Flavio Ziegelmann (UFRGS, Brazil)

Title: Factor Functional Time Series Models for Financial Risk Forecasting

In modern days, the accurate prediction and forecasting of risk measures, such as Value at Risk and Expected Shortfall, is an essential task for asset market managers. When calculating risk measures, an essential step, for most approaches, is to estimate the probability density function of asset returns. A daily sequence of intraday return densities of p assets can be seen as a p-dimensional functional time series. If p is large, then one has to perform a two-way dimension reduction: in the high dimensional vector and in the infinite dimensional curves. Here we propose combining a Functional Factor Model with a univariate Dynamic Functional Principal Components Analysis as a two way dimension reduction approach, which feeds the error term of a high-frequency ARMA-GARCH model aiming to forecast future daily risk measures.