A data-driven framework for consistent financial valuation and risk measurement

Zhenyu Cui, J. Lars Kirkby, Duy Nguyen

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for the underlying asset prices, and then utilize Fourier techniques to obtain the physical nonparametric density and cumulative distribution function for the log-returns process, based on which we compute risk measures. Then we risk-neutralize the nonparametric density and distribution functions to model-independently valuate a variety of financial derivatives, including path-independent European options and path-dependent exotic contracts. By estimating the state-price density explicitly, and utilizing a convenient basis representation, we are able to greatly simplify the pricing of exotic options all within a consistent model-free framework. Numerical examples, and an empirical example using real market data (Brent crude oil prices) illustrate the accuracy and versatility of the proposed method in handling pricing and risk management of multiple financial contracts based solely on observable time series data.

Original languageEnglish
Pages (from-to)381-398
Number of pages18
JournalEuropean Journal of Operational Research
Volume289
Issue number1
DOIs
StatePublished - 16 Feb 2021

Keywords

  • Data-driven
  • Empirical characteristic function
  • Empirical density
  • Finance
  • Model-free
  • Nonparametric
  • Risk management

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