A0521
Title: Volatility prediction under misspecification
Authors: Genaro Sucarrat - BI Norwegian Business School (Norway) [presenting]
Abstract: The volatility models used in practice are unlikely to equal the data-generating process (DGP). Accordingly, models that are valid under misspecification are of great importance. Exact, general and mild conditions are established under which a large class of volatility prediction specifications exists. Crucially, the specifications within the class generate volatility predictions that are weakly identified for volatility under misspecification. Next, a consistent and asymptotically normal estimator that is valid under dependence of unknown form is derived. The volatility prediction specifications considered in more detail are modifications of the log-ARCH-X model. The specifications are highly interpretable and versatile and accommodate zero returns (in contrast to the classic log-ARCH specification), short-term and long-term persistence, asymmetry, volatility proxies and additional covariates. Since the volatility specifications are in logs, the inference is standard under the nullity of the parameters, and the positivity of the volatility predictions is guaranteed. In the simulation experiments, the predictions are both unbiased and identified for the benchmark model, whereas in the empirical illustration, the volatility predictions compare well with those of the benchmark volatility model.
