Journal of Risk Model Validation

Value-at-risk time scaling: a Monte Carlo approach

Moepa Malataliana and Michael Rigotard

  • Model uses Composite Normal-Pareto Distribution for improved tail modelling
  • The model has been bench-marked against a Kernel distribution
  • Monte Carlo simulation is used for long term VaR estimation
  • The model testing results show that the square-root scaling approach underestimates long term VaR


This paper discusses a value-at-risk (VaR) time-scaling approach based on fitting a distribution function so as to apply a Monte Carlo simulation to determine long-term VaR. The paper uses composite normal-Pareto distribution to better capture tail risk. Due to the material model risk inherent in the long-term VaR calculation, kernel distribution is used as a benchmark distribution.


Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to View our subscription options

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here