European Finance Association
Milan, Italy | August 25-27, 2021
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APE 02: Derivatives
Generalized Bounds on the Conditional Expected Excess Return on Individual Stocks
1Isenberg School of Management, University of Massachusetts-Amherst; 2Frankfurt School of Finance & Management
We derive generalized bounds on conditional expected excess returns that can be computed from option prices. The generalized lower bound (GLB) may serve as an expected excess return proxy for individual and basket-type assets, is conditionally tight, accounts for the entire risk-neutral distribution of returns, and outperforms existing variance-based models in out-of-sample predictions. Bounds calibrated to realized returns correspond to reasonable risk aversion and prudence. On average, expected stock returns given by the bounds decrease on even weeks of the FOMC cycle. Cross-sectional tests deliver a reasonable market risk premium.
Structural Stochastic Volatility
1Johns Hopkins University, United States of America; 2University of Verona
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 that 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 variance 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.
Testing for Asset Price Bubbles using Options Data
1Johns Hopkins University; 2Cornell University; 3Johns Hopkins University
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 a bubble in the price of the 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.
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