Testing for Asset Price Bubbles using Options Data

57 Pages Posted: 27 Aug 2020 Last revised: 10 May 2022

See all articles by Nicola Fusari

Nicola Fusari

Johns Hopkins University - Carey Business School

Robert Jarrow

Cornell SC Johnson College of Business

Sujan Lamichhane

International Monetary Fund (IMF)

Date Written: August 10, 2020

Abstract

We present a new approach to identifying asset price bubbles based on options data. We estimate asset bubbles by exploiting the differential pricing between put and call options. 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 do not exhibit significant bubbles, Amazon and Facebook show frequent and significant bubbles. The estimated bubbles tend to be associated with high trading volume and earning announcement days. Since our approach can be implemented in real time, it is useful to both policy-makers and investors. As an illustration we consider two case studies: the Nasdaq dot-com bubble (between 1999 to 2002) and GameStop (between December 2020 and January 2021). In both cases we identify significant and persistent bubbles.

Keywords: Asset Price Bubbles, Option Pricing, Stochastic Volatility, Martingales, Local Martingales

JEL Classification: C51, C52, G12, G13

Suggested Citation

Fusari, Nicola and Jarrow, Robert and Lamichhane, Sujan, Testing for Asset Price Bubbles using Options Data (August 10, 2020). Johns Hopkins Carey Business School Research Paper No. 20-12, Available at SSRN: https://ssrn.com/abstract=3670999 or http://dx.doi.org/10.2139/ssrn.3670999

Nicola Fusari (Contact Author)

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

Robert Jarrow

Cornell SC Johnson College of Business

Ithaca, NY 14850
United States

Sujan Lamichhane

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

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