Jorge Caiado - Profile on Academia.edu (original) (raw)

Jorge Caiado, after receiving his Ph.D. in Applied Mathematics to Economics and Management from the Technical University of Lisbon, joined the School of Economics and Management at Technical University of Lisbon where he is currently Professor of Econometrics and Time Series Analysis. From 1995 to 2003 he was Lecture at the Portuguese Bank Training Institute. During 2004-2005, he was visiting researcher of the Department of Statistics at University Carlos III of Madrid (Spain). He is now Vice-President of the Centre for Applied Mathematics and Economics (CEMAPRE). His research in econometrics, finance, time series analysis, forecasting methods and statistical software (EViews, Stata, and Lingo) has led to numerous publications in journals such as Quantitative Finance, Journal of Retailing and Consumer Services, Computational Statistics & Data Analysis, Management Decision, Journal of Business Economics and Management, Journal of Statistical Computation and Simulation, Communications in Statistics: Simulation and Computation, Physica A: Statistical Mechanics and its Applications, Journal of Hydrologic Engineering, Portuguese Journal of Management Studies, and others. He is also author and co-author of several books and chapters in books such as “Classification and Clustering of Time Series” (Lambert Academic Publishing, 2010), “Computational Statistics” (Physica-Verlag, 2008), “Recent Advances in Stochastic Modeling and Data Analysis” (World Scientific Publishing, 2007), "Handbook of Cluster Analysis (Taylor & Francis , 2015), "Métodos de Previsão em Gestão com aplicações em excel" (Edições Sílabo, 2nd Ed., 2016), "Gestão de Instituições Financeiras" (Edições Sílabo, 2nd Ed., 2008). He is serving as econometric and statistical consultant and trainer for numerous companies and organizations including central banks, commercial and investment banks, bureau of statistics, bureau of economic analysis, transportation and logistic companies, health companies and insurance companies.

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Papers by Jorge Caiado

The statistical discrimination and clustering literature has studied the problem of identifying s... more The statistical discrimination and clustering literature has studied the problem of identifying similarities in time series data. Some studies use non-parametric approaches for splitting a set of time series into clusters by looking at their Euclidean distances in the space of points. A new measure of distance between time series based on the normalized periodogram is proposed. Simulation results comparing this measure with others parametric and non-parametric metrics are provided. In particular , the classification of time series as stationary or as non-stationary is discussed. The use of both hierarchical and non-hierarchical clustering algorithms is considered. An illustrative example with economic time series data is also presented.

This paper deals with hypothesis testing for independent time series with unequal length. It prop... more This paper deals with hypothesis testing for independent time series with unequal length. It proposes a spectral test based on the distance between the periodogram ordinates and a parametric test based on the distance between the parameter estimates of fitted autoregressive moving average models. Both tests are compared with a likelihood ratio test based on the pooled spectra. In all cases, the null hypothesis is that the two series under consideration are generated by the same stochastic process. The performance of the three tests is investigated by a Monte Carlo simulation study.

This study investigates the presence of deterministic dependencies in international stock markets... more This study investigates the presence of deterministic dependencies in international stock markets using recurrence plots and recurrence quantification analysis (RQA). The results are based on a large set of free float-adjusted market capitalization stock indices, covering a period of 15 years. The statistical tests suggest that the dynamics of stock prices in emerging markets is characterized by higher values of

This study introduces a new distance measure for clustering financial time series based on varian... more This study introduces a new distance measure for clustering financial time series based on variance ratio test statistics. The proposed metric attempts to assess the level of interdependence of time series from the point of view of return predictability. An empirical application of this approach to international stock market returns is presented. The results suggest that this metric discriminates reasonably

This study explores the interconnection between human factors and social factors and analyses the... more This study explores the interconnection between human factors and social factors and analyses the relations influenced by the specific activity and age of firms. A statistical approach is implemented which applies factor analysis techniques, based on a sample of small and medium sized firms from four sectors of activity which are between four and fifteen years old, and are split

