Risk measures computation by Fourier inversion

Ngoc Quynh Anh Nguyen*, Thi Ngoc Trang Nguyen

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Purpose: The purpose of this paper is to present the method for efficient computation of risk measures using Fourier transform technique. Another objective is to demonstrate that this technique enables an efficient computation of risk measures beyond value-at-risk and expected shortfall. Finally, this paper highlights the importance of validating assumptions behind the risk model and describes its application in the affine model framework. Design/methodology/approach: The method proposed is based on Fourier transform methods for computing risk measures. The authors obtain the loss distribution by fitting a cubic spline through the points where Fourier inversion of the characteristic function is applied. From the loss distribution, the authors calculate value-at-risk and expected shortfall. As for the calculation of the entropic value-at-risk, it involves the moment generating function which is closely related to the characteristic function. The expectile risk measure is calculated based on call and put option prices which are available in a semi-closed form by Fourier inversion of the characteristic function. We also consider mean loss, standard deviation and semivariance which are calculated in a similar manner. Findings: The study offers practical insights into the efficient computation of risk measures as well as validation of the risk models. It also provides a detailed description of algorithms to compute each of the risk measures considered. While the main focus of the paper is on portfolio-level risk metrics, all algorithms are also applicable to single instruments. Practical implications: The algorithms presented in this paper require little computational effort which makes them very suitable for real-world applications. In addition, the mathematical setup adopted in this paper provides a natural framework for risk model validation which makes the approach presented in this paper particularly appealing in practice. Originality/value: This is the first study to consider the computation of entropic value-at-risk, semivariance as well as expectile risk measure using Fourier transform method.

Original languageEnglish
Pages (from-to)76-87
Number of pages12
JournalJournal of Risk Finance
Volume18
Issue number1
DOIs
Publication statusPublished - 2017

Fingerprint

Risk measures
Value at risk
Measure of risk
Fourier transform
Risk model
Characteristic function
Loss distribution
Semivariance
Expected shortfall
Moment generating function
Call option
Standard deviation
Design methodology
Put option
Affine model
Cubic spline
Model validation
Option prices

Keywords

  • Entropic value-at-risk
  • Expected shortfall
  • Expectile risk measure
  • Fourier transform
  • Risk management
  • Value-at-risk

ASJC Scopus subject areas

  • Finance
  • Accounting

Cite this

Risk measures computation by Fourier inversion. / Nguyen, Ngoc Quynh Anh; Nguyen, Thi Ngoc Trang.

In: Journal of Risk Finance, Vol. 18, No. 1, 2017, p. 76-87.

Research output: Contribution to journalArticle

Nguyen, Ngoc Quynh Anh ; Nguyen, Thi Ngoc Trang. / Risk measures computation by Fourier inversion. In: Journal of Risk Finance. 2017 ; Vol. 18, No. 1. pp. 76-87.
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