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When a pair of independent series is highly persistent, there is a spurious regression bias in a regression between these series, closely related to the classic studies of Granger and Newbold (1974). Although this is well known to occur with independent I(1) processes, this paper provides theoretical and numerical evidence that the phenomenon of spurious regression also arises in regressions between stationary AR(p) processes with structural breaks, which occur at different points in time, in the means and the trends. The intuition behind this is that structural breaks can increase the persistence levels in the processes (e.g., Granger and Hyung (2004)), which then leads to spurious regressions. These phenomena occur for general distributions and serial dependence of the innovation terms.

A new test for hysteresis based on a nonlinear unobserved components model is proposed. Observed unemployment rates are decomposed into a natural rate component and a cyclical component. Threshold type nonlinearites are introduced by allowing past cyclical unemployment to have a different impact on the natural rate depending on the regime of the economy. The impact of lagged cyclical shocks on the current natural component is the measure of hysteresis. To derive an appropriate p-value for a test for hysteresis two alternative bootstrap algorithms are proposed: the first is valid under homoskedastic errors and the second allows for heteroskedasticity of unknown form. A Monte Carlo simulation study shows the good performance of both bootstrap algorithms. The bootstrap testing procedure is applied to data from Italy, France and the United States. We find evidence of hysteresis for all countries under study.

This study investigates the relationship between the return on a stock index and its volatility using high frequency data. Two well-known hypotheses are reexamined: the leverage effect and the volatility feedback effect hypotheses. An analysis of the five-minute data of the S&P500 index reveals two distinct characteristics of the high frequency data. First, the sign of the relationship between the smallest wavelet scale components for return and volatility appears to be different from those between other scale components. Second, it was found that long memory exists in the daily realized volatility. These characteristics are incorporated in the test for the return-volatility relationship of the S&P500 index.In the study of the impact of changes in volatility on returns, we find that the leverage effect does not appear in intraday data, in contrast to the results for daily data. The difference can be attributed to the different relationships between scale components. By applying wavelet multiresolution analysis, it becomes clear that the leverage effect holds true between return and volatility components with scales equal to or larger than twenty minutes. However, these relationships are obscured in a five-minute data analysis because the smallest scale component accounts for a dominant portion of the variation of return. In testing the volatility feedback hypothesis, a modified model was used to incorporate apparent long memory in the daily realized volatility. This makes both sides of the test model balanced in integration order. No evidence of a volatility feedback effect was found under these stipulations.The results of this study reinforce the horizon dependency of the relationships. Hence, investors should be mindful of the different risk-return relationships for each horizon of interest. Additionally, the results show that the introduction of the long memory property to the proposed model is critical in the testing of risk-return relationships.

Over the last two decades, the degree of international financial integration has increased substantially, becoming an important area of research for many financial economists. This paper explores the determinants of the asymmetries in the international integration of banking systems. We consider an approach based on both network analysis and the concept of geographic neutrality. Our analysis focuses on the banking systems of 18 advanced economies from 1999 to 2005. Results indicate that banking integration should be assessed from the perspective of both inflows and outflows, given that they show different patterns for different countries. The parametric techniques point out the remarkable role of both geographic distance and trade integration. However, the most relevant results are yielded by nonparametric techniques, which we use due to the lack of well-established results on the determinants of trade in assets. These techniques reveal that the effect of the covariates on banking integration is not constant over the conditional distribution which (in practical terms) implies that the sign of the relationship varies across countries.

In this paper, we develop a modified version of the procedure proposed in Aparicio et al. (2008). There, we presented three tests in order to verify the existence of general dependence in time series, within the framework of the Recurrence Quantification Analysis (RQA). Initially, this procedure could be applied both to the original series, and the series transformed by means of the Time Delay Method. However, in the latter case (i.e. if the Time Delay Method is used) recent evidence has shown that spurious structures appear, strongly distorting the results of the indicators of the RQA and, consequently, the conclusions of our tests. Here, we propose a modification of the procedure, which solves this problem, and we apply it successfully to a wide variety of series, simulated and real.

In this paper, we test the martingale property of a set of U.S. exchange rates already analyzed in a recent paper by Yilmaz [J. of Buss. and Ec. Stat., 2003]. We claim that the tests used by Yilmaz are not the most convenient to test the martingale hypothesis (or the equivalent martingale difference of the returns); hence, we compute a recently proposed test by Kuan and Lee [Stud. in Nonlin. Dyn. and Econ., 2004] and compare our results to Yilmaz's. Striking differences arise, which can give a clue about the type of data generating process governing the evolution of exchange rates in each sub-period.