Determinants of innovation in a small open economy: a multidimensional perspective

Journal of Business Economics and Management, Jun 1, 2013

ABSTRACT This paper uses logistic regression analysis to examine how intramural and extramural R&... more ABSTRACT This paper uses logistic regression analysis to examine how intramural and extramural R&D, acquisition of machinery, equipment and software, acquisition of external knowledge, training, market introduction and other procedures and technical preparations determine the innovation behaviour of manufacturing and service firms. We adopt a multidimensional view of innovation by considering product, process, organizational and marketing innovations as dependent variables separately. The study reports on the Community Innovation Survey (CIS4) of a small open-economy country. The empirical results indicate that intramural R&D has a positive impact on innovation. In contrast, the influence of extramural R&D on innovation is unclear. All innovation activities contribute towards organizational innovation. The study also suggests that there are no significant differences between services and manufacturing firms concerning the propensity to innovation.

Several authors have been investigating which factors influence academic performance in undergrad... more Several authors have been investigating which factors influence academic performance in undergraduate business and accounting courses. However, most of these studies are not conclusive and some results are contradictory. This study aims to determine which demographic (age, sex, professional status, place of residence) and academic (entrance score, accounting score, math score) factors are relevant in the accounting course final score in a school near the coast when compared with those of a school in the interior of Portugal. Through the methodology of the linear correlation analysis and stepwise multiple regression, we found that only the variables associated, excluding age, with the academic performance were significant to explain the performance in the undergraduate accounting course, and that the magnitude of their effects did not quite differ with the geographic location of the school.

This study explores the interconnection between human factors and social factors and analyses the... more This study explores the interconnection between human factors and social factors and analyses the relations influenced by the specific activity and age of firms. A statistical approach is implemented which applies factor analysis techniques, based on a sample of small and medium sized firms from four sectors of activity which are between four and fifteen years old, and are split into three time periods. It is found that there are interconnected groups of human capital and social capital factors, although a sizeable proportion of the literature conceptually separates these factors and deals with them individually. It is also ascertained that this relationship is influenced by the field of activity and the age of the firms.

The behavior of international stock market returns in terms of rate of return, unconditional vola... more The behavior of international stock market returns in terms of rate of return, unconditional volatility, skewness, excess kurtosis, serial dependence and long-memory is examined. A factor analysis approach is employed to identify the underlying dimensions of stock market returns. In our approach, the factors are estimated not from the observed historical returns but from their empirical properties, without imposing any restriction about the time dependence of the observations. To identify clusters of markets and multivariate outliers, factor analysis is then used to generate factor scores. The findings suggest the existence of meaningful factors which determine the differences in terms of the dependence structure between developed and emerging market returns.

In this paper, we examine the daily water demand forecasting performance of double seasonal univa... more In this paper, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Exponential Smoothing, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. We investigate whether combining forecasts from different methods and from different origins and horizons could improve forecast accuracy. We use daily data for water consumption in Spain from 1 January 2001 to 30 June 2006.

A GARCH-based method for clustering of …nancial time series: International stock markets evidence

In this paper, we introduce a volatility-based method for clustering analysis of …nancial time se... more In this paper, we introduce a volatility-based method for clustering analysis of …nancial time series. Using the generalized autoregressive con- ditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior of the time series and solves the problem of dier- ent lengths. As an illustrative example, we investigate the similarities

Most of economic and financial time series have a nonstationary behavior. There are different typ... more Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes. In this paper, we compare random walk and determinist trend processes using sample autocorrelation, sample partial autocorrelation and

This paper proposes spectral and asymmetric-volatility based methods for cluster analysis of stoc... more This paper proposes spectral and asymmetric-volatility based methods for cluster analysis of stock returns. Using the information about both the periodogram of the squared returns and the estimated parameters in the TARCH equation, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We

