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Put-Call Parity in Practice: How Options Traders Really Close Positions

Daniel Brown shows how he teaches put-call parity through a real S&P 500 options trading question using live CBOE prices and an intuitive trading strategy.

  • Higher Education
  • On-demand
  • Event
  • Finance
  • Derivatives Futures & Options
  • Investments
  • Education For All
  • 40 Minutes
  • On-Demand Video

Description

Put–call parity is often taught as just a relationship between call and put prices, an equation students memorize but rarely use. Daniel Brown demonstrates how he turns put–call parity into one of his most engaging classroom discussions by framing it around a simple question: You own a deep in-the-money S&P 500 call option, how do you close out the position?

Using real option prices from the CBOE, Daniel walks through the decision to either sell the option in the market or replicate the close-out using a trading strategy implied by put–call parity. This approach allows students to see put-call parity, not as an abstract identity, but as a practical tool for evaluating real trading decisions.

Daniel also shares how, in his advanced optimization course, students build a “close-out” tool that automates the strategy, reinforcing both options theory and applied problem-solving.

Viewers will gain ideas for:

  • Making put–call parity intuitive and memorable

  • Using live market data to drive classroom discussion

  • Connecting options theory to real trading and optimization decisions

About Your Speaker

  • Daniel Brown -

    Daniel Brown

    Daniel Brown is a teaching professor in the Finance department of the Leeds School of Business. Prior to joining the school, Daniel spent 18 years working on Wall Street and in the City of London. He spent 14 years at Credit Suisse primarily as a derivatives "quant" but also spending time in risk management and exotics derivatives trading. Following that he spent two years at CQS, a credit and convertible bond hedge fund, and two years at Morgan Stanley again as a derivatives quant.