Leonardo Rocha Souza

Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes: a Monte-Carlo study

For a fractionally integrated ARFIMA(p,d,q) model, temporal aggregation changes the order of the process to an ARFIMA(p,d,infinity), while leaving the value of d unchanged. This paper analyses the effects of temporal aggregation on the estimated long memory parameter, d, using both semi-parametric and parametric estimation methods. We find that if, for the non-aggregated series, the bias in the fractional parameter is large due to the influence of short run AR and MA parameters, temporal aggregation can reduce this bias. We compare aggregated forecasts from the underlying (non-aggregated) series with forecasts from the aggregated series and find that for d 0, the forecast comparison results are less clear-cut

Bias in the memory parameter for different sampling rates

Abstract This paper analyses the bias in the estimate of the long memory parameter arising as a consequence of changing the frequency of the data for both semi-parametric and parametric estimation methods. Decreasing the sampling rate biases the estimation of the long memory parameter towards zero for all estimation methods. For the semi-parametric methods the observed empirical bias can be explained by analysing the form of each of the estimation methods.

Using irregularly spaced returns to estimate multi-factor models: application to Brazilian equity data

Abstract Multi-factor models are useful tools to explain cross-sectional covariance in equities returns. In this paper a new estimation method is proposed that makes use of irregularly spaced returns and an empirical example is provided with the 389 most liquid equities in the Brazilian Market. The market index shows itself capable of explaining equity returns while the US

Forecasting electricity demand using generalized long memory

/Brazilian real exchange rate and the Brazilian short interest rate do not. The example shows the usefulness of the estimation method in further using the model to fill in missing values and to provide interval forecasts.