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on Market Microstructure |
| By: | Hautsch, Nikolaus; Hess, Dieter; Veredas, David |
| Abstract: | We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. Bid and ask returns are decomposed into a common ('efficient return') factor and two market-side-specific components capturing market microstructure effects. The corresponding variance components reflect information-driven and noise-induced volatilities.We find that all volatility components reveal distinct dynamics and are positively influenced by news. The proportion of noise-induced variances is highest before announcements and significantly declines thereafter. Moreover, news-affected responses in all volatility components are influenced by order flow imbalances. -- |
| Keywords: | effcient return,macroeconomic announcements,microstructure noise,informational volatility |
| JEL: | C32 G14 E44 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:1106&r=mst |
| By: | Manabu Asai (Faculty of Economics Soka University, Japan); Michael McAleer (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics) Erasmus Universiteit, Tinbergen Instituut (Tinbergen Institute).); Marcelo C. Medeiros (Department of Economics Pontifical Catholic University of Rio de Janeiro(PUC-Rio)) |
| Abstract: | Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even bias-corrected and consistent realized volatility (RV) estimates of IV can contain residual microstructure noise and other measurement errors. Such noise is called “realized volatility error”. As such errors are ignored, we need to take account of them in estimating and forecasting IV. This paper investigates through Monte Carlo simulations the effects of RV errors on estimating and forecasting IV with RV data. It is found that: (i) neglecting RV errors can lead to serious bias in estimators; (ii) the effects of RV errors on one-step ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large; and (iii) even the partially corrected 2R recently proposed in the literature should be fully correcte d for evaluating forecasts. This paper proposes a full correction of 2 R . An empirical example for S&P 500 data is used to demonstrate the techniques developed in the paper. |
| Keywords: | realized volatility; diffusion; financial econometrics; measurement errors; forecasting; model evaluation; goodness-of-fit. |
| JEL: | G32 G11 C53 C22 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1109&r=mst |
| By: | Michele Modugno (European Central Bank, DG-R/EMO, Kaiserstrasse 29, D-60311, Frankfurt am Main, Germany.) |
| Abstract: | This paper proposes a methodology to nowcast and forecast inflation using data with sampling frequency higher than monthly. The nowcasting literature has been focused on GDP, typically using monthly indicators in order to produce an accurate estimate for the current and next quarter. This paper exploits data with weekly and daily frequency in order to produce more accurate estimates of inflation for the current and followings months. In particular, this paper uses the Weekly Oil Bulletin Price Statistics for the euro area, the Weekly Retail Gasoline and Diesel Prices for the US and daily World Market Prices of Raw Materials. The data are modeled as a trading day frequency factor model with missing observations in a state space representation. For the estimation we adopt the methodology exposed in Banbura and Modugno (2010). In contrast to other existing approaches, the methodology used in this paper has the advantage of modeling all data within a unified single framework that, nevertheless, allows one to produce forecasts of all variables involved. This offers the advantage of disentangling a model-based measure of ”news” from each data release and subsequently to assess its impact on the forecast revision. The paper provides an illustrative example of this procedure. Overall, the results show that these data improve forecast accuracy over models that exploit data available only at monthly frequency for both countries. JEL Classification: C53, E31, E37. |
| Keywords: | Factor Models, Forecasting, Inflation, Mixed Frequencies. |
| Date: | 2011–04 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20111324&r=mst |