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Essays in empirical asset pricing

Huang, Jiantao (2022) Essays in empirical asset pricing. PhD thesis, London School of Economics and Political Science.

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Identification Number: 10.21953/lse.00004401


In this thesis, I develop new econometric techniques to measure and understand the sources of economic risks in equity markets. The first chapter studies frequency-dependent risks in the factor zoo. My approach generalizes canonical principal component analysis (PCA) by exploiting frequency-dependent information in asset returns. Empirically, the linear stochastic discount factor (SDF) composed of the first few low-frequency principal components (PCs) capture all the risk premia in asset returns. It also explains well the cross-section of characteristic-sorted portfolios. In contrast, high-frequency and canonical PCA have inferior performance since they fail to identify slow-moving information in asset returns. Moreover, I decompose the low-frequency SDF into two orthogonal priced components. The first component is constructed by high-frequency or traditional PCA. It is almost serially uncorrelated and relates to discount-rate news, intermediary factors, jump risk, and investor sentiment. The second component is slow-moving and captures business-cycle risks related to consumption and GDP growth. Hence, only low-frequency PCA identifies the second persistent component emphasized by many macro-finance models. The second chapter (with Svetlana Bryzgalova and Christian Julliard) proposes a novel framework for linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For (potentially misspecified) standalone models, it provides reliable estimates of risk prices for both tradable and non-tradable factors and detects those weakly identified. For competing factors and (possibly non-nested) models, the method automatically selects the best specification { if a dominant one exists { or provides a Bayesian model averaging (BMASDF) if there is no clear winner. We analyse 2.25 quadrillion models generated by a large set of factors and find that the BMA-SDF outperforms existing models in- and out-of-sample. The third chapter (with Ran Shi) develops a Bayesian approach to quantify model uncertainty about linear SDFs, defined as the entropy of posterior model probabilities. We show that model uncertainty displays massive fluctuations over time, and high model uncertainty coincides with major market events. These observations hold not only in US markets but also in European and Asian Pacific equity markets. Moreover, positive model uncertainty shocks relate to sharp out flows from US equity mutual funds but significant in flows to government bond funds, with effects persisting for three years. In survey data, investors tend to be more pessimistic about equity performance during periods of higher model uncertainty.

Item Type: Thesis (PhD)
Additional Information: © 2022 Jiantao Huang
Library of Congress subject classification: H Social Sciences > HB Economic Theory
H Social Sciences > HG Finance
Sets: Departments > Finance
Supervisor: Julliard, Christian

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