full textRetailers are experiencing a systematic shift in the buying habits of their customers as more customers buy across different channels. Marketing managers face the daunting task of embracing online and offline channels to engage consumers, influence choice, and create habits to sustain a competitive advantage. We develop a dynamic segmentation model of channel choice and purchase frequency to assess the responsiveness of segments to catalogues and email communications. In addition, we perform profitability analysis to offer insights on the profitability of using catalogues and emails to reach customers. For certain firms, especially those with a history of using catalogue mailings, the findings suggest that catalogues remain relevant and are an effective tool at influencing purchases across both online and offline channels despite the increasing trend toward digital marketing. In addition, we found a segment of digital consumers respond favorably to both emails and catalogues. We argue catalogues have retained their competitive advantage over email marketing communication because the catalogue may not compete for attention with consumers' other digital distractions. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved
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
DYNAMIC MIXTURES OF FACTOR ANALYZERS TO CHARACTERIZE MULTIVARIATE AIR POLLUTANT EXPOSURES
Full text linkThe assessment of pollution exposure is based on the analysis
of multivariate time series that include the concentrations of several
pollutants as well as the measurements of multiple atmospheric variables.
It typically requires methods of dimensionality reduction that
are capable to identify potentially dangerous combinations of pollutants
and, simultaneously, to segment exposure periods according
to air quality conditions. When the data are high-dimensional, however,
efficient methods of dimensionality reduction are challenging
because of the formidable structure of cross-correlations that arise
from the dynamic interaction between weather conditions and natural/anthropogenic
pollution sources. In order to assess pollution exposure
in an urban area while taking the above mentioned difficulties
into account, we develop a class of parsimonious hidden Markov
models. In a multivariate time-series setting, this approach allows to
simultaneously perform temporal segmentation and dimensionality
reduction. We specifically approximate the distribution of multiple
pollutant concentrations by mixtures of factor analysis models, whose
parameters evolve according to a latent Markov chain. Covariates are
included as predictors of the chain transition probabilities. Parameter
constraints on the factorial component of the model are exploited
to tune the flexibility of dimensionality reduction. In order to estimate
the model parameters efficiently, we propose a novel three-step
Alternating Expected Conditional Maximization (AECM) algorithm,
which is also assessed in a simulation study. In the case study, the
proposed methods were capable (1) to describe the exposure to pollution
in terms of a few latent regimes, (2) to associate these regimes
with specific combinations of pollutant concentration levels as well
as distinct correlation structures between concentrations, and (3) to
capture the influence of weather conditions on transitions between
regime
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