Aggregating information in option transactions

The listed options market in the United States trades hundreds of option contracts across different strikes and expirations for each underlying stock. The order flow from these option transactions reveals important information about the underlying stock price movement and its volatility variation. H...

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Bibliographic Details
Main Authors: Holowczak, Richard, HU, Jianfeng, WU, Liuren
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/3609
https://ink.library.smu.edu.sg/context/lkcsb_research/article/4608/viewcontent/SSRN_id1787407.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:The listed options market in the United States trades hundreds of option contracts across different strikes and expirations for each underlying stock. The order flow from these option transactions reveals important information about the underlying stock price movement and its volatility variation. How to aggregate the trade information of different option contracts underlying the same stock presents an important challenge for developing microstructure theories and understanding price discovery mechanisms in the derivatives market. This paper takes options on QQQQ, the Nasdaq 100 tracking stock, as an example and examines different order flow aggregation methods in terms of their effectiveness in extracting information about the underlying stock price movement and its volatility variation. The analysis shows that an effective aggregation method must account for each contract’s different exposure to the stock price and volatility movements, and accommodate concerns on interference from other potential risk dimensions, such as market crashes and long-term versus short-term volatility factors. The paper identifies significant relations, both contemporaneous and predictive, between the appropriately aggregated options order flow and the stock return and the return volatility.