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Forecast Horizons (forecast + horizon)
Selected AbstractsSHOULD OIL PRICES RECEIVE SO MUCH ATTENTION?ECONOMIC INQUIRY, Issue 4 2008AN EVALUATION OF THE PREDICTIVE POWER OF OIL PRICES FOR THE U.S. ECONOMY This paper evaluates the potential gains from using oil prices to forecast a variety of measures of inflation, economic activity, and monetary policy,related variables. With a few exceptions, oil prices do not have any predictive content for these variables. This finding is robust to the use of rolling forecast windows, the use of industry-level data, changes in the forecast horizon, and allowing for nonlinearities. (JEL Q43, E37, C32) [source] Automated generation of new knowledge to support managerial decision-making: case study in forecasting a stock marketEXPERT SYSTEMS, Issue 4 2004Se-Hak Chun Abstract: The deluge of data available to managers underscores the need to develop intelligent systems to generate new knowledge. Such tools are available in the form of learning systems from artificial intelligence. This paper explores how the novel tools can support decision-making in the ubiquitous managerial task of forecasting. For concreteness, the methodology is examined in the context of predicting a financial index whose chaotic properties render the time series difficult to predict. The study investigates the circumstances under which enough new knowledge is extracted from temporal data to overturn the efficient markets hypothesis. The efficient markets hypothesis precludes the possibility of anticipating in financial markets. More precisely, the markets are deemed to be so efficient that the best forecast of a price level for the subsequent period is precisely the current price. Certain anomalies to the efficient market premise have been observed, such as calendar effects. Even so, forecasting techniques have been largely unable to outperform the random walk model which corresponds to the behavior of prices under the efficient markets hypothesis. This paper tests the validity of the efficient markets hypothesis by developing knowledge-based tools to forecast a market index. The predictions are examined across several horizons: single-period forecasts as well as multiple periods. For multiperiod forecasts, the predictive methodology takes two forms: a single jump from the current period to the end of the forecast horizon, and a multistage web of forecasts which progresses systematically from one period to the next. These models are first evaluated using neural networks and case-based reasoning, and are then compared against a random walk model. The computational models are examined in the context of forecasting a composite for the Korean stock market. [source] Use of Forecasts of Earnings to Estimate and Compare Cost of Capital Across RegimesJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 3-4 2006Article first published online: 19 MAY 200, Peter Easton Abstract: I critically examine several of the methods used in the recent literature to estimate and compare the cost of capital across different accounting/regulatory regimes. I focus on the central importance of expectations of growth beyond the short period for which forecasts of future pay-offs (dividends and/or earnings) are available. I illustrate, using the stocks that comprised the Dow Jones Industrial Average (DJIA) at December 31, 2004, as an example, the differences between the growth rates implied by the data, and growth rates that are often assumed in the literature. My analyses show that assumptions about growth beyond the (short) forecast horizon may seriously affect the estimates of the expected rate of return and may lead to spurious inferences. [source] Power transformation models and volatility forecastingJOURNAL OF FORECASTING, Issue 7 2008Perry Sadorsky Abstract This paper considers the forecast accuracy of a wide range of volatility models, with particular emphasis on the use of power transformations. Where one-period-ahead forecasts are considered, the power autoregressive models are ranked first by a range of error metrics. Over longer forecast horizons, however, generalized autoregressive conditional heteroscedasticity models are preferred. A value-at-risk-based forecast assessment indicates that, while the forecast errors are independent, they are not independent and identically distributed, although this latter result is sensitive to the choice of forecast horizon. Our results are robust across a number of different asset markets. Copyright © 2008 John Wiley & Sons, Ltd. [source] Selection of the relevant information set for predictive relationships analysis between time seriesJOURNAL OF FORECASTING, Issue 8 2002Umberto Triacca Abstract In time series analysis, a vector Y is often called causal for another vector X if the former helps to improve the k -step-ahead forecast of the latter. If this holds for k=1, vector Y is commonly called Granger-causal for X. It has been shown in several studies that the finding of causality between two (vectors of) variables is not robust to changes of the information set. In this paper, using the concept of Hilbert spaces, we derive a condition under which the predictive relationships between two vectors are invariant to the selection of a bivariate or trivariate framework. In more detail, we provide a condition under which the finding of causality (improved predictability at forecast horizon 1) respectively non-causality of Y for X is unaffected if the information set is either enlarged or reduced by the information in a third vector Z. This result has a practical usefulness since it provides a guidance to validate the choice of the bivariate system {X, Y} in place of {X, Y, Z}. In fact, to test the ,goodness' of {X, Y} we should test whether Z Granger cause X not requiring the joint analysis of all variables in {X, Y, Z}. Copyright © 2002 John Wiley & Sons, Ltd. [source] Option-Implied Risk Aversion EstimatesTHE JOURNAL OF FINANCE, Issue 1 2004Robert R. Bliss ABSTRACT Using a utility function to adjust the risk-neutral PDF embedded in cross sections of options, we obtain measures of the risk aversion implied in option prices. Using FTSE 100 and S&P 500 options, and both power and exponential-utility functions, we estimate the representative agent's relative risk aversion (RRA) at different horizons. The estimated coefficients of RRA are