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on Forecasting |
| By: | Helena Veiga |
| Abstract: | This paper compares empirically the forecasting performance of a continuous time stochastic volatility model with two volatility factors (SV2F) to a set of alternative models (GARCH, FIGARCH, HYGARCH, FIEGARCH and Component GARCH). We use two loss functions and two out-of-sample periods in the forecasting evaluation. The two out-of-sample periods are characterized by different patterns of volatility. The volatility is rather low and constant over the first period but shows a significant increase over the second out-of-sample period. The empirical results evidence that the performance of the alternative models depends on the characteristics of the out-ofsample periods and on the forecasting horizons. Contrarily, the SV2F forecasting performance seems to be unaffected by these two facts, since the model provides the most accurate volatility forecasts according to the loss functions we consider. |
| Date: | 2006–04 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws062509&r=for |
| By: | Eric Ghysels; Jonathan H. Wright |
| Abstract: | Surveys of forecasters, containing respondents' predictions of future values of growth, inflation and other key macroeconomic variables, receive a lot of attention in the financial press, from investors, and from policy makers. They are apparently widely perceived to provide useful information about agents' expectations. Nonetheless, these survey forecasts suffer from the crucial disadvantage that they are often quite stale, as they are released only infrequently, such as on a quarterly basis. In this paper, we propose methods for using asset price data to construct daily forecasts of upcoming survey releases, which we can then evaluate. Our methods allow us to estimate what professional forecasters would predict if they were asked to make a forecast each day, making it possible to measure the effects of events and news announcements on expectations. We apply these methods to forecasts for several macroeconomic variables from both the Survey of Professional Forecasters and Consensus Forecasts. |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2006-10&r=for |
| By: | Maximilian Auffhammer (University of California, Berkeley) |
| Abstract: | The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and gas prices as well as natural gas consumption, GDP and energy intensity. |
| Keywords: | Forecasting, Asymmetric Loss, Energy Intensity, Energy Information Administration, |
| Date: | 2005–12–16 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:agrebk:1009&r=for |
| By: | Andrew Bauer; Robert A. Eisenbeis; Daniel F. Waggoner; Tao Zha |
| Abstract: | In 1994, the Federal Open Market Committee (FOMC) began to release statements after each meeting. This paper investigates whether the public’s views about the current path of the economy and of future policy have been affected by changes in the Federal Reserve’s communications policy as reflected in private sector’s forecasts of future economic conditions and policy moves. In particular, has the ability of private agents to predict where the economy is going improved since 1994? If so, on which dimensions has the ability to forecast improved? We find evidence that the individuals’ forecasts have been more synchronized since 1994, implying the possible effects of the FOMC’s transparency. On the other hand, we find little evidence that the common forecast errors, which are the driving force of overall forecast errors, have become smaller since 1994. |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedawp:2006-03&r=for |
| By: | Marco Castellani; Emanuel Santos |
| Abstract: | This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets. |
| Keywords: | interest rates; forecasting; neural networks; fuzzy logic. |
| URL: | http://d.repec.org/n?u=RePEc:ise:isegwp:wp42006&r=for |
| By: | Assenmacher-Wesche, Katrin; Gerlach, Stefan |
| Abstract: | Several authors have recently interpreted the ECB's two-pillar framework as separate approaches to forecast and analyse inflation at different time horizons or frequency bands. The ECB has publicly supported this understanding of the framework. This paper presents further evidence on the behaviour of euro area inflation using band spectrum regressions, which allow for a natural definition of the short and long run in terms of specific frequency bands, and causality tests in the frequency domain. The main finding is that variations in inflation are well explained by low-frequency movements of money and real income growth and high-frequency fluctuations of the output gap. |
| Keywords: | frequency domain; inflation; money growth; quantity theory; spectral regression |
| JEL: | C22 E3 E5 |
| Date: | 2006–04 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:5632&r=for |
| By: | Jonathan H. Wright |
| Abstract: | The slope of the Treasury yield curve has often been cited as a leading economic indicator, with inversion of the curve being thought of as a harbinger of a recession. In this paper, I consider a number of probit models using the yield curve to forecast recessions. Models that use both the level of the federal funds rate and the term spread give better in-sample fit, and better out-of-sample predictive performance, than models with the term spread alone. There is some evidence that controlling for a term premium proxy as well may also help. I discuss the implications of the current shape of the yield curve in the light of these results, and report results of some tests for structural stability and an evaluation of out-of-sample predictive performance. |
| Keywords: | Economic indicators ; Economic forecasting ; Interest rates |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2006-07&r=for |
| By: | Erik Hjalmarsson |
| Abstract: | Using Monte Carlo simulations, I show that typical out-of-sample forecast exercises for stock returns are unlikely to produce any evidence of predictability, even when there is in fact predictability and the correct model is estimated. |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:855&r=for |
| By: | Hui Guo; Robert Savickas |
| Abstract: | Finance theory, e.g., Campbell's (1993) ICAPM, indicates that the expected equity premium is a linear function of stock market volatility and the volatility of shocks to investment opportunities. We show that one can use average CAPM-based idiosyncratic volatility as a proxy for the latter. In particular, over the period 1927:Q1 to 2005:Q4, stock market volatility and idiosyncratic volatility jointly forecast stock market returns both in sample and out of sample. This finding is robust to alternative measures of idiosyncratic volatility; subsamples; the log transformation of volatility measures; and control for various predictive variables commonly used by early authors. Our results suggest that stock market returns are predictable. |
| Keywords: | Stock exchanges ; Stock - Prices |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2006-019&r=for |
| By: | Erik Hjalmarsson |
| Abstract: | I develop new asymptotic results for long-horizon regressions with overlapping observations. I show that rather than using auto-correlation robust standard errors, the standard t-statistic can simply be divided by the square root of the forecasting horizon to correct for the effects of the overlap in the data. Further, when the regressors are persistent and endogenous, the long-run OLS estimator suffers from the same problems as does the short-run OLS estimator, and similar corrections and test procedures as those proposed for the short-run case should also be used in the long-run. In addition, I show that under an alternative of predictability, long-horizon estimators have a slower rate of convergence than short-run estimators and their limiting distributions are non-standard and fundamentally different from those under the null hypothesis. These asymptotic results are supported by simulation evidence and suggest that under standard econometric specifications, short-run inference is generally preferable to long-run inference. The theoretical results are illustrated with an application to long-run stock-return predictability. |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:853&r=for |