Python for Finance, 2nd Edition

Chapter 21. Market-Based Valuation

We are facing extreme volatility.

Carlos Ghosn

A major task in derivatives analytics is the market-based valuation of options and derivatives that are not liquidly traded. To this end, one generally calibrates a pricing model to market quotes of liquidly traded options and uses the calibrated model for the pricing of the non-traded options.¹

This chapter presents a case study based on the DX package and illustrates that this package, as developed step-by-step in the previous four chapters, is suited to implement a market-based valuation. The case study is based on the DAX 30 stock index, which is a blue chip stock market index consisting of stocks of 30 major German companies. On this index, liquidly traded European call and put options are available.

The chapter is divided into sections that implement the following major tasks:

“Options Data”

One needs two types of data, namely for the DAX 30 stock index itself and for the liquidly traded European options on the index.

“Model Calibration”

To value the non-traded options in a market-consistent fashion, one generally first calibrates the chosen model to quoted option prices in such a way that the model based on the optimal parameters replicates the market prices as well as possible.

“Portfolio Valuation”

Equipped with the data and a market-calibrated model for the DAX 30 stock index, the final task then is to model and value the non-traded options; important risk measures are also estimated ...