Title: Bootstrap based inference and probability forecasting in multiplicative error models
Authors: Indeewara Perera - University of Sheffield (United Kingdom) [presenting]
Mervyn Silvapulle - Monash University (Australia)
Abstract: As evidenced by an extensive empirical literature, multiplicative error models (MEM) show good performance in capturing the stylized facts of nonnegative time series; examples include, trading volume, financial durations, and volatility. A bootstrap-based method is developed for producing multi-step-ahead probability forecasts for a nonnegative valued time-series obeying a parametric MEM. To test the adequacy of the underlying parametric model, a class of bootstrap specification tests is also developed. Rigorous proofs are provided for establishing the validity of the proposed bootstrap methods. The paper also establishes the validity of a bootstrap based method for producing probability forecasts in a class of semiparametric MEMs. Monte Carlo simulations suggest that our methods perform well in finite samples. A real data example involving realized volatility of the S\&P 500 index illustrates the methods.