Point process models for extreme returns: Harnessing implied volatility

Forecasting the risk of extreme losses is an important issue in the management of financial risk. There has been a great deal of research examining how option implied volatilities (IV) can be used to forecast asset return volatility. However, the role of IV in the context of predicting extreme risk has received relatively little attention. The potential benefit of IV in forecasting extreme risk is considered within a range of models beginning with the traditional GARCH based approach, along with a number of novel point process models. Univariate models where IV is included as an exogenous variable are considered along with a novel bivariate approach where extreme movements in IV are treated as another point process. It is found that in the context of forecasting Value-at-Risk, the bivariate models produce the most accurate forecasts across a wide range of scenarios.