Computational challenges for value-at-risk and expected shortfall: Chebyshev interpolation to the rescue? Online publication date: Fri, 25-Jun-2021
by Sascha Wilkens
International Journal of Financial Markets and Derivatives (IJFMD), Vol. 8, No. 2, 2021
Abstract: Computational challenges associated with calculating risk measures are inherent to many applications in financial institutions. An example is the need to revalue portfolios of trading positions hundreds or thousands of times to determine the future distribution of their present values and risk measures such as value-at-risk and expected shortfall. This paper reports on an exploratory study in which recently popularised 'smart grids' based on Chebyshev interpolation are compared to standard (uniform) grids as well as Taylor expansion when applied to this task. While generally outperforming other methods and despite their advantageous properties, Chebyshev grids are still subject to drawbacks such as difficult error control and the curse of dimensionality. They cannot yet be seen as a quantum leap in the calculation of risk measures. Ongoing research focussing on sparse grids and their approximation quality, however, is promising.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Financial Markets and Derivatives (IJFMD):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook