In this dissertation, three related issues of building empirical time series models for
financial markets are investigated with respect to contemporaneous causality, dynamics,
and structural change. In the first essay, nation-wide industry information transmission
among stock returns of ten sectors in the U.S. economy is examined through the
Directed Acyclical Graph (DAG) for contemporaneous causality and Bernanke
decomposition for dynamics. The evidence shows that the information technology sector
is the most root cause sector. Test results show that DAG from ex ante forecast
innovations is consistent with the DAG fro m ex post fit innovations. This supports
innovation accounting based on DAGs using ex post innovations.
In the second essay, the contemporaneous/dynamic behaviors of real estate and stock
returns are investigated. Selected macroeconomic variables are included in the model to
explain recent movements of both returns. During 1971-2004, there was a single
structural break in October 1980. A distinct difference in contemporaneous causal
structure before and after the break is found. DAG results show that REITs take the role of a causal parent after the break. Innovation accounting shows significantly positive
responses of real estate returns due to an initial shock in default risk but insignificant
responses of stock returns. Also, a shock in short run interest rates affects real estate
returns negatively with significance but does not affect stock returns.
In the third essay, a structural change in the volatility of five Asian and U.S. stock
markets is examined during the post-liberalization period (1990-2005) in the Asian
financial markets, using the Sup LM test. Four Asian financial markets (Hong Kong,
Japan, Korea, and Singapore) experienced structural changes. However, test results do
not support the existence of structural change in volatility for Thailand and U.S. Also,
results show that the Generalized Autoregressive Conditional Heteroskedasticity
(GARCH) persistent coefficient increases, but the Autoregressive Conditional
heteroskedasticity (ARCH) impact coefficient, implying short run adjustment, decreases
in Asian markets.
In conclusion, when the econometric model is set up, it is necessary to consider
contemporaneous causality and possible structural breaks (changes). The dissertation
emphasizes causal inference and structural consistency in econometric modeling. It
highlights their importance in discovering contemporaneous/dynamic causal
relationships among variables. These characteristics will likely be helpful in generating
accurate forecasts.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4231 |
| Date | 30 October 2006 |
| Creators | Kim, Jin Woong |
| Contributors | Bessler, David A., Leatham, David J. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 992390 bytes, electronic, application/pdf, born digital |
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