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on Econometric Time Series |
| By: | Laurent A.F. Callot (School of Economics and Management, Aarhus University and CREATES) |
| Abstract: | This paper proposes a sequential procedure to determine the common cointegration rank of panels of cointegrated VARs. It shows how a panel of cointegrated VARs can be transformed in a set of independent individual models. The likelihood function of the transformed panel is the sum of the likelihood functions of the individual Cointegrated VARs (CVAR) models. A bootstrap based procedure is used to compute empirical distributions of the trace test statistics for these individual models. From these empirical distributions two panel trace test statistics are constructed. The satisfying small sample properties of these tests are documented by means of Monte Carlo. An empirical application illustrates the usefullness of this tests. |
| Keywords: | Rank test, Panel data, Cointegration, Bootstrap, Cross section dependence. |
| JEL: | C12 C32 C33 |
| Date: | 2010–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2010-75&r=ets |
| By: | Peter R. Hansen (Stanford University, Department of Economics and CREATES); Asger Lunde (Aarhus University, School of Economics and Management and CREATES); Valeri Voev (Aarhus University, School of Economics and Management and CREATES) |
| Abstract: | We introduce a multivariate GARCH model that utilizes and models realized measures of volatility and covolatility. The realized measures extract information contained in high-frequency data that is particularly beneficial during periods with variation in volatility and covolatility. Applying the model to market returns in conjunction with an individual asset yields a model for the conditional regression coefficient, known as the beta. We apply the model to a set of highly liquid stocks and find that conditional betas are much more variable than usually observed with rolling-window OLS regressions with dailty data. In the empirical part of the paper we examine the cross-sectional as well as the time variation of the conditional beta series. The model links the conditional and realized second moment measures in a self-contained system of equations, making it amenable to extensions and easy to estimate. A multi-factor extension of the model is briefly discussed. |
| Keywords: | Financial Volatility, Beta, Realized GARCH, High Frequency Data. |
| JEL: | G11 |
| Date: | 2010–11–29 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2010-74&r=ets |
| By: | Massimiliano Caporin (Department of Economic Sciences, University of Padova); Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University) |
| Abstract: | DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing multiple thresholds and time-dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset-specific shocks and common innovations by partitioning the multivariate density support. This paper presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasi- maximum likelihood estimators. The paper also presents an empirical example to highlight the usefulness of the new model. |
| Keywords: | Multivariate asymmetry, conditional variance, stationarity conditions, asymptotic theory, multivariate news impact curve. |
| JEL: | C32 C51 C52 |
| Date: | 2010–11 |
| URL: | http://d.repec.org/n?u=RePEc:kyo:wpaper:741&r=ets |
| By: | Eklund, Jana (Bank of England); Kapetanios, George (Queen Mary College, London); Price, Simon (Bank of England) |
| Abstract: | We examine how to forecast after a recent break. We consider monitoring for change and then combining forecasts from models that do and do not use data before the change; and robust methods, namely rolling regressions, forecast averaging over different windows and exponentially weighted moving average (EWMA) forecasting. We derive analytical results for the performance of the robust methods relative to a full-sample recursive benchmark. For a location model subject to stochastic breaks the relative mean square forecast error ranking is EWMA < rolling regression < forecast averaging. No clear ranking emerges under deterministic breaks. In Monte Carlo experiments forecast averaging improves performance in many cases with little penalty where there are small or infrequent changes. Similar results emerge when we examine a large number of UK and US macroeconomic series. |
| Keywords: | monitoring; recent structural change; forecast combination; robust forecasts |
| JEL: | C10 C59 |
| Date: | 2010–12–02 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0406&r=ets |