Previous studies have investigated the comovements of international equity returns by using mean ... more Previous studies have investigated the comovements of international equity returns by using mean correlations, cointegration, common factor analysis, and other approaches. This paper investigates the evolution of the affinity among major euro and non-euro area stock markets in the period 1966-2006 by using distance-based methods for clustering analysis of time series. A periodogram-based metric for mean and squared returns is

In statistical data analysis it is often important to compare, classify, and cluster different ti... more In statistical data analysis it is often important to compare, classify, and cluster different time series. For these purposes various methods have been proposed in the literature, but they usually assume time series with the same sample size. In this paper, we propose a spectral domain method for handling time series of unequal length. The method make the spectral estimates

We propose a periodogram-based metric for classification and clustering of time series with diffe... more We propose a periodogram-based metric for classification and clustering of time series with different sample sizes. For such cases, we know that the Euclidean distance between the periodogram ordinates cannot be used. One possible way to deal with this problem is to interpolate lineary one of the periodograms in order to estimate ordinates of the same frequencies.

The comparison and classification of time series is an important issue in practical time series a... more The comparison and classification of time series is an important issue in practical time series analysis. For these purposes, various methods have been proposed in the literature, but all have shortcomings, especially when the observed time series have different sample sizes. In this paper, we propose spectral domain methods for handling time series of unequal length. The methods make the

Journal of Retailing and Consumer Services, 2016

This research studies the role of private labels (PLs) on consumers' store loyalty. It offers an ... more This research studies the role of private labels (PLs) on consumers' store loyalty. It offers an integrative approach that comprises several store loyalty drivers (in-store and economic factors), analyzing the role that PLs play among different types of retailers. Data were collected through an online survey. Using structural equation modeling, we run our analysis across different retail formats, assessing which factors lead to store loyalty and to what extent PLs contribute to it. Findings suggest that depending on retailers' market positioning, different factors contribute to loyalty and that the impact of PLs is mostly significant for medium-cost and premium supermarkets.

Do Private Labels Lead to Store Loyalty? An Integrated Framework of Analysis Using a Structural Equation Modeling Approach

Springer Proceedings in Business and Economics, 2015

Recent Advances in Stochastic Modeling and Data Analysis, 2007

In this paper, we introduce a volatility-based method for clustering analysis of …nancial time se... more In this paper, we introduce a volatility-based method for clustering analysis of …nancial time series. Using the generalized autoregressive conditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior of the time series and solves the problem of di¤erent lengths. As an illustrative example, we investigate the similarities among major international stock markets using daily return series with di¤erent sample sizes from 1966 to 2006. From cluster analysis, most European markets countries, United States and Canada appear close together, and most Asian/Paci…c markets and the South/Middle American markets appear in a distinct cluster. After the terrorist attack on September 11, 2001, the European stock markets have become more homogenous, and North American markets, Japan and Australia seem to come closer.

The statistical discrimination and clustering literature has studied the problem of identifying s... more The statistical discrimination and clustering literature has studied the problem of identifying similarities in time series data. Some studies use non-parametric approaches for splitting a set of time series into clusters by looking at their Euclidean distances in the space of points. A new measure of distance between time series based on the normalized periodogram is proposed. Simulation results comparing this measure with others parametric and non-parametric metrics are provided. In particular , the classification of time series as stationary or as non-stationary is discussed. The use of both hierarchical and non-hierarchical clustering algorithms is considered. An illustrative example with economic time series data is also presented.

This paper deals with hypothesis testing for independent time series with unequal length. It prop... more This paper deals with hypothesis testing for independent time series with unequal length. It proposes a spectral test based on the distance between the periodogram ordinates and a parametric test based on the distance between the parameter estimates of fitted autoregressive moving average models. Both tests are compared with a likelihood ratio test based on the pooled spectra. In all cases, the null hypothesis is that the two series under consideration are generated by the same stochastic process. The performance of the three tests is investigated by a Monte Carlo simulation study.

