Synthetic Options and Rationale

The prices of put and call options have an identity relationship through the concept of put-call parity.

c0 + X/(1+rF)T = p0 + S0

  • c0 = Current price of the European call
  • p0 = Current price of the European put
  • X = Strike price of the put and the call
  • T = Time to expiration
  • rF = Risk free rate
  • S0 = Current spot price of the underlying asset

The formula translation is: the price of a call with strike X plus the present value of strike price X equals the price of the put with strike X plus the current spot price.

  • Synthetic Call Option: If an investor believes that a call option is over-priced, then he/she can sell the call on the market and replicate a synthetic call.

  • Borrow the present value of the strike price at the risk free rate and purchase the underlying stock and a put.

c0 = p0 + S0 - X/(1+rF)T

  • Synthetic Put Option: Similar to the synthetic call option. A synthetic put can be created by re-arranging the put-call parity relationship, if the trader believes the put is overvalued.
  • Synthetic Stock: A synthetic stock can also be created by rearranging the put-call parity identity. In this case, the investor will buy the call, sell the put, and lend the present value of the strike at the current risk free rate.

S0 = c0 - p0 + X/(1+rF)T

  • If the stock rises in value, then the long call will provide the upside; if the stock falls, then the short put will replicate the downside.

  • Rationale for a "Synthetic"

  • Rational investors would not arbitrarily enter into "synthetic" position; it is done to exploit a perceived mispricing. For example, if the investor believes that put call parity is showing that a stock's call is overvalued, then he/she may execute a synthetic call strategy.

  • The key to a synthetic strategy is to buy the undervalued asset, sell the overvalued asset and invest or borrow the difference at the risk free rate.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.