Time Series Analysis Of GDP and Market Indices
AbstractThe paper is concerned with time series analysis of GDP growth and returns of securities market indices. The main goal was to identify cyclical patterns in the examined series and to demonstrate correlation among the individual series. First part of the paper presents deals with fitting the selected models to real data. Chapter 3 is devoted to correlation analysis. The analysed time series are quarterly GDP growth in the USA, in Germany and in the Czech Republic and monthly and quarterly returns of S&P500, DAX and PX50. All models applied in Chapter 2 are based on autoregressive processes. The analysis starts with linear ARMA model, followed by non-linear extensions thereof: the ARCH family based on conditional heteroscedasticity and the Threshold AutoRegressive Model (TAR). Non-linear models appeared to be more appropriate than simple ARMA models. Traces of cyclical patterns were identified in several time series, however, only US GDP growth and S&P500 seemed to be closely related in terms of cyclical behaviour. The correlation analysis confirmed two frequent hypotheses. First, returns on different capital markets in the world are strongly correlated reducing thus investors' possibilities to diversify risk. Second, the development of the Czech GDP growth and of PX50 responds (with certain delay) to the development of German GDP growth, whereas no correlation can be found between the Czech GDP growth and PX50 returns.
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Bibliographic InfoArticle provided by The Czech Econometric Society in its journal Bulletin of the Czech Econometric Society.
Volume (Year): 10 (2003)
Issue (Month): 18 ()
time series; autoregressive models; correlation; econometrics; financial economics; dependence;
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- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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