| This dissertation consists of three essays focusing on financial markets and financial industry.;In the first essay, we use a time-varying conditional copula approach to model Chinese and U.S. stock markets dependence structures with other financial markets. The AR-GARCH-t model is used to examine the marginal distributions, while Normal and Generalized Joe-Clayton copula models are employed to analyze the joint distributions. In this pairwise analysis, both constant and time-varying conditional dependence parameters are estimated by a two-step maximum likelihood method. A comparative analysis of dependence structures in Chinese versus U.S. stock markets is also provided. There are three main findings: First, the time-varying-dependence model does not always perform better than constant-dependence model. This result has not previously been reported in the literature. Second, we find that the upper tail dependence is much higher than the lower tail dependence in some short periods, which has not been documented in previous literature. Last, Chinese financial market is relatively separate from other international financial markets in contrast to the U.S. market. The tail dependence with other financial markets is much lower in China than in the United States. Dependence, on average, rises significantly over sub-periods.;In the second chapter, we use copula-GARCH models to investigate dependence between the returns to banking and three other financial businesses: insurance underwriting, securities brokerage, and mortgage finance. Typically, negative shocks are less likely to occur to both banking and one of these other financial businesses than to both banking and the market overall. However, in a financial crisis, when risk-reducing diversification is most important, negative tail dependence between banking and other financial businesses increases dramatically. These findings casts doubt on any risk-reducing benefits associated with conglomerates combining banking and other financial businesses.;In the third essay, in response to a recent debate on the weather effect on stock market returns (see Kamstra et al. (2003, 2009), Cao and Wei (2005a,b), Jacobsen and Marquering (2008, 2009)), we use semi-parametric bin tests, various regression models and conditional copula techniques to identify the relationship between temperature and stock market returns. After examining 25 international stock markets, we find that the negative correlation is statistically significant in most individual countries. However, we fail to find joint significance of temperature effects across markets after correcting for market comovement by seemingly unrelated regression. We disentangle pure temperature effect and seasonality by including month dummies to regression models and find negative temperature effects on returns do not change very much. Our results are also robust to a variety of model specifications and different measures of daily temperature. More importantly, to overcome some drawbacks of naive regression analyses, both constant and time-varying conditional copula models are employed to analyze the general dependence between temperature and stock market returns. The copula results show that the negative relation remains even after controlling for autocorrelations, GARCH effects and non-normality. We therefore conclude that the correlation between temperature and stock market returns is prevalent and relatively stable over time. |
