Date of Award
Doctor of Philosophy (PhD)
In the first chapter (``Good and Bad Uncertainty: Macroeconomic and Financial Market Implications'' with Ivan Shaliastovich and Amir Yaron) we decompose aggregate uncertainty into `good' and `bad' volatility components, associated with positive and negative innovations to macroeconomic growth. We document that in line with our theoretical framework, these two uncertainties have opposite impact on aggregate growth and asset prices. Good uncertainty predicts an increase in future economic activity, such as consumption, and investment, and is positively related to valuation ratios, while bad uncertainty forecasts a decline in economic growth and depresses asset prices. The market price of risk and equity beta of good uncertainty are positive, while negative for bad uncertainty. Hence, both uncertainty risks contribute positively to risk premia.
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 two-sector 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 to comove more strongly.
Segal, Gill, "Essays in Asset Pricing and Volatility Risk" (2016). Publicly Accessible Penn Dissertations. 1996.