We present a novel GARCH model that accounts for time varying, state dependent, persistence in the volatility dynamics. The proposed model generalizes the component GARCH model of Ding and Granger (1996). The volatility is modelled as a convex combination of unobserved GARCH components where the...

We evaluate the performance of several specification tests for Markov regime-switching time series models. We consider the Lagrange Multiplier and dynamic specification tests of Hamilton (1994) and Ljung-Box tests based on both the generalized residual and a standard-normal residual constructed...

Driven by the rise in computational power, it has become popular to measure integrated variance with high-frequency squared returns. Though the squared return is a natural choice as a variance estimate, it is not the most efficient one for a given interval length. Extreme-value based estimators...

Whether Republican or Democratic presidents are better for the the stock market has been closely scrutinized for years. Although much of it is discussed only in casual terms, a recent academic study by Santa Clara and Valkanov (2003) documenting that the market does significantly better under...

Recent research has generated support to the notion that the real exchange rate adjustment is nonlinear and that the PPP half-life is faster than the puzzling 3 to 5 years based on linear models. While different nonlinear models survive the specification tests against linear ones, there is...

A least squares estimation approach for the estimation of a GARCH (1,1) model is developed. The asymptotic properties of the estimator are studied given mild regularity conditions, which require only that the error term has a conditional moment of some order. We establish the consistency,...

Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model...

The use of non-linear models to accurately estimate the Value at Risk (VaR) of financial portfolios is increasing. Specifically, the use of Autoregressive Conditional Heteroscedasticity (ARCH) models, which can forecast the time-varying volatility of a financial asset. In this research paper...

We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated...

Modelling of the financial variable evolution represents an important issue in financial econometrics. Stochastic dynamic models allow to describe more accurately many features of the financial variables, but often there exists a trade-off between the modelling accuracy and the complexity....