Risk management under time varying volatility and Pareto-stable distributions

摘要

Risk measures based on Gaussian return distributions are simple but inaccurate while such measures based on alternative methodologies are known to be more precise but complex. In this context, practitioners seem biased towards simplicity and tend to choose the inaccurate Gaussian measures, leading to unsuspected losses in the event of a negative episode. This article proposes generalized autoregressive conditional heteroskedasticity (GARCH) family models with stable Paretian innovations in measuring the value-at-risk, expected shortfall and spectral risk measures that promise a markedly improved performance while maintaining simplicity.