This study investigates the presence of deterministic dependencies in international stock markets... more This study investigates the presence of deterministic dependencies in international stock markets using recurrence plots and recurrence quantification analysis (RQA). The results are based on a large set of free float-adjusted market capitalization stock indices, covering a period of 15 years. The statistical tests suggest that the dynamics of stock prices in emerging markets is characterized by higher values of

This study introduces a new distance measure for clustering financial time series based on varian... more This study introduces a new distance measure for clustering financial time series based on variance ratio test statistics. The proposed metric attempts to assess the level of interdependence of time series from the point of view of return predictability. An empirical application of this approach to international stock market returns is presented. The results suggest that this metric discriminates reasonably

This study explores the interconnection between human factors and social factors and analyses the... more This study explores the interconnection between human factors and social factors and analyses the relations influenced by the specific activity and age of firms. A statistical approach is implemented which applies factor analysis techniques, based on a sample of small and medium sized firms from four sectors of activity which are between four and fifteen years old, and are split

Determinants of innovation in a small open economy: a multidimensional perspective

Journal of Business Economics and Management, Jun 1, 2013

ABSTRACT This paper uses logistic regression analysis to examine how intramural and extramural R&... more ABSTRACT This paper uses logistic regression analysis to examine how intramural and extramural R&D, acquisition of machinery, equipment and software, acquisition of external knowledge, training, market introduction and other procedures and technical preparations determine the innovation behaviour of manufacturing and service firms. We adopt a multidimensional view of innovation by considering product, process, organizational and marketing innovations as dependent variables separately. The study reports on the Community Innovation Survey (CIS4) of a small open-economy country. The empirical results indicate that intramural R&D has a positive impact on innovation. In contrast, the influence of extramural R&D on innovation is unclear. All innovation activities contribute towards organizational innovation. The study also suggests that there are no significant differences between services and manufacturing firms concerning the propensity to innovation.

Several authors have been investigating which factors influence academic performance in undergrad... more Several authors have been investigating which factors influence academic performance in undergraduate business and accounting courses. However, most of these studies are not conclusive and some results are contradictory. This study aims to determine which demographic (age, sex, professional status, place of residence) and academic (entrance score, accounting score, math score) factors are relevant in the accounting course final score in a school near the coast when compared with those of a school in the interior of Portugal. Through the methodology of the linear correlation analysis and stepwise multiple regression, we found that only the variables associated, excluding age, with the academic performance were significant to explain the performance in the undergraduate accounting course, and that the magnitude of their effects did not quite differ with the geographic location of the school.

This study explores the interconnection between human factors and social factors and analyses the... more This study explores the interconnection between human factors and social factors and analyses the relations influenced by the specific activity and age of firms. A statistical approach is implemented which applies factor analysis techniques, based on a sample of small and medium sized firms from four sectors of activity which are between four and fifteen years old, and are split into three time periods. It is found that there are interconnected groups of human capital and social capital factors, although a sizeable proportion of the literature conceptually separates these factors and deals with them individually. It is also ascertained that this relationship is influenced by the field of activity and the age of the firms.

The behavior of international stock market returns in terms of rate of return, unconditional vola... more The behavior of international stock market returns in terms of rate of return, unconditional volatility, skewness, excess kurtosis, serial dependence and long-memory is examined. A factor analysis approach is employed to identify the underlying dimensions of stock market returns. In our approach, the factors are estimated not from the observed historical returns but from their empirical properties, without imposing any restriction about the time dependence of the observations. To identify clusters of markets and multivariate outliers, factor analysis is then used to generate factor scores. The findings suggest the existence of meaningful factors which determine the differences in terms of the dependence structure between developed and emerging market returns.

In this paper, we examine the daily water demand forecasting performance of double seasonal univa... more In this paper, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Exponential Smoothing, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. We investigate whether combining forecasts from different methods and from different origins and horizons could improve forecast accuracy. We use daily data for water consumption in Spain from 1 January 2001 to 30 June 2006.

