Essays on Asset Pricing and Political Risk
Issue Date
2012-12-31Author
Lugovskyy, Josephine Cruz
Publisher
University of Kansas
Format
103 pages
Type
Dissertation
Degree Level
Ph.D.
Discipline
Economics
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This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
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Some financial assets experience higher average returns than others. Asset-pricing theory suggests that this is due to the amount of systematic risk of that particular asset: the riskier the asset, the higher the average return. I hypothesize that one of these systematic risk factors is political risk, the result of political uncertainty. This dissertation examines the effect of political risk, in the form of terrorism risk and political regime change risk, on asset prices using linear factor asset pricing models. I find that terrorism risk is a significantly priced risk factor for nonindustry portfolios from January 1971 through December 2010. Results, however, differ when the data is divided into time periods before and after September 11, 2001, with the latter period indicating no risk from terror attacks. I further analyze the determinants of terrorism finding that social and geographic variables contribute more to terror activity than economic variables. I discover that political regime change risk is a nontrivial risk factor from 1927 through 2009, though risk premia results are smaller from industry returns than returns organized based on firm size and book-equity to market-equity. I also find that average excess returns are larger when the government is under the control of the Democratic party rather than for the Republican party for that same time period. Both sets of risk premia are ascertained by regression-based and nonlinear estimation methodologies. I investigate the small sample properties of two cross-sectional regression methods, ordinary and generalized least squares, and two generalized method of moments estimators, two-step and iterative. Using monte carlo simulations, I determine that the least squares regression methods outperform the generalized method of moments estimation procedures in terms of rejection rates and point estimates in small samples.
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