New York, New York, United States
Researched methods of identifying and profiling possible negligent trades in wide options markets using real-time estimated risk measurements, or option Greeks.
• Created MS SQL Server queries for analyzing multiple years of historical trade data, joining across multiple data sources.
I learned how to use important SQL tools such as table-valued functions and common table expressions. I mainly focused on generating descriptive statistics of options that are highly sensitive to underlier price changes.
• Created alternative thresholds for defining wide markets, per SEC Harmonized Rules 34-73884.
In short, these Harmonized Rules define bid-ask spread thresholds at which an option trade is considered to be occurring in a "wide market". The purpose of these thresholds is to standardize the definition of a wide market, during which trades may occur under illiquid or other non-ideal conditions. Currently, the threshold chosen for a given trade is based on its bid price, but for trades with bids above $100, there is only one default threshold.
As an intern, I developed a numerical root solver in Python to find more statistically rigorous bid-ask spread thresholds for option trades with bid prices above $100. My supervisor broke down this large category of option trades into smaller bands (e.g., including a $100-$200 category, etc.). I then used the Bisection Method to find the threshold for each of these smaller categories at which 3% of the category's net dollar volume could be considered "wide," defining a more refined threshold.
• Wrote asynchronous Java code to query a proprietary time-series datastore
