In the second chapter (``A Tale of Two Volatilities: Sectoral Uncertainty, Growth, and Asset-Prices'') I document several novel empirical facts: Technological volatility that originates from the consumption sector plays the ``traditional'' role of depressing the real economy and stock prices, whereas volatility that originates from the investment sector boosts prices and growth; Investment (consumption) sector's technological volatility has a positive (negative) market-price of risk; Investment sector's technological volatility helps explain return spreads based on momentum, profitability, and Tobin's Q. I show that a standard DSGE twosector model fails to fully explain these findings, while a model that features monopolistic power for firms and sticky prices, can quantitatively explain the differential impact of sectoral volatilities on real and financial variables.
In the third chapter (``From Private-Belief Formation to Aggregate-Vol Oscillation'') I propose a model that relies on learning and informational asymmetry, for the endogenous amplification of the conditional volatility in macro aggregates and of cross-sectional dispersion during economic slowdowns. The model quantitatively matches the fluctuations in the conditional volatility of macroeconomic growth rates, while generating realistic real business-cycle moments. Consistently with the data, shifts in the correlation structure between firms are an important source of aggregate volatility fluctuations. Cross-firm correlations rise in downturns due to a higher weight that firms place on public information, which causes their beliefs and policies tocomove more strongly.
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