Essays on time series and causality analysis in financial markets

Financial market and its various components are currently in turmoil. Many largecorporations are devising new ways to overcome the current market instability.Consequently, any study fostering the understanding of financial markets and thedependencies of various market components would greatly benefit both the practitionersand academicians. To understand different parts of the financial market, this dissertationemploys time series methods to model causality and structure and degree of dependence.The relationship of housing market prices for nine U.S. census divisions is studied in thefirst essay. The results show that housing market is very interrelated. The New Englandand West North Central census divisions strongly lead house prices of the rest of thecountry. Further evidence suggests that house prices of most census divisions are mainlyinfluenced by house price changes of other regions.The interdependence of oil prices and stock market indices across countries isexamined in the second essay. The general dependence structure and degree is estimatedusing copula functions. The findings show weak dependence between stock marketindices and oil prices for most countries except for the large oil producing nations which show high dependence. The dependence structure for most oil consuming (producing)countries is asymmetric implying that stock market index and oil price returns tend tomove together more during the market downturn (upturn) than a market boom(downturn).In the third essay, the relationship among stock returns of ten U.S. sectors isstudied. Copula models are used to explore the non-linear, general association among theseries. The evidence shows that sectors are strongly related to each other. Energy sectoris relatively weakly connected with the other sectors. The strongest dependence isbetween the Industrials and Consumer Discretionary sectors. The high dependencesuggests small (if any) gains from industry diversification in U.S.In conclusion, the correct formulation of relationships among variables of interestis crucial. This is one of the fundamental issues in portfolio analysis. Hence, a thoroughexamination of time series models that are used to understand interactions of financialmarkets can be helpful for devising more accurate investment strategies.