University of Notre Dame

About: University of Notre Dame is a education organization based out in Notre Dame, Indiana, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 22238 authors who have published 55201 publications receiving 2032925 citations. The organization is also known as: University of Notre Dame du Lac & University of Notre Dame, South Bend.

Energy Shocks and Financial Markets

Abstract: This study analyzes the information transmission mechanism linking oil futures with stock prices, where we examine the lead and lag cross-correlations of returns in one market with the others We investigate the dynamic interactions between oil futures prices traded on the New York Mercantile Exchange (NYMEX) and US stock prices, which allows us to examine the effects of energy shocks on financial markets In particular, we examine the extent to which these markets are contemporaneously correlated, with particular attention paid to the association of oil price indexes with the SP 12 major industry stock price indices and 3 individual oil company stock price series We also examine the extent to which price changes or returns in one market dynamically lead returns in the others and whether volatility spillover effects exist across these markets Using VAR model estimates for various time series of returns we find that petroleum industry stock index and our three oil company stocks are the only series where we can reject the null hypothesis that oil futures do not lead Treasury Bill rates and stock returns, while we can reject the hypothesis that oil futures lag these other two series Finally, the return volatility evidence for oil futures leading individual oil company stocks is much weaker than is the evidence for returns themselves

Investigating population heterogeneity with factor mixture models.

Abstract: Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved population heterogeneity. Observed sources of heterogeneity can be included as covariates. The different ways to incorporate covariates correspond to different conceptual interpretations. These are discussed in detail. Characteristics of factor mixture modeling are described in comparison to other methods designed for data stemming from heterogeneous populations. A step-by-step analysis of a subset of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied in an exploratory fashion to data collected at a single time point.