UBC Theses and Dissertations
Essays in time series econometrics and international finance Kang, Da (Natasha)
Chapter 1 develops a new econometric framework to model persistent and low-frequency stochastic cycles (referred to as "long cycles") in the data. Existing inferential procedures based on the conventional asymptotic theory may produce misleading results in presence of such cycles. This work provides a new asymptotic theory for statistical inference on long-cycle data. The proposed procedure can be used to rule out cyclical dynamics in the data and to test for the periodicity of cycles. Chapter 2 examines the cyclical properties of business and financial cycles in the U.S. Using the methodology developed in Chapter 1, it provides a set of new empirical results on the cyclical dynamics of macro and financial aggregates. Results from this chapter find evidence of stochastic cycles in key business cycle indicators, as well as in credits to non-financial corporations and households. Moreover, the credit cycle operates at a lower frequency and has more prominent and persistent oscillations. In contrast, fluctuations in the asset market variables related to risk and uncertainty do not exhibit any cyclical dynamics. Chapter 3 studies the relationship between industrial structure of economies and their international portfolio composition. Using U.S industry-level data, it uncovers a new empirical fact: industries with higher capital intensity tend to exhibit lower degrees of equity home bias. This empirical pattern is rationalized in a two-sector and two-country framework with incomplete asset markets. This work contributes to the literature by adding two novel channels affecting international portfolio choices: (i) differences in capital intensity and productivity processes across sectors, which have opposite effects on international portfolio diversification; (ii) differences in capital intensity across sectors, which affect the strength of the demand for domestic equities to hedge fluctuations in non-tradable risk.
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