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Essays in asset pricing

Shi, Ran (2022) Essays in asset pricing. PhD thesis, London School of Economics and Political Science.

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Identification Number: 10.21953/lse.00004418


This thesis contains three chapters studying asset prices from different financial markets to understand the economic forces driving their movements and recover economic variables of interest. In Chapter 1, I develop and estimate a model to quantify the effects of financial constraints, arbitrage capital, and hedging demands on asset prices and their deviations from frictionless benchmarks. Using foreign exchange derivatives data, I find that financial constraints and hedging demands contribute to 46 and 35 percent variation in the deviations from covered interest parity of the one-year maturity. While arbitrage capital fluctuation explains the remaining 19 percent variation on average, it periodically stabilizes prices when the other two forces exert disproportionately large impacts. The model features general financial constraints and produces a nonparametric arbitrage profit function. I unveil the shapes and dynamics of financial constraints from estimates of this function. In Chapter 2 (co-authored with Ian Martin), we propose a framework to compute sharp bounds of the crash probability of an individual stock using option prices. Empirical tests suggest that these bounds are close to the exact forward-looking crash probabilities. Out of sample, either the lower or upper bound outperforms combinations of stock characteristics in terms of forecasting stock-specific crash events. Applying the framework to study the equity of global systemically important banks (G-SIBs) gives rise to forward-looking fragility and stability measures of the global financial system. In Chapter 3 (co-authored with Jiantao Huang), we develop a transparent Bayesian approach to quantify uncertainty in linear stochastic discount factor (SDF) models. We show that, for a Bayesian decision maker, posterior model probabilities increase with maximum in-sample Sharpe ratios and decrease with model dimensions. Entropy of posterior probabilities represents model uncertainty. We apply our approach to quantify the time series of model uncertainty in North American, European, and Asian Pacific equity markets. Model uncertainty is countercyclical in these markets before the 2008 financial crisis, but remains high afterwards. It predicts investors’ asset allocation decisions across equity and fixed-income funds. In survey data, investors tend to be more pessimistic about equity performance during periods of high model uncertainty.

Item Type: Thesis (PhD)
Additional Information: © 2022 Ran Shi
Library of Congress subject classification: H Social Sciences > HG Finance
Sets: Departments > Finance
Supervisor: Martin, Ian and Yuan, Kathy

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