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on Econometric Time Series |
| By: | Tommaso Proietti (Faculty of Economics, University of Rome "Tor Vergata"); Alessandra Luati (University of Bologna) |
| Abstract: | The paper focuses on the adaptation of local polynomial filters at the end of the sample period. We show that for real time estimation of signals (i.e. exactly at the boundary of the time support) we cannot rely on the automatic adaptation of the local polynomial smoothers, since the direct real time filter turns out to be strongly localised, and thereby yields extremely volatile estimates. As an alternative we evaluate a general family of asymmetric filters that minimises the mean square revision error subject to polynomial reproduction constraints; in the case of the Henderson filter it nests the well known Musgrave’s surrogate filters. The class of filters depends on unknown features of the series such as the slope and the curvature of the underlying signal, which can be estimated from the data. Several empirical examples illustrate the effectiveness of our proposal. We also discuss the merits of using a nearest neighbour bandwidth as opposed to a fixed bandwidth for improving the quality of the approximation. |
| Keywords: | Henderson filter. Trend estimation. Nearest Neighbour Bandwidth. Musgrave asymmetric filters |
| Date: | 2008–07–14 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:112&r=ets |
| By: | Gianluca Cubadda (Dipartimento SEFEMEQ - Università di Roma "Tor Vergata"); Alain Hecq (University of Maastricht); Franz C. Palm (University of Maastricht) |
| Abstract: | For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA hereafter), we show that the presence of common cyclical features or cointegration leads to a reduction of the order of the implied univariate autoregressive-moving average (ARIMA hereafter) models. This finding can explain why we identify parsimonious univariate ARIMA models in applied research although VAR models of typical order and dimension used in macroeconometrics imply nonparsimonious univariate ARIMA representations. Next, we develop a strategy for studying interactions between variables prior to possibly modelling them in a multivariate setting. Indeed, the similarity of the autoregressive roots will be informative about the presence of co-movements in a set of multiple time series. Our results justify both the use of a panel setup with homogeneous autoregression and heterogeneous cross-correlated vector moving average errors and a factor structure, and the use of cross-sectional aggregates of ARIMA series to estimate the homogeneous autoregression. |
| Keywords: | Interactions, multiple time series, co-movements, ARIMA, cointegration, common cycles, dynamic panel data. |
| JEL: | C32 |
| Date: | 2008–07–14 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:125&r=ets |
| By: | Ole E. Barndorff-Nielsen; Peter Reinhard Hansen; Asger Lunde; Neil Shephard |
| Abstract: | We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used returns measured over 5 or 10 minutes intervals. We show the new estimator is substantially more precise. |
| Keywords: | HAC estimator, Long run variance estimator; Market frictions; Quadratic variation; Realised variance |
| JEL: | C01 C14 C32 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:sbs:wpsefe:2008fe29&r=ets |
| By: | Enzo Weber |
| Abstract: | Information flows across international financial markets typically occur within hours, making volatility spillover appear contemporaneous in daily data. Such simultaneous transmission of variances is featured by the stochastic volatility model developed in this paper, in contrast to usually employed multivariate ARCH processes. The identification problem is solved by considering heteroscedasticity of the structural volatility innovations, and estimation takes place in an appropriately specied state space setup. In the empirical application, unidirectional volatility spillovers from the US stock market to three American countries are revealed. The impact is strongest for Canada, followed by Mexico and Brazil, which are subject to idiosyncratic crisis effects. |
| Keywords: | Stochastic Volatility, Identification, Variance Transmission |
| JEL: | C32 G15 |
| Date: | 2008–07 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2008-049&r=ets |