Structural Stochastic Volatility
(with Federico Bandi and Roberto Reno')

We present a new methodology for estimating spot equity characteristics (i.e. spot volatility, sport leverage, and spot volatility-of-volatility) from option data. Within a simple structural model we show both empirically and theoretically a strong link between financial leverage and the three equity characteristics.
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We use a local (in time) expansion of the characteristic function of the equity process in continuous time to derive short-maturity option prices. The prices, along with data on short- maturity options, are employed to jointly identify equity characteristics (spot volatility, spot leverage and spot volatility of volatility) which have been the focus of separate strands of the literature. We show that the proposed identification method yields measurements which are statistically accurate and economically revealing. Interpreting equity as a call option on asset values, all equity characteristics should depend on fundamental state variables, such as the vari- ance of the firm’s assets and the extent of the firm’s financial leverage. Among other findings, consistent with economic logic, we document a strong link between spot leverage (the generally- negative correlation between equity returns and spot volatility) and financial leverage (the firm’s debt-to-equity ratio), a relation invariably found to be elusive in the data. We conclude that the economic content of option-implied measurements can be put to work to study the structural drivers of equity (and debt) return dynamics from a novel vantage point.

Asset Pricing with Cohort-Based Trading in MBS Markets
(with Wei Li, Haoyang Liu, and Zhaogang Song)

We show that the dispersion in quality and the unique market structure of agency mortgage backed securities have significant impact on MBS returns.
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Agency mortgage backed securities (MBSs) with diverse characteristics are traded in parallel with individualized contracts in the specified pool (SP) market and with standardized contracts in the to-be-announced (TBA) market. We find that this unique parallel trading environment significantly affects MBS returns: (1) Greater heterogeneity in MBS values in- creases the yields of all MBSs, because it exacerbates the cheapest-to-deliver concerns for TBA buyers and reduces the value of the TBA market as a backup selling venue for SP buyers; (2) high selling pressure amplifies the impact of MBS heterogeneity on MBS yields; (3) greater MBS heterogeneity dampens trading activities on both the SP and TBA markets but increases the ratio between the two. We provide evidence that these effects differ from the impacts of prepayment risks.

Testing for Asset Price Bubbles using Options Data
(with Robert Jarrow and Sujan Lamichhane)

We show that option data can be effectively utilized to detect and quantify bubbles in the underlying asset.
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We present a new approach to identifying asset price bubbles based on options data. Given their forward-looking nature, options are ideal instruments with which to investigate market expectations about the future evolution of asset prices, which are key to understanding price bubbles. By exploiting the differential pricing between put and call options, we can detect and quantify bubbles in the prices of underlying asset. We apply our methodology to two stock market indexes, the S&P 500 and the Nasdaq-100, and two technology stocks, Amazon and Facebook, over the 2014-2018 sample period. We find that, while indexes exhibit rare and modest bubbles, Amazon and Facebook show more frequent and much larger bubbles. Since our approach can be implemented in real time, it is useful to both policy-makers and investors. As an illustration, our methodology applied to GameStop identifies a significant bubble between December 2020 and January 2021.