In this paper, we study on the stock indexes return¡¦s dependence structure of the U.S. versus other G5 members during the 2008 subprime mortgage financial crisis. The sample series are weekly returns of the MSCI stock price indexes from 2003 to 2011. The model structure is combined with marginal model and copula model. We model the marginal distributions of our returns using the univariate skewed Student t AR¡]1¡^-GARCH model of Hansen¡]1994¡^, and we model the time-varying copula of Patton¡]2006¡^to measure the dependence structure between stock indexes returns. By analyzing the time series behavior of the dynamic copula parameters, we find that,¡]1¡^the dependence of stock indexes returns increased significantly between U.S. and other G5 members in early subprime mortgage financial crisis, which means the dependence structure has contagion effect.¡]2¡^Except the dependence structure between U.S. and Japan, the other dependence structure between U.S. and other G5 members in later subprime mortgage financial crisis have the phenomenon of interdependence, and their average tail dependence increased significantly.¡]3¡^By the above, international portfolio constructed by correlation coefficient will failed to diversify the downside risk and the systematic risk will be increased in financial crisis period, which is similar with the 2008 subprime mortgage financial crisis. Therefore, the construction of an international portfolio must consider the asymmetric dependence structure between the stock indexes returns.

This paper studies the dependence structure between the real estate and financial markets in the United States from roughly 1975 to 2010, including the stock, bond and foreign exchange markets. This analysis uses dynamic copulas, including the Gaussian, Gumbel and Clayton copula. The Gumbel and Clayton copulas are used to separately capture the tail dependence of data. The dependence between the property indices (HPI and NCREIF) and the three financial markets is analyzed using the parameters of the copula. The property indices are divided in two different ways: by different regions and by different types of real estate. Although we study the dependence between the real estate and the financial markets in the U.S., the main objective of this paper is to analyze the change in the dependence structure when financial disasters occur. This study indicates that the real estate and the stock markets were positively related during this time period, and this dependence drove extreme movement when financial crises occurred. This dependence differed depending on the type of financial crisis, such as the Internet bubble crisis or the financial crisis in 2008. The dependence between the real estate and bond markets was also positively related, and extreme movement also occurred during financial crises. As for the dependence between the real estate and foreign exchange markets, although the results shows that dependence decreased when financial crises occurred, this is because the value of U.S. dollars are opposite to those of the index, and the left tail dependence exists as previous result. When looking at different regions or types of property, the differences in dependence structure were not obvious, although they were positively related. Both right and left tail dependences existed for most regions and property types, although some regions or types showed either right or left tail dependences alone. Therefore, investors should focus on the relationship between different markets, not on the region or type of real estate.

This study measures the short-term and long-term contagion effects in U.S. stock markets and REITs (Real Estate Investment Trusts) markets during the periods of subprime mortgage and financial crises. First, we test contagion between the U.S. stock market and the U.S. REITs market. Then, we test the contagion effects between the U.S. REITs market and eighteen international REITs markets, selected from North America, Oceania, Asian and Europe. To catch the asymmetric effect in the volatility structure of index returns and consider the time-varying data, this study employs asymmetric dynamic Copula models that measure contagion effects. The test result in this study shows that the contagion effect exists because of the fact that during the subprime mortgage crisis, the correlation between the U.S. stock market and REITs market significantly increased. Thus, the two markets lost ground together. While managing not to emerge in Asian REITs markets, the contagion then spread from the U.S. REITs market to Canada, Australia and most of the European REITs markets. In the later financial crisis period, however, the number of European REITs markets impacted by contagion from the U.S. REITs market decreased. Except for Singapore, contagion is absent from the Asian REITs markets. Contagion is more obvious in the short term than in the long term. These results imply that the Asian REITs markets are not easily affected by the U.S. REITs market, which in turn implies that investors could obtain the positive effects of international diversification by investing in this portfolio. In addition, investors should reduce the proportion of their investments placed in REITs markets, as well as focus on a long-term diversification strategy.

This dissertation is composed of three chapters, each an application of Bayesian statistical models to particular research questions. In Chapter 1, we evaluate systemic risk exposure of financial institutions. Building upon traditional regime switching approaches, we propose a network model for volatility contagion to assess linkages between institutions in the financial system. Focusing empirical analysis on the financial sector, we find that network connectivity has dynamic properties, with linkages between institutions increasing immediately before the recent crisis. Out-of-sample forecasts demonstrate the ability of the model to predict losses during distress periods. We find that institutional exposure to crisis events depends upon the structure of linkages, not strictly the number of linkages. In Chapter 2, we develop procedures for benchmarking small area estimates. In sample surveys, precision can be increased by introducing small area models which "borrow strength" by incorporating auxiliary covariate information. One consequence of using small area models is that small area estimates at lower geographical levels typically will not aggregate to the estimate at the corresponding higher geographical levels. Benchmarking is the statistical procedure for reconciling these differences. Two new approaches to Bayesian benchmarking are introduced, one procedure based on Minimum Discrimination Information, and another for Bayesian self-consistent conditional benchmarking. Notably the proposed procedures construct adjusted posterior distributions whose moments all satisfy benchmarking constraints. In the context of the Fay-Herriot model, simulations are conducted to assess benchmarking performance. In Chapter 3, we exploit the Pair Copula Construction (PCC) to develop a flexible multivariate model for time-varying dependence. The PCC is an extremely flexible model for capturing complex, but static, multivariate dependency. We use a Bayesian framework to extend the PCC to account for time dynamic dependence structures. In particular, we model the time series of a transformation of parameters of the PCC as an autoregressive model, conducting inference using a Markov Chain Monte Carlo algorithm. We use financial data to illustrate empirical evidence for the existence of time dynamic dependence structures, show improved out-of-sample forecasts for our time dynamic PCC, and assess performance of dynamic PCC models for forecasting Value-at-Risk. / Statistics