In this paper we suggest a framework to assess the degree of reliability of provisional estimates as forecasts of final data, and we reexamine the question of the most appropriate way in which available data should be used for ex ante forecasting in the presence of a data revision process....

In this paper we address the issue of forecasting Value--at--Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two-scales realized volatility, realized kernel, as well as the daily range. We propose a dynamic model with a flexible trend...

We analyze the properties of multiperiod forecasts which are formulated by a number of companies for a fixed horizon ahead which moves each month one period closer and are collected and diffused each month by some polling agency. Some descriptive evidence and a formal model suggest that knowing...

When observed over a large panel, measures of risk (such as realized volatilities) usually exhibit a secular trend around which individual risks cluster. In this article we propose a vector Multiplicative Error Model achieving a decomposition of each risk measure into a common systematic and an...

In this paper we suggest a convenient way to obtain parameter estimates of a discrete state hidden Markov volatility process within a framework consistent with observed option prices and stochastic volatility. Relative to similar proposals, we simplify the model estimation by resorting to some...

Long memory in conditional variance is one of the empirical features of most financial time series. One class of models that was suggested to capture this behavior refers to the so-called Fractionally Integrated GARCH processes (Baillie, Bollerslev and Mikkelsen 1996) in which the ideas of...

Financial market price formation and exchange activity can be investigated by means of ultra-high frequency data. In this paper we investigate an extension of the Autoregressive Conditional Duration (ACD) model of Engle and Russell (1998) by adopting a mixture of distribution approach with time...

A new method, called relevant transformation of the inputs network approach (RETINA) is proposed as a tool for model building and selection. It is designed to improve on some of the shortcomings of neural networks. RETINA has the flexibility of neural network models, the concavity of the...

In this paper, we examine the characteristics of market opening news and its impact on the estimated coefficients of the conditional volatility models of the GARCH class. We find that the differences between the opening price of one day and the closing price of the day before have different...

The instantaneous volatility of the price process is analyzed through the intraday financial durations between price changes. Previous research has traditionally dealt with parametric models without reaching a satisfactory level of adequacy. In this study, it is shown that by using a mixture of...