Suchergebnisse

We study the impact of volatility on intraday serial correlation, at time scales of less than 20 minutes, exploiting a data set with all transaction on SPX500 futures from 1993 to 2001. We show that, while realized volatility and intraday serial correlation are linked, this relation is driven by unexpected volatility only, that is by the fraction of volatility which cannot be forecasted. The impact of predictable volatility is instead found to be negative (LeBaron effect). Our results are robust to microstructure noise, and they confirm the leading economic theories on price formation.

In this paper we discuss the estimation of the diffusion coefficient in one‐factor models for the short rate via non‐parametric methods. We test the estimators proposed by Ait‐Sahalia (1996), Stanton (1997) and Bandi and Phillips (2003) on Monte Carlo simulations of the Vasicek and CIR model. We show that the Ait‐Sahalia estimator is not applicable for values of the mean reversion coefficient typically displayed by interest rate data, while the Stanton and Bandi–Phillips estimators perform better. Each of the three estimators depends crucially on the choice of the bandwidth parameter. Our analysis shows that the estimators give different results for both the data set analysed by Ait‐Sahalia (1996) and by Stanton (1997). Finally we show that the data sets used by Ait‐Sahalia and Stanton are inherently different and, in particular, that very short‐term data exhibit characteristics which are inconsistent with a diffusion.