Before revision:  The properties of the resulting GPO-ABC method are studied using stochastic volatility (SV) models with both synthetic and real-world data . We conclude that the algorithm enjoys: good accuracy comparable to particle Markov chain Monte Carlo with a significant reduction in computational cost and better robustness to noise in the estimates compared with a gradient-based optimisation algorithm. Finally, we make use of GPO-ABC to estimate the Value-at-Risk for a portfolio using a copula model with SV models for the margins .
After revision:  The properties of the resulting GPO-ABC method are studied using stochastic volatility (SV) models with both synthetic and real-world data as well as for estimating the Value-at-Risk for a portfolio using a copula model with SV models for the margins .