Journal of Risk Model Validation
ISSN:
1753-9579 (print)
1753-9587 (online)
Editor-in-chief: Steve Satchell
Value-at-risk time scaling: a Monte Carlo approach
Moepa Malataliana and Michael Rigotard
Need to know
- 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
Abstract
ABSTRACT
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.
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