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on Econometric Time Series |
| By: | Guidolin, Massimo; Timmermann, Allan G |
| Abstract: | This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that accounting for both regimes in interest rate dynamics and combining forecasts from different models helps improve the out-of-sample forecasting performance for US short-term rates. Imposing restrictions from the expectations hypothesis on the forecasting model are found to help at long forecasting horizons. |
| Keywords: | forecast combinations; term structure of interest rates |
| JEL: | C53 G12 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:6188&r=ets |
| By: | Floden, Martin (Dept. of Economics, Stockholm School of Economics) |
| Abstract: | This note examines the accuracy of methods that are commonly used to approximate AR(1)-processes with discrete Markov chains. The quadrature-based method suggested by Tauchen and Hussey (1991) generates excellent approximations with a small number of nodes when the autocorrelation is low or modest. This method however has problems when the autocorrelation is high, as it typically is found to be in recent empirical studies of income processes. I suggest an alternative weighting function for the Tauchen-Hussey method, and I also note that the older method suggested by Tauchen (1986) is relatively robust to high autocorrelation. |
| Keywords: | numerical methods; income processes; autoregressive process |
| JEL: | C60 |
| Date: | 2007–03–12 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:hastef:0656&r=ets |
| By: | Torben G. Andersen; Tim Bollerslev; Dobrislav Dobrev |
| Abstract: | We develop a sequential procedure to test the adequacy of jump-diffusion models for return distributions. We rely on intraday data and nonparametric volatility measures, along with a new jump detection technique and appropriate conditional moment tests, for assessing the import of jumps and leverage effects. A novel robust-to-jumps approach is utilized to alleviate microstructure frictions for realized volatility estimation. Size and power of the procedure are explored through Monte Carlo methods. Our empirical findings support the jump-diffusive representation for S&P500 futures returns but reveal it is critical to account for leverage effects and jumps to maintain the underlying semi-martingale assumption. |
| JEL: | C15 C22 C52 C80 G10 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:12963&r=ets |
| By: | Fabio C. Bagliano; Claudio Morana |
| Abstract: | What are the sources of macroeconomic comovement among G-7 countries? Two main candidate explanations may be singled out: common shocks and common transmission mechanisms. In the paper it is shown that they are complementary, rather than alternative, explanations. By means of a large-scale factor vector autoregressive (FVAR) model, allowing for full economic and statistical identification of all global and idiosyncratic shocks, it is found that both common disturbances and common transmission mechanisms of global and country-specific shocks account for business cycle comovement in the G-7 countries. Moreover, spillover effects of foreign idiosyncratic disturbances seem to be a less important factor than the common transmission of global or domestic shocks in the determination of international macroeconomic comovements. |
| Keywords: | business cycle comovement, factor vector autoregressive model, transmission mechanisms. |
| JEL: | C32 E32 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:cca:wpaper:40&r=ets |
| By: | Richard T. Baillie (Michigan State University and Queen Mary, University of London); Claudio Morana (Michigan State University, Università del Piemonte Orientale and ICER) |
| Abstract: | This paper introduces a new long memory volatility process, denoted by Adaptive <i>FIGARCH</i>, or <i>A-FIGARCH</i>, which is designed to account for both long memory and structural change in the conditional variance process. Structural change is modeled by allowing the intercept to follow a slowly varying function, specified by Gallant (1984)'s flexible functional form. A Monte Carlo study finds that the <i>A-FIGARCH</i> model outperforms the standard <i>FIGARCH</i> model when structural change is present, and performs at least as well in the absence of structural instability. An empirical application to stock market volatility is also included to illustrate the usefulness of the technique. |
| Keywords: | <i>FIGARCH</i>, Long memory, Structural change, Stock market volatility |
| JEL: | C15 C22 F31 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:wp593&r=ets |
| By: | Matthias Fischer |
| Abstract: | A new test for constant correlation is proposed. Based on the bivariate Student-t distribution, this test is derived as Lagrange multiplier (LM) test. Whereas most of the traditional tests (e.g. Jennrich, 1970, Tang, 1995 and Goetzmann, Li & Rouwenhorst, 2005) specify the unknown correlations as piecewise constant, our model-setup for the correlation coefficient is based on trigonometric functions. Applying this test to assets from different financial markets (stocks, exchange rates, metals) there is empirical evidence that many of the correlations vary over time. |
| Keywords: | Lagrange multiplier test, constant correlation, trigonometric functions. |
| JEL: | C22 C32 G12 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2007-012&r=ets |
| By: | Weron, Rafal; Misiorek, Adam |
| Abstract: | This paper is a continuation of our earlier studies on short-term price forecasting of California electricity prices with time series models. Here we focus on whether models with heavy-tailed innovations perform better in terms of forecasting accuracy than their Gaussian counterparts. Consequently, we limit the range of analyzed models to autoregressive time series approaches that have been found to perform well for pre-crash California power market data. We expand them by allowing for heavy-tailed innovations in the form of α-stable or generalized hyperbolic noise. |
| Keywords: | Electricity; price forecasting; heavy tails; time series; α-stable distribution; generalized hyperbolic distribution |
| JEL: | C53 C46 C22 Q40 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:2292&r=ets |