Recent empirical evidence suggests that the weekend and holiday calendar effects are much stronger and statistically significant in volatility as opposed to expected returns. This paper seeks an explanation for this empirical finding by undertaking a comprehensive investigation of the predictive...

This paper examines the relation between dollar-real exchange rate volatility implied in option prices and subsequent realized volatility. It investigates whether implied volatilities contain information about volatility over the remaining life of the option which is not present in past returns....

This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors should be serially uncorrelated at the single period horizon with...

A common problem in out-of-sample prediction is that there are potentially many relevant predictors that individually have only weak explanatory power. We propose bootstrap aggregation of pre-test predictors (or bagging for short) as a means of constructing forecasts from multiple regression...

In real time forecasting, the sample is usually split into an estimation period of R observations and a prediction period of P observations, where T=R+P. Parameters are often estimated in a recursive manner, initially using R observations, then R+1 observations and so on until T-1 observations...

The possibility of confusing long memory behavior with structural changes need to specify what kind of long memory behavior is concerned in literature and applications. One attraction of long memory models is that they imply different long run predictions and effects of shocks to conventional...

Filtering techniques are often applied to the estimation of dynamic latent variable models. However, these techniques are often based on a set assumptions which restrict models to be specified in a linear state-space form. Numerical filtering techniques have been propsed that avoid invoking such...

In this paper, we propose a method of analyzing time series in the spatial domain. The analysis is based on the inference on the local time and its expectation. Both for the stationary and nonstationary time series, the spatial distributions are provided by the local time, and some of their...

A new family of kernels is suggested for use in heteroskedasticity and autocorrelation consistent (HAC) and long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or...