We consider stochastic volatility models for discrete financial time series of the nonlinear autoregressive-ARCH type with exogenous components.We discuss how the trend and volatility functions determining the process may be estimated nonparametrically by least-squares fitting of neural networks or, more generally, of functions from other parametric classes having a universal approximation property...
Keywords: ARCH process, backtesting, expected shortfall, GARCH process, market risk
02/2006 | Statistics & Decisions, Oldenbourg Wissenschaftsverlag