Browsing by Author "Caiado, Jorge"
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- Comparison of time series with unequal lengthPublication . Caiado, Jorge; Crato, Nuno; Peña, DanielThe 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 spectral estimates comparable, by producing statistics at the same frequency. A first sensible approach may consist on zero-padding the shorter time series in order to increase the corresponding number of periodogram ordinates. We show that this works well provided the sample sizes are not very different, but does not give good results in case the time series lengths are very unbalanced. For this latter case, we study some periodogram-based comparison methods and construct a test. Both the methods and the test display reasonable properties for series of any lengths. Additionally and for reference, we develop a parametric comparison method. The procedures are assessed by a Monte Carlo simulation study. As an illustrative example, a periodogram method is used to compare and cluster industrial production series of some developed countries.
- A fragmented-periodogram approach for clustering big data time seriesPublication . Caiado, Jorge; Crato, Nuno; Poncela, PilarWe propose and study a new frequency-domain procedure for characterizing and comparing large sets of long time series. Instead of using all the information available from data, which would be computationally very expensive, we propose some regularization rules in order to select and summarize the most relevant information for clustering purposes. Essentially, we suggest to use a fragmented periodogram computed around the driving cyclical components of interest and to compare the various estimates. This procedure is computationally simple, but able to condense relevant information of the time series. A simulation exercise shows that the smoothed fragmented periodogram works in general better than the non-smoothed one and not worse than the complete periodogram for medium to large sample sizes. We illustrate this procedure in a study of the evolution of several stock markets indices. We further show the effect of recent financial crises over these indices behaviour.
- A GARCH-based method for clustering of financial time series: International stock markets evidencePublication . Caiado, Jorge; Crato, NunoIn this paper, we introduce a volatility-based method for clustering analysis of financial 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 different lengths. As an illustrative example, we investigate the similarities among major international stock markets using daily return series with different sample sizes from 1966 to 2006. From cluster analysis, most European markets countries, United States and Canada appear close together, and most Asian/Pacific 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.
- Human capital, social capital and organizational performancePublication . Felício, J. Augusto; Couto, Eduardo; Caiado, JorgePurpose – The aim of this paper is to evaluate the human capital and social capital of managers and the influence of these attributes on the performance of small and medium-sized Portuguese companies. Design/methodology/approach – The structural modeling approach was applied to a sample of 199 small and medium-sized companies aged between 3 and 15 years, from five different sectors of activity. Findings – It was found that human capital affects social capital, and that experience and cognitive ability influence personal relations and complicity. Organizational performance is strongly influenced by human capital through the cognitive ability of the manager. Practical implications – Based on these findings managers can gain a better knowledge about how to improve the performance of their firms, for example through adjustments in communication methods or strategic decision capacities. Originality/value – This work is innovative in the sense that it confirms the influence of human capital on social capital, and shows that it is cognitive ability that affects organizational performance.
- Identifying common dynamic features in stock returnsPublication . Caiado, Jorge; Crato, NunoThis paper proposes volatility and spectral based methods for the cluster analysis of stock returns. Using the information about both the estimated parameters in the threshold GARCH (or TGARCH) equation and the periodogram of the squared returns, 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.
- Identifying common spectral and asymmetric features in stock returnsPublication . Caiado, Jorge; Crato, NunoThis 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 employ these techniques to investigate the similarities and dissimilarities between the "blue-chip" stocks used to compute the Dow Jones Industrial Average (DJIA) index. For reference, we investigate also the similarities among stock returns by mean and squared correlation methods.
- Modelling and forecasting the volatility of the portuguese stock index PSI-20Publication . Caiado, JorgeThe volatility clustering often seen in financial data has increased the interest of researchers in applying good models to measure and forecast stock returns. This paper aims to model the volatility for daily and weekly returns of the Portuguese Stock Index PSI-20. By using simple GARCH, GARCH-M, Exponential GARCH (EGARCH) and Threshold ARCH (TARCH) models, we find support that there are significant asymmetric shocks to volatility in the daily stock returns, but not in the weekly stock returns. We also find that some weekly returns time series properties are substantially different from properties of daily returns, and the persistence in conditional volatility is different for some of the sub-periods referred. Finally, we compare the forecasting performance of the various volatility models in the sample periods before and after the terrorist attack on September 11, 2001.
- On the classification of financial data with domain agnostic featuresPublication . Bastos, João A.; Caiado, JorgeWe compare a data-driven domain agnostic set of canonical features with a smaller collection of features that capture well-known stylized facts about financial asset returns. We show that these facts discriminate better different asset types than general-purpose features. Therefore, financial time series analysis is a domain where well-informed expert knowledge may not be disregarded in favor of agnostic representations of the data.
- A periodogram-based metric for time series classificationPublication . Caiado, Jorge; Crato, Nuno; Peña, DanielThe 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.
- Public and private investments : a VAR analysis of their impact on economic growth in 18 advanced economiesPublication . Afonso, António; Caiado, Jorge; Kanaan, Omar Al.This paper examines the macroeconomic returns on public and private investments in 18 advanced economies from 1965 to 2019, using a Vector Autoregressive (VAR) approach. We assess whether higher investment levels drive economic growth and explore the interplay between public and private investments, particularly regarding crowding-in and crowding-out effects. A sensitivity analysis, altering the order of investments in the VAR model, tests the robustness of the results and highlights the dynamic relationships between them. The findings show that private investment consistently stimulates growth, while public investment’s impact varies by country. The analysis underscores the importance of investment sequencing, suggesting the need for flexible policies and a deeper understanding of investment dynamics. This study contributes to the debate on public investment’s role in fostering growth and offers empirical insights for future economic policy and investment strategies.