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on Econometric Time Series |
| By: | Maria M. De Mello (CEF.UP, Faculdade de Economia, Universidade do Porto) |
| Abstract: | This paper assesses the forecast performance of a set of VAR models under a growing number of restrictions. With a maximum forecast horizon of 12 years, we show that the farther the horizon is, the more structured and restricted VAR models have to be to produce accurate forecasts. Indeed, unrestricted VAR models, not subjected to integration or cointegration, are poor forecasters for both short and long run horizons. Differenced VAR models, subject to integration, are reliable predictors for one-step horizons but ineffectual for multi-step horizons. Cointegrated VAR models including appropriate structural breaks and exogenous variables, as well as being subjected to over-identifying theory consistent restrictions, are excellent forecasters for both short and long run horizons. Hence, to obtain precise forecasts from VAR models, proper specification and cointegration are crucial for whatever horizons are at stake, while integration is relevant only for short run horizons. |
| Keywords: | VAR demand systems; structural breaks, exogenous regressors, integration; cointegration; forecast accuracy. |
| JEL: | C32 C53 |
| Date: | 2009–10 |
| URL: | http://d.repec.org/n?u=RePEc:por:cetedp:0902&r=ets |
| By: | Daisuke Nagakura; Toshiaki Watanabe |
| Abstract: | Abstract We call the realized variance (RV) calculated with observed prices contaminated by (market) microstructure noises (MNs) the noise-contaminated RV (NCRV), referring to the bias component in the NCRV associated with the MNs as the MN component. This paper develops a state space method for estimating the integrated variance (IV) and MN component. We represent the NCRV by a state space form and show that the state space form parameters are not identifiable, however, they can be expressed as functions of identifiable parameters. We illustrate how to estimate these parameters. The proposed method also serves as a convenient way for estimating a general class of continuous-time stochastic volatility (SV) models under the existence of MN. We apply the proposed method to yen/dollar exchange rate data, where we find that most of the variation in NCRV is of the MN component. |
| Keywords: | Realized Variance, Integrated Variance, Microstructure Noise, State Space, Identification, Exchange Rate |
| Date: | 2010–02 |
| URL: | http://d.repec.org/n?u=RePEc:hst:ghsdps:gd09-115&r=ets |
| By: | Croux, Christophe; Gelper, Sarah; Mahieu, Koen |
| Abstract: | Multivariate time series may contain outliers of different types. In presence of such outliers, applying standard multivariate time series techniques becomes unreliable. A robust version of multivariate exponential smoothing is proposed. The method is affine equivariant, and involves the selection of a smoothing parameter matrix by minimizing a robust loss function. It is shown that the robust method results in much better forecasts than the classic approach in presence of outliers, and performs similar when the data contain no outliers. Moreover, the robust procedure yields an estimator of the smoothing parameter less subject to downward bias. As a byproduct, a cleaned version of the time series is obtained, as is illustrated by means of a real data example. |
| Keywords: | Data cleaning; Exponential smoothing; Forecasting; Multivariate time series; Robustness; |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:ner:leuven:urn:hdl:123456789/242199&r=ets |
| By: | Anindya Banerjee; Massimiliano Marcellino; Igor Masten |
| Abstract: | As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor- augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latters speci cation in dfferences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simula- tions and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets. |
| Keywords: | Forecasting with Factor-augmented Error Correction |
| JEL: | C32 E17 |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:bir:birmec:09-06r&r=ets |
| By: | Ruipeng Liu; Thomas Lux |
| Abstract: | Long memory (long-term dependence) of volatility counts as one of the ubiquitous stylized facts of financial data. Inspired by the long memory property, multifractal processes have recently been introduced as a new tool for modeling financial time series. In this paper, we propose a parsimonious version of a bivariate multifractal model and estimate its parameters via both maximum likelihood and simulation based inference approaches. In order to explore its practical performance, we apply the model for computing value-at-risk and expected shortfall statistics for various portfolios and compare the results with those from an alternative bivariate multifractal model proposed by Calvet et al. (2006) and the bivariate CC-GARCH of Bollerslev (1990). As it turns out, the multifractal models provide much more reliable results than CC-GARCH, and our new model compares well with the one of Calvet et al. although it has an even smaller number of parameters |
| Keywords: | Long memory, multifractal models, simulation based inference, value-at-risk, expected shortfall |
| JEL: | C11 C13 G15 |
| Date: | 2010–02 |
| URL: | http://d.repec.org/n?u=RePEc:kie:kieliw:1594&r=ets |