Wavelet-based option pricing: An empirical study

Xiaoquan Liu, Yi Cao, Chenghu Ma, Liya Shen

Research output: Journal PublicationArticlepeer-review

11 Citations (Scopus)

Abstract

In this paper, we scrutinize the empirical performance of a wavelet-based option pricing model which leverages the powerful computational capability of wavelets in approximating risk-neutral moment-generating functions. We focus on the forecasting and hedging performance of the model in comparison with that of popular alternative models, including the stochastic volatility model with jumps, the practitioner Black–Scholes model and the neural network based model. Using daily index options written on the German DAX 30 index from January 2009 to December 2012, our results suggest that the wavelet-based model compares favorably with all other models except the neural network based one, especially for long-term options. Hence our novel wavelet-based option pricing model provides an excellent nonparametric alternative for valuing option prices.

Original languageEnglish
Pages (from-to)1132-1142
Number of pages11
JournalEuropean Journal of Operational Research
Volume272
Issue number3
DOIs
Publication statusPublished - 1 Feb 2019

Keywords

  • Artificial neural networks
  • Jump risk
  • Option valuation
  • Pricing
  • Stochastic volatility

ASJC Scopus subject areas

  • Computer Science (all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management
  • Industrial and Manufacturing Engineering

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