In this paper we introduce a new nonparametric test for Granger non-causality which avoids the over-rejection observed in the frequently used test proposed by Hiemstra and Jones [1994. Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance 49, 1639-1664]. After illustrating the problem by showing that rejection probabilities under the null hypothesis may tend to one as the sample size increases, we study the reason behind this phenomenon analytically. It turns out that the Hiemstra-Jones test for the null of Granger non-causality, which can be rephrased in terms of conditional independence of two vectors X and Z given a third vector Y, is sensitive to variations in the conditional distributions of X and Z that may be present under the null. To overcome this problem we replace the global test statistic by an average of local conditional dependence measures. By letting the bandwidth tend to zero at appropriate rates, the variations in the conditional distributions are accounted for automatically. Based on asymptotic theory we formulate practical guidelines for choosing the bandwidth depending on the sample size. We conclude with an application to historical returns and trading volumes of the Standard and Poor's index which indicates that the evidence for volume Grangercausing returns is weaker than suggested by the Hiemstra-Jones test. r
a b s t r a c tWe propose new scoring rules based on conditional and censored likelihood for assessing the predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. These scoring rules can be interpreted in terms of Kullback-Leibler divergence between weighted versions of the density forecast and the true density. Existing scoring rules based on weighted likelihood favor density forecasts with more probability mass in the given region, rendering predictive accuracy tests biased toward such densities. Using our novel likelihood-based scoring rules avoids this problem.
We address a consistency problem in the commonly used nonparametric test for Granger causality developed by Hiemstra and Jones (1994). We show that the relationship tested is not implied by the null hypothesis of Granger non-causality. Monte Carlo simulations using processes satisfying the null hypothesis show that, for a given nominal size, the actual rejection rate may tend to one as the sample size increases. Our results imply that evidence for nonlinear Granger causality reported in the applied empirical literature should be re-interpreted.
This paper connects variance-covariance estimation methods, Gaussian graphical models, and the growing literature on economic and financial networks. We construct the network using the concept of partial correlations which captures direct linear dependence between any two entities, conditional on dependence between all other entities. We relate the centrality measures of this network to shock propagation. The methodology is applied to construct the perceived network of publicly traded Australian banks and their connections to domestic economic sectors and international markets. We find strong links between the big four Australian banks, real estate and other sectors of the economy, and determine which entities play a central role in transmitting and absorbing the shocks. * We benefited from helpful comments by Shu-Heng Chen, Mardi Dungey, Hakan Eratalay and Matt Jackson, two anonymous referees, as well as by participants of the 1st Conference on Recent Developments in Financial Econometrics and Applications, Deakin University; the Summer Workshop at the Centre for Mathematical Social Sciences,
a b s t r a c tWe investigate the extent to which emerging stock market integration affects the joint behavior of stock and bond returns using a two-stage semi-parametric approach. Using a sample of 18 emerging markets, we find an unambiguous and robust link between emerging stock market integration and stock-bond return decoupling. We explain this with a decline in the segmentation risk premia in equities modeled by De Jong and De Roon [De Jong, F., De Roon, F.A., 2005. Time-varying market integration and expected returns in emerging markets. Journal of Financial Economics 78, 583-613] that leads to increased demand for stocks and reduced or unchanged demand for bonds. Our findings deliver new insights into the financial liberalization and stock-bond comovement literatures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.