A GARCH-based method for clustering of …nancial time series: International stock markets evidence

In this paper, we introduce a volatility-based method for clustering analysis of …nancial time se... more In this paper, we introduce a volatility-based method for clustering analysis of …nancial time series. Using the generalized autoregressive con- ditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior of the time series and solves the problem of dier- ent lengths. As an illustrative example, we investigate the similarities

Most of economic and financial time series have a nonstationary behavior. There are different typ... more Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes. In this paper, we compare random walk and determinist trend processes using sample autocorrelation, sample partial autocorrelation and

This paper proposes spectral and asymmetric-volatility based methods for cluster analysis of stoc... more This paper proposes spectral and asymmetric-volatility based methods for cluster analysis of stock returns. Using the information about both the periodogram of the squared returns and the estimated parameters in the TARCH equation, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We

Previous studies have investigated the comovements of international equity returns by using mean ... more Previous studies have investigated the comovements of international equity returns by using mean correlations, cointegration, common factor analysis, and other approaches. This paper investigates the evolution of the affinity among major euro and non-euro area stock markets in the period 1966-2006 by using distance-based methods for clustering analysis of time series. A periodogram-based metric for mean and squared returns is

In statistical data analysis it is often important to compare, classify, and cluster different ti... more In statistical data analysis it is often important to compare, classify, and cluster different time series. For these purposes various methods have been proposed in the literature, but they usually assume time series with the same sample size. In this paper, we propose a spectral domain method for handling time series of unequal length. The method make the spectral estimates

We propose a periodogram-based metric for classification and clustering of time series with diffe... more We propose a periodogram-based metric for classification and clustering of time series with different sample sizes. For such cases, we know that the Euclidean distance between the periodogram ordinates cannot be used. One possible way to deal with this problem is to interpolate lineary one of the periodograms in order to estimate ordinates of the same frequencies.

The comparison and classification of time series is an important issue in practical time series a... more The comparison and classification of time series is an important issue in practical time series analysis. For these purposes, various methods have been proposed in the literature, but all have shortcomings, especially when the observed time series have different sample sizes. In this paper, we propose spectral domain methods for handling time series of unequal length. The methods make the

Journal of Retailing and Consumer Services, 2016

This research studies the role of private labels (PLs) on consumers' store loyalty. It offers an ... more This research studies the role of private labels (PLs) on consumers' store loyalty. It offers an integrative approach that comprises several store loyalty drivers (in-store and economic factors), analyzing the role that PLs play among different types of retailers. Data were collected through an online survey. Using structural equation modeling, we run our analysis across different retail formats, assessing which factors lead to store loyalty and to what extent PLs contribute to it. Findings suggest that depending on retailers' market positioning, different factors contribute to loyalty and that the impact of PLs is mostly significant for medium-cost and premium supermarkets.

Do Private Labels Lead to Store Loyalty? An Integrated Framework of Analysis Using a Structural Equation Modeling Approach

Springer Proceedings in Business and Economics, 2015

Recent Advances in Stochastic Modeling and Data Analysis, 2007

In this paper, we introduce a volatility-based method for clustering analysis of …nancial time se... more In this paper, we introduce a volatility-based method for clustering analysis of …nancial time series. Using the generalized autoregressive conditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior of the time series and solves the problem of di¤erent lengths. As an illustrative example, we investigate the similarities among major international stock markets using daily return series with di¤erent sample sizes from 1966 to 2006. From cluster analysis, most European markets countries, United States and Canada appear close together, and most Asian/Paci…c markets and the South/Middle American markets appear in a distinct cluster. After the terrorist attack on September 11, 2001, the European stock markets have become more homogenous, and North American markets, Japan and Australia seem to come closer.

This paper proposes an asymmetric-volatility based method for cluster analysis of stock returns. ... more This paper proposes an asymmetric-volatility based method for cluster analysis of stock returns. Using the information about the estimated parameters in the TARCH equation, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the similarities and dissimilarities between the "blue-chip" stocks used to compute the Dow Jones Industrial Average (DJIA) index.