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on Econometric Time Series |
| By: | Guglielmo Maria Caporale; Luis A. Gil-Alana |
| Abstract: | This paper examines several US monthly financial time series data using fractional integration and cointegration techniques. The univariate analysis based on fractional integration aims to determine whether the series are I(1) (in which case markets might be efficient) or alternatively I(d) with d < 1, which implies mean reversion. The multivariate framework exploiting recent developments in fractional cointegration allows to investigate in greater depth the relationships between financial series. We show that there exist many (fractionally) cointegrated bivariate relationships among the variables examined. |
| Keywords: | Fractional integration, long-range dependence, fractional cointegration, financial data |
| JEL: | C22 G10 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1116&r=ets |
| By: | Hallin, M.; Akker, R. van den; Werker, B.J.M. (Tilburg University, Center for Economic Research) |
| Abstract: | We propose a class of distribution-free rank-based tests for the null hypothesis of a unit root. This class is indexed by the choice of a reference density g, which needs not coincide with the unknown actual innovation density f. The validity of these tests, in terms of exact finite sample size, is guaranteed, irrespective of the actual underlying density, by distribution-freeness. Those tests are locally and asymptotically optimal under a particular asymptotic scheme, for which we provide a complete analysis of asymptotic relative efficiencies. Rather than asymptotic optimality, however, we emphasize finitesample performances. Finite-sample performances of unit root tests, however, depend quite heavily on initial values. We therefore investigate those performances as a function of initial values. It appears that our rank-based tests significantly outperform the traditional Dickey-Fuller tests, as well as the more recent procedures proposed by Elliot, Rothenberg, and Stock (1996), Ng and Perron (2001), and Elliott and M¨uller (2006), for a broad range of initial values and for heavy-tailed innovation densities. As such, they provide a useful complement to existing techniques. |
| Keywords: | Unit root;Dickey-Fuller test;Local Asymptotic Normality;Rank test. |
| JEL: | C12 C22 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:kubcen:2011002&r=ets |
| By: | Regnard, Nazim; Zakoïan, Jean-Michel |
| Abstract: | This paper examines the relationship between gas spot prices at the Zeebrugge market, one-month ahead Brent prices and temperatures over the period 2000–2005. A cointegration analysis is carried out and it is discovered that a cointegration relationship exists between the three series. To take into account the influence of temperature on the gas volatility, a GARCH(1,1) model with temperature-dependent coefficients is considered. Stability and estimation properties are discussed. An empirical finding is the existence of distinct volatility regimes for the volatility of gas prices, depending on the temperature level. |
| Keywords: | Gas Prices; Nonstationary Models; GARCH; Periodic models; Time-Varying Coefficients; Quasi-Maximum Likelihood Estimation; |
| JEL: | C73 G13 C13 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:ner:dauphi:urn:hdl:123456789/2603&r=ets |
| By: | John Cotter |
| Abstract: | Accurate volatility modelling is paramount for optimal risk management practices. One stylized feature of financial volatility that impacts the modelling process is long memory explored in this paper for alternative risk measures, observed absolute and squared returns for high frequency intraday UK futures. Volatility series for three different asset types, using stock index, interest rate and bond futures are analysed. Long memory is strongest for the bond contract. Long memory is always strongest for the absolute returns series and at a power transformation of k < 1. The long memory findings generally incorporate intraday periodicity. The APARCH model incorporating seven related GARCH processes generally models the futures series adequately documenting ARCH, GARCH and leverage effects. |
| Date: | 2011–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1103.5651&r=ets |
| By: | Iqbal, Javed |
| Abstract: | It is well established that regression analysis on non-stationary time series data may yield spurious results. An earlier response to this problem was to run regression with first difference of variables. But this transformation destroys any long-run information embodied in the levels of variables. According to ‘Granger Representation Theorem’ (Engle and Granger, 1987) if variables are co-integrated, there exist an error correction mechanism which incorporates long run information in modeling changes in variables. This mechanism employs an additional lag value of the disequilibrium error as an additional variable in modeling changes in variables. It has been argued that ECM performs better for long run forecast than a simple first difference or level regression. This process contributes to the literature in two important ways. Firstly empirical evidence does not exist on the relative merits of ECM arrived at using alternative co-integration techniques. The three popular co-integration procedures considered are the Engle-Granger (1987) two step procedure, the Johansen (1988) multivariate system based technique and the recently developed Auto regressive Distributed Lag based technique of Pesaran et al. (1996, 2001). Secondly, earlier studies on the forecasting performance of the ECM employed macroeconomic data on developed economies i.e. the US and the UK. By employing data form the Asian countries and using absolute version of the purchasing power parity and money demand function this paper compares forecast accuracy of the three alternative error correction models in forecasting the nominal exchange rate and monetary aggregate (M2). |
| Keywords: | Co-integration; Error Correction Models; Forecasting |
| JEL: | C32 C53 |
| Date: | 2011–03–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:29826&r=ets |