Essays on household finance and the macroeconomy

The first essay develops a life-cycle portfolio selection model in which households are subject to ambiguity about the risky return dynamics and imperfect information about the mean return. Households have access to different sources of information: 'drift' and 'diffusion' signals. Drift signals provide predictive information that helps households estimate the unobservable mean return. Diffusion signals convey information about the future financial events that affect equity returns. Numerical experiments show that ambiguity has a significant effect on the portfolio decision of households. The model predicts empirically consistent level of portfolio shares and a weak hump with a peak at age late 40s, as in the data.;Chapter 2 examines the cyclic behavior of U.S. housing market by decomposing state-level real housing returns at different frequencies and extracting latent common factor. I also investigate to uncover the source of the nonlinear linkages between housing market and monetary policy by the federal funds rate through EGARCH-filtered nonlinear causality tests in time-frequency domain. The analysis concludes that the time-varying volatility effect over the two-year horizon is the source of the nonlinear relationship. Evidence on strong volatility spillovers implies significant information flows between housing market and the federal funds rate.;Chapter 3 is a note to examine option pricing performance of double exponential jump-diffusion process. Empirical studies show that distribution of asset returns has leptokurtic features and the volatility smile that are caused in part by jumps in diffusion process. This study focuses on the Kuo (2002)'s double exponential jump-diffusion model and experiments pricing performance with alternative models. It shows that the double exponential jump-diffusion model captures deviations from the standard geometric Brownian motion with more precision than the lognormal jump-diffusion model does. However, the double exponential jump-diffusion model and the stochastic volatility with jumps model complement each other in terms of in-sample pricing performance.