Python for Finance, 3rd Edition

Chapter 31. Market-Based Valuation

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Model-based valuation becomes useful in practice only when you connect it to market data. In the previous chapters, you implemented simulation models and Monte Carlo valuation routines, including an LSM pricer for American options. This chapter adds the missing “market link”: you load a real options snapshot, extract a small implied-volatility surface, and use it to set model inputs for valuation.

This chapter uses an SPX options quotes snapshot from data/ to build a small three-expiry implied-volatility surface with five strikes per expiry. You derive forward levels from put-call parity, compute implied volatilities via a Black-76 pricing function, build a small interpolating surface object, calibrate jump-diffusion and Heston models to selected smiles, and price European and American options from calibrated Heston paths. The examples assume numpy, pandas, Matplotlib, the valuation code from Chapter 29, the portfolio code from Chapter 30, and the SPX options ...