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Essays on Empirical Option Pricing and Asset Pricing
Hu, Yuan
Hu, Yuan
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Thesis/Dissertation
Date
2025-08
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Business Administration/Finance
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Hu_temple_0225E_16264.pdf
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https://doi.org/10.34944/jeg5-8113
Abstract
My dissertation consists of three chapters that explore the equilibrium prices and returns of options, as well as the cross-sectional dispersion of expected asset returns. In the first chapter, based on my joint work with Gurdip Bakshi and Xiaohui Gao Bakshi, we present and analyze two salient observations of the crude oil derivatives markets: (i) deep out-of-the-money calls exhibit significantly negative average excess returns, and (ii) the VIXoil is high and consistently exceeds VIXS&P500. The two empirical facts motivate a model with idiosyncratic, jump, unspanned, and spanned risks. We derive expressions for futures prices, option prices, and option risk premiums, and use Kalman filtering and maximum-likelihood to estimate the model. Our results show that the model simultaneously explains the diverse behaviors of option risk premiums, VIXoil, option prices, and realized oil return volatilities. In the second chapter, drawing on another paper coauthored with Gurdip Bakshi and Xiaohui Gao Bakshi, we propose a model of options and futures on the dollar index, which are traded vehicles to protect against concerted U.S. dollar appreciations or depreciations. Estimating themodel, we draw quantitative assessments and examine consistency with new empirical findings. First, average excess returns for out-of-the-money (OTM) calls on dollar index futures are negative and statistically significant, whereas those for OTM puts are insignificant. Second, conveying strike-price-dependent dollar concerns, average excess returns for call options become more negative at higher strikes. The estimated model adheres to the data on option prices, volatilities, and supports negative risk premiums for OTM call options. In the third chapter, I study the puzzling disconnect between stock expected returns and macrofinance theories. I attempt to explain characteristics-based portfolios through a simultaneous consideration of macroeconomic fundamental shocks, each motivated by an individual economic theory. Empirically, I construct the shocks as innovations to large language models’ forecasts of theory-motivated macroeconomic fundamentals, which are referred to as AI-generated shocks. I find that both the idea of aggregation and using large language models to generate shocks contribute incrementally to explaining the cross-section of expected returns.
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