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Essays in Asset Pricing and Volatility Risk University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2016 Essays in Asset Pricing and Volatility Risk Gill Segal University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Finance and Financial Management Commons Recommended Citation Segal, Gill, "Essays in Asset Pricing and Volatility Risk" (2016). Publicly Accessible Penn Dissertations. 1996. https://repository.upenn.edu/edissertations/1996 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1996 For more information, please contact [email protected]. Essays in Asset Pricing and Volatility Risk Abstract 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 eaturf es monopolistic power for firms and sticky prices, can quantitatively explain the differential impact of sectoral volatilities on real and financial ariables.v 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. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Finance First Advisor Amir Yaron Keywords Asset Pricing, Economic growth, Volatility Subject Categories Finance and Financial Management This dissertation is available at ScholarlyCommons: https://repository.upenn.edu/edissertations/1996 ESSAYS IN ASSET PRICING AND VOLATILITY RISK Gill Segal A DISSERTATION in Finance For the Graduate Group in Managerial Science and Applied Economics Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy 2016 Supervisor of Dissertation Amir Yaron, Robert Morris Professor of Banking and Finance Graduate Group Chairperson Eric T. Bradlow, K.P. Chao Professor of Marketing, Statistics, and Education Dissertation Committee Itay Goldstein, Joel S. Ehrenkranz Family Professor of Finance Jo~aoGomes, Howard Butcher III Professor of Finance Ivan Shaliastovich, Assistant Professor of Finance ACKNOWLEDGEMENT I would like to thank my advisors Amir Yaron, Itay Goldstein, Jo~aoGomes and Ivan Shaliastovich for their helpful guidance, for the time and dedication in advising me, and for the moral support that they provided me. All mistakes are my own. I would also like to thank my friends and classmates at Penn: Christine Dobridge, Ian Appel, Sang Byung Seo, Ram Yamarthy, Michael Lee, Andrew Wu, Colin Ward, and Yasser Boualam. Finally, I am extremely grateful to my family for their support. ii ABSTRACT ESSAYS IN ASSET PRICING AND VOLATILITY RISK Gill Segal Amir Yaron In the first chapter (\Good and Bad Uncertainty: Macroeconomic and Financial Market Im- plications" with Ivan Shaliastovich and Amir Yaron) we decompose aggregate uncertainty into `good' and `bad' volatility components, associated with positive and negative innova- tions 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 in- vestment, 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 eq- uity 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 pro- iii pose a model that relies on learning and informational asymmetry, for the endogenous amplification of the conditional volatility in macro aggregates and of cross-sectional dis- persion 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 cor- relations rise in downturns due to a higher weight that firms place on public information, which causes their beliefs and policies to comove more strongly. iv TABLE OF CONTENTS ACKNOWLEDGEMENT . ii ABSTRACT . iii LIST OF TABLES . ix LIST OF ILLUSTRATIONS . x CHAPTER 1 : Good and Bad Uncertainty: Macroeconomic and Financial Market Implications . 1 1.1 Introduction . 1 1.2 Economic model . 8 1.3 Data and uncertainty measures . 20 1.4 Empirical results . 25 1.5 Robustness . 36 1.6 Conclusion . 40 CHAPTER 2 : A Tale of Two Volatilities: Sectoral Uncertainty, Growth, and Asset- Prices . 63 2.1 Introduction . 63 2.2 Related Literature . 68 2.3 The Facts . 72 2.4 The Model . 88 2.5 Model Intuition . 96 2.6 Quantitative Model Results . 103 2.7 Conclusion . 117 CHAPTER 3 : From Private-Belief Formation to Aggregate-Vol Oscillation . 142 v 3.1 Introduction . 142 3.2 Related Literature . 147 3.3 Model . 151 3.4 Data and Volatility Measures . 159 3.5 Calibration and Unconditional Moments . 163 3.6 Results . 167 3.7 Conclusion . 182 APPENDIX . 199 BIBLIOGRAPHY . 213 vi LIST OF TABLES TABLE 1.1 : Data summary statistics . 42 TABLE 1.2 : Macroeconomic uncertainties and aggregate growth . 43 TABLE 1.3 : Macroeconomic uncertainties and investment . 44 TABLE 1.4 : Macroeconomic uncertainties and aggregate prices . 45 TABLE 1.5 : Macroeconomic uncertainties and equity returns . 46 TABLE 1.6 : Cross-sectional implications . 47 TABLE 1.7 : Risk premia decomposition . 48 TABLE 1.8 : Simulation analysis of macroeconomic uncertainties and aggregate growth . 49 TABLE 1.9 : Model-implied significance of the volatility coefficients . 50 TABLE 1.10 :Conditionally demeaned industrial production-based uncertainties . 51 TABLE 1.11 :Industrial production-based uncertainties with shifted cutoff . 52 TABLE 1.12 :Earnings-based uncertainties . 53 TABLE 1.13 :Benchmark uncertainties: post-war sample . 54 TABLE 2.1 : Sectoral Shocks and Aggregate Cash-Flow (Macroeconomic) Growth 119 TABLE 2.2 : Sectoral Shocks and Aggregate Inputs Growth . 120 TABLE 2.3 : Sectoral Shocks and Detrended Macroeconomic Variables . 121 TABLE 2.4 : Sectoral Shocks and the Cross-section of Returns . 122 TABLE 2.5 : Sectoral (Industry) Exposures to Sectoral Shocks . 123 TABLE 2.6 : Summary of Pricing Statistics from a Four-Factor Model . 124 TABLE 2.7 : Summary of Pricing Statistics from a Two-Factor Model . 125 TABLE 2.8 : Sectoral Volatilities and Debt Measures .
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