Jorge Caiado

Learn 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(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(More)
In this article, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Holt-Winters, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. A within-week seasonal cycle and a within-year seasonal cycle are accommodated in the various model speci…cations to capture both seasonalities. We(More)
This paper uses structural equation modeling to examine the linkages between financial performance, sporting performance and stock market performance for English football clubs over the period from 1995 to 2007. The results indicate that there is a strong correlation between financial and sporting latent constructs. Additionally, the study indicates that(More)