This paper proposes a new way of modeling and forecasting intraday returns. We decompose the volatility of high frequency asset returns into components that may be easily interpreted and estimated. The conditional variance is expressed as a product of daily, diurnal and stochastic intraday...

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...

We develop an estimated time-series model of revisions of U.S. payroll employment in order to obtain more accurate filtered estimates of the "true" or underlying condition of U.S. employment. Our estimates of "true" employment are filtered, according to an estimated signal-plus-noise (S+N)...

The empirical evidence from financial markets suggests that the pattern of response of market volatility to shocks is highly dependent on the magnitude of shocks themselves. Markov-Switching GARCH (MS-GARCH) models are a valuable tool for modelling state dependence in the dynamics of the...

This paper is a step towards the econometric foundation of computational intelligence in finance. Financial time series modeling and forecasting are addressed with an artificial neural network, examining issues of its topology dependency. Structural dependency of results is viewed not as a...

The Efficient Market Hypothesis (EMH) states that the current market price fully reflects all available information. The weak form of the EMH considers only past price data and rules out predictions based on the price data only. The prices follow a random walk, where successive changes have zero...

This paper describes and analyses the use of the Filtered Historical Simulation algorithm in pricing spread options. Spread options are contracts whose payoff depends on the price difference (spread) between two or more underlying assets at a future date. Such kind of options are written in the...

The main problem in the combination of volatility forecasts is that the volatility cannot be directly observed and hence loss functions such as the MSFE cannot be directly used unless a suitable proxy of the conditional variance is defined. A common approach is to use the squared returns but...