all reasonable. The RRA estimates are remarkably consistent across utility functions and across markets for given horizons. The degree of RRA declines broadly with the forecast horizon and is lower during periods of high market volatility. [source] Forecasting realized volatility: a Bayesian model-averaging approachJOURNAL OF APPLIED ECONOMETRICS, Issue 5 2009Chun Liu How to measure and model volatility is an important issue in finance. Recent research uses high-frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model-averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility. Copyright © 2009 John Wiley & Sons, Ltd. [source] Measuring predictability: theory and macroeconomic applicationsJOURNAL OF APPLIED ECONOMETRICS, Issue 6 2001Francis X. Diebold We propose a measure of predictability based on the ratio of the expected loss of a short-run forecast to the expected loss of a long-run forecast. This predictability measure can be tailored to the forecast horizons of interest, and it allows for general loss functions, univariate or multivariate information sets, and covariance stationary or difference stationary processes. We propose a simple estimator, and we suggest resampling methods for inference. We then provide several macroeconomic applications. First, we illustrate the implementation of predictability measures based on fitted parametric models for several US macroeconomic time series. Second, we analyze the internal propagation mechanism of a standard dynamic macroeconomic model by comparing the predictability of model inputs and model outputs. Third, we use predictability as a metric for assessing the similarity of data simulated from the model and actual data. Finally, we outline several non-parametric extensions of our approach. Copyright © 2001 John Wiley & Sons, Ltd. [source] Multiple horizons and information in USDA production forecastsAGRIBUSINESS : AN INTERNATIONAL JOURNAL, Issue 1 2008Dwight R. Sanders United States Department of Agriculture (USDA) livestock production forecasts are evaluated for their information content across multiple forecast horizons using the direct test developed by Vuchelen and Gutierrez (2005). Forecasts are explicitly tested for rationality (unbiased and efficient) as well as for incremental information out to three-quarters ahead. The results suggest that although the forecasts are often not rational, they typically do provide the forecast user with unique information at each horizon. Turkey and milk production forecasts are found to provide the most consistent performance, while beef production forecasts provide little information beyond the two-quarter horizon. [C53, Q13] © 2008 Wiley Periodicals, Inc. [source] Power transformation models and volatility forecastingJOURNAL OF FORECASTING, Issue 7 2008Perry Sadorsky Abstract This paper considers the forecast accuracy of a wide range of volatility models, with particular emphasis on the use of power transformations. Where one-period-ahead forecasts are considered, the power autoregressive models are ranked first by a range of error metrics. Over longer forecast horizons, however, generalized autoregressive conditional heteroscedasticity models are preferred. A value-at-risk-based forecast assessment indicates that, while the forecast errors are independent, they are not independent and identically distributed, although this latter result is sensitive to the choice of forecast horizon. Our results are robust across a number of different asset markets. Copyright © 2008 John Wiley & Sons, Ltd. [source] Identification of asymmetric prediction intervals through causal forcesJOURNAL OF FORECASTING, Issue 4 2001J. Scott Armstrong Abstract When causal forces are specified, the expected direction of the trend can be compared with the trend based on extrapolation. Series in which the expected trend conflicts with the extrapolated trend are called contrary series. We hypothesized that contrary series would have asymmetric forecast errors, with larger errors in the direction of the expected trend. Using annual series that contained minimal information about causality, we examined 671 contrary forecasts. As expected, most (81%) of the errors were in the direction of the causal forces. Also as expected, the asymmetries were more likely for longer forecast horizons; for six-year-ahead forecasts, 89% of the forecasts were in the expected direction. The asymmetries were often substantial. Contrary series should be flagged and treated separately when prediction intervals are estimated, perhaps by shifting the interval in the direction of the causal forces. Copyright © 2001 John Wiley & Sons, Ltd. [source] GENETIC PROGRAMMING AND ITS APPLICATION IN REAL-TIME RUNOFF FORECASTING,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2001Soon Thiam Khu ABSTRACT: Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real-time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real-time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly. [source] Prediction of Operating Cash Flows: Further Evidence from AustraliaAUSTRALIAN ACCOUNTING REVIEW, Issue 2 2010Ahsan Habib This paper examines empirically the relative abilities of current operating cash flows (hereafter OCF) and earnings in predicting future operating cash flows in Australia. It extends prior Australian research on cash flow prediction (Percy and Stokes 1992; Clinch, Sidhu and Sing 2002; Farshadfar, Ng and Brimble 2009) by examining future cash flow predictions for one-, two- and three-year-ahead forecast horizons; incorporating additional contextual variables likely to affect the predictive association between current cash flows or earnings and future cash flows; and comparing cross-sectional versus time series-based prediction models to ascertain the relative superiority of one approach over the other. Regression results reveal that the cash flow-based models are more accurate in predicting future operating cash flows than earnings-based models. This result, however, is moderated by firm-specific contextual factors like firm size, negative versus positive cash flow pattern, cash flow variability and firm operating cycle. Finally, a comparison between cross-sectional and time series approaches reveals that the cross-sectional model outperforms the time series model for both the operating cash flows and earnings models in most of the forecast years. [source] | |||||||||||||