Stock Market Integration: Granger Causality Testing with Respect to Nonsynchronous Trading Effects

In this paper, we perform Granger causality analysis on stock market indices from several Asian, European, and U.S. markets. Using daily data, we point out the potential problems caused by the presence of nonsynchronous trading effects. We deal with two kinds of nonsynchronicity – one induced by differing numbers of observations in the series being analyzed and the other related to the different time zones in which the markets operate. To address the first problem, we propose a data-matching process. To address the second problem, we modify the regressions used in the Granger causality testing. When comparing the empirical results obtained using the standard technique and our modified methodology, we find substantially different results. Most of the relationships that are subject to nonsynchronous trading are not significant in the general case. However, when we use the adjusted methodology, the null hypothesis of a Granger non-causal relationship is rejected in all cases.stock market integration, nonsynchronous trading, Granger causality

Asymmetric GARCH and the financial crisis: a preliminary study

The paper deals with estimation of both general GARCH as well as asymmetric EGARCH and TGARCH models, used to model the leverage effect of good news and bad news on market volatility. We estimate the models using daily returns of S&P 500 stock index and describe the news impact curves (NICs) for these models. When estimating the crisis series, we show the possibility of using a news impact surface to describe the results from models of higher orders.volatility modeling, financial crisis, asymmetric GARCH class models, news impact curve

On the relationship of persistence and number of breaks in volatility: new evidence for three CEE countries

In this article, we contribute to the discussion of volatility persistence in the presence of sudden changes. We follow previous research, particularly Wang and Moore (2009), who analysed stock market returns in five Central and Eastern European countries using the Iterated Cumulative Sum of Squares (ICSS) algorithm for detecting multiple breaks and the test (IT) proposed by Inclán and Tiao (1994). We complement this analysis by using the κ1 and κ2 statistic introduced by Sansó et al. (2004), which lead us to the hypothesis that the estimated persistence in volatility depends inversely on the number of breakpoints in volatility. We explored this claim through a simulation study, where by randomizing an increasing number of breakpoints over the sample, we estimated kernel density of the persistence measure. The results confirmed the relationship between persistence and the number of breakpoints. It also showed that the use of break detection algorithms leads to lower persistence estimates, even within the class of models with an equal number of breaks. Therefore, the overall decrease in persistence can be attributed both to the number of breaks and their position, as suggested by the chosen break detection tests.volatility persistence, GARCH model, ICSS procedure, CEE stock markets

Industry Concentration Dynamics and Structural Changes: The Case of Aerospace & Defence

In this paper we present a general approach and methodology for modelling concentration dynamics on industrial level. The majority of research in this field has usually been focused on estimating adjustment models, where the speed of adjustment of actual level of concentration to the long-run concentration was considered to be responsible for concentration dynamics. The long-run concentration is usually modelled implicitly by the means of often complex industry characteristic variables. We model the changes in concentration through a) long-term structural changes in the specific industry, b) short-term structural changes, stemming from individual company conduct, and c) changes in number of companies constituting the industry. On the sample of quarterly data from 1999 to 2009 using total assets for the companies in Aerospace & Defence Industry in the U.S. we have confirmed the existence of short-term, but lacked evidence for the long-term structural changes.Production, Pricing, Market Structure; Size Distribution of Firms

Volatility Regimes in Macroeconomic Time Series: The Case of the Visegrad Group

The authors analyze several monthly and quarterly macroeconomic time series for the Czech Republic, Poland, Hungary, and Slovakia. These countries embarked on an economic transition in the early 1990s which ultimately led to their membership in the European Union, with Slovakia joining the euro area in 2009. It is natural to assume that changes of such a magnitude should also influence the major macroeconomic indicators. The authors explore the characteristics of these series by endogenously identifying their volatility regimes. In the course of their analysis, they show the difficulties in the handling of unit roots as a necessary step preceding volatility modeling. The final set of breaks identified shows very few changes near the beginning of the series, which corresponds to the transition period.macroeconomic fluctuations, economic transition, structural breaks, volatility regimes, cumulative sum of squares, unit root testing

Asymmetric GARCH and the financial crisis: a preliminary study

The paper deals with estimation of both general GARCH as well as asymmetric EGARCH and TGARCH models, used to model the leverage effect of good news and bad news on market volatility. We estimate the models using daily returns of S&P 500 stock index and describe the news impact curves (NICs) for these models. When estimating the crisis series, we show the possibility of using a news impact surface to describe the results from models of higher orders.volatility modeling, financial crisis, asymmetric GARCH class models, news impact curve

Are we able to capture the EU debt crisis? Evidence from PIIGGS countries in panel unit root framework

We assess the issue of fiscal sustainability in the selected EU countries. Our sample includes those showing the highest government debts, which are nowadays known under the somewhat degrading acronym – PIIGGS (Portugal, Ireland, Italy, Greece, Great Britain and Spain). Assuming the so-called present value borrowing constraint, stationarity of debts presents a sufficient condition for fiscal sustainability. Utilizing various standard panel unit root tests and the test by Im et al. (2010), we examine this condition on quarterly debt-to-GDP ratios over the period 2000 to 2010. Results provide evidence, that when trend breaks in the series are incorporated, not all of these countries exhibit non-stationarity behavior of their debt-to-GDP ratios.Fiscal sustainability, Government debt, Panel unit-root tests

Unit-root and stationarity testing with empirical application on industrial production of CEE-4 countries

The purpose of this paper is to explain both the need and the procedures of unit-root testing to a wider audience. The topic of stationarity testing in general and unit root testing in particular is one that covers a vast amount of research. We have been discussing the problem in four different settings. First we investigate the nature of the problem that motivated the study of unit-root processes. Second we present a short list of several traditional as well as more recent univariate and panel data tests. Third we give a brief overview of the economic theories, in which the testing of the underlying research hypothesis can be expressed in a form of a unit-root / stationary test like the issues of purchasing power parity, economic bubbles, industry dynamic, economic convergence and unemployment hysteresis can be formulated in a form equivalent to the testing of a unit root within a particular series. The last, fourth aspect is dedicated to an empirical application of testing for the non-stationarity in industrial production of CEE-4 countries using a simulation based unit-root testing methodology.Unit-root, Stationarity, Univariate tests, Panel tests, Simulation based unit root tests, Industrial production

Country effects in CEE3 stock market networks: a preliminary study

The stock markets in the Czech Republic, Poland and Hungary (CEE3) are studied in the context of stock market networks. A total of 17 shares are followed during the period of 1998 – 2012. The daily returns are used for calculation of rolling correlations of various window lengths. The resulting correlation matrices are then used to construct network models. Minimum spanning trees (MST) are used as a form of abstraction in the graph structure, and their evolution is studied over time. The main objective of the paper is to test whether the individual assets cluster in the MSTs by the country to which they belong or whether the origin is of lesser importance, leading to cross-country links within the MSTs. The latter might hint at increasing integration within CEE3 stock markets. We find that at the beginning of the series, the MSTs exhibited very strong country clustering, which changed in the later 2000s. The country effects do not seem to be synchronized between all markets