In this issue we have: Beating the Random Walk: a Performance Assessment of Long-term Interest Rate Forecasts ; GDP nowcasting with ragged-edge data : A semi-parametric modelling ; How Much Can Outlook Forecasts be Improved? ; Path Forecast Evaluation ; Forecasting Stock Market Returns: The Sum of the Parts is More than the Whole, and more.
|By:||Frank A.G. den Butter (VU University Amsterdam)
Pieter W. Jansen (Aegon Investment Management)
|This paper assesses the performance of a number of long-term interest rate forecast approaches, namely time series models, structural economic models, expert forecasts and combinations thereof. The predictive performance of these approaches is compared using out of sample forecast errors, where a random walk forecast acts as benchmark. It is found that for five major OECD countries, namely United States, Germany, United Kingdom, The Netherlands and Japan, the other forecasting approaches do not outperform the random walk, or a somewhat more sophisticated time series model, on a 3 month forecast horizon. On a 12 month forecast horizon the random walk model can be outperformed by a model that combines economic data and expert forecasts. Here several methods of combination are considered: equal weights, optimized weights and weights based on forecast error. It appears that the additional information contents of the st ructural models and expert knowledge is only relevant for forecasting 12 months ahead.|
|Keywords:||interest rate forecasting; expert knowledge; combining forecasts; optimizing forecast errors|
|JEL:||C53 E27 E43 E47|
|By:||Laurent Ferrara (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I, Banque de France – Business Conditions and Macroeconomic Forecasting Directorate)
Dominique Guegan (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I, EEP-PSE – Ecole d'Économie de Paris – Paris School of Economics – Ecole d'Économie de Paris)
Patrick Rakotomarolahy (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I)
|This papier formalizes the process of forecasting unbalanced monthly data sets in order to obtain robust nowcasts and forecasts of quarterly GDP growth rate through a semi-parametric modelling. This innovative approach lies on the use on non-parametric methods, based on nearest neighbors and on radial basis function approaches, ti forecast the monthly variables involved in the parametric modelling of GDP using bridge equations. A real-time experience is carried out on Euro area vintage data in order to anticipate, with an advance ranging from six to one months, the GDP flash estimate for the whole zone.|
|Keywords:||Euro area GDP, real-time nowcasting, forecasting, non-parametric models.|
|By:||Colino, Evelyn V.
Irwin, Scott H.
|This study investigates the predictability of outlook hog price forecasts released by Iowa State University relative to alternative market and time-series forecasts. The findings suggest that predictive performance of the outlook hog price forecasts can be improved substantially. Under RMSE, VARs estimated with Bayesian procedures that allow for some degree of flexibility and model averaging consistently outperform Iowa outlook estimates at all forecast horizons. Evidence from the encompassing tests, which are highly stringent tests of forecast performance, indicates that many price forecasts do provide incremental information relative to Iowa. Simple combinations of these models and outlook forecasts are able to reduce forecast errors by economically significant levels. The value of the forecast information is highest at the first horizon and then gradually declines.|
|Keywords:||forecast, futures, models, prices, time-series, vector autoregression, Agricultural Finance,|
|A path forecast refers to the sequence of forecasts 1 to H periods into the future. A summary of the range of possible paths the predicted variable may follow for a given confidence level requires construction of simultaneous confidence regions that adjust for any covariance between the elements of the path forecast. This paper shows how to construct such regions with the joint predictive density and Scheffé's (1953) S-method. In addition, the joint predictive density can be used to construct simple statistics to evaluate the local internal consistency of a forecasting exercise of a system of variables. Monte Carlo simulations demonstrate that these simultaneous confidence regions provide approximately correct coverage in situations where traditional error bands, based on the collection of marginal predictive densities for each horizon, are vastly off mark. The paper showcases these methods with an application t o the most recent monetary episode of interest rate hikes in the U.S. macroeconomy.|
|Keywords:||path forecast, simultaneous confidence region, error bands|
|JEL:||C32 C52 C53|
|By:||Miguel A. Ferreira
|We propose forecasting separately the three components of stock market returns: dividend yield, earnings growth, and price-earnings ratio growth. We obtain out-of-sample R-square coefficients (relative to the historical mean) of nearly 1.6% with monthly data and 16.7% with yearly data using the most common predictors suggested in the literature. This compares with typically negative R-squares obtained in a similar experiment by Goyal and Welch (2008). An investor who timed the market with our approach would have had a certainty equivalent gain of as much as 2.3% per year and a Sharpe ratio 77% higher relative to the historical mean. We conclude that there is substantial predictability in equity returns and that it would have been possible to time the market in real time.|
|By:||Lennart Hoogerheide (Erasmus University Rotterdam)
Herman K. van Dijk (Erasmus University Rotterdam)
|An efficient and accurate approach is proposed for forecasting Value at Risk [VaR] and Expected Shortfall [ES] measures in a Bayesian framework. This consists of a new adaptive importance sampling method for Quantile Estimation via Rapid Mixture of <I>t</I> approximations [QERMit]. As a first step the optimal importance density is approximated, after which multi-step `high loss' scenarios are efficiently generated. Numerical standard errors are compared in simple illustrations and in an empirical GARCH model with Student-<I>t</I> errors for daily S&P 500 returns. The results indicate that the proposed QERMit approach outperforms several alternative approaches in the sense of more accurate VaR and ES estimates given the same amount of computing time, or equivalently requiring less computing time for the same numerical accuracy.|
|Keywords:||Value at Risk; Expected Shortfall; numerical accuracy; numerical standard error; importance sampling; mixture of Student-<I>t</I> distributions; variance reduction technique|
|JEL:||C11 C15 C53 D81|
|By:||Carlos Felipe Lopez Suarez
Jose Antonio Rodriguez Lopez (Department of Economics, University of California-Irvine)
|We study whether the nonlinear behavior of the real exchange rate can help us account for the lack of predictability of the nominal exchange rate. We construct a smooth nonlinear error-correction model that allows us to test the hypotheses of nonlinear predictability of the nominal exchange rate and nonlinear behavior on the real exchange rate in the context of a fully specified cointegrated system. Using a panel of 19 countries and three numeraires, we find strong evidence of nonlinear predictability of the nominal exchange rate and of nonlinear mean reversion of the real exchange rate. Out-of-sample Theil's U-statistics show a higher forecast precision of the nonlinear model than the one obtained with a random walk specification. The statistical significance of the out-of-sample results is higher for short-run horizons, specially for one-quarter-ahead forecasts.|
|Keywords:||Exchange rates; Predictability; Nonlinearities; Purchasing power parity|
|JEL:||C53 F31 F47|
|By:||Dahlgran, Roger A.
|This study focuses on hedging effectiveness defined as the proportionate price risk reduction created by hedging. By mathematical and simulation analysis we determine the following: (a) the regression R2 in the hedge ratio regression will generally overstate the amount of price risk reduction that can be achieved by hedging, (b) the properly computed hedging effectiveness in the hedge ratio regression will also generally overstate the amount of risk reduction that can be achieved by hedging, (c) the overstatement in (b) declines as the sample size increases, (d) application of estimated hedge ratios to non sample data results in an unbiased estimate of hedging effectiveness, (e) application of hedge ratios computed from small samples presents a significant chance of actually increasing price risk by hedging, and (f) comparison of in sample and out of sample hedging effectiveness is not the best method for testing f or structural change in the hedge ratio regression.|
|Keywords:||out of sample, post sample, hedging, effectiveness, forecasts, simulation, Agricultural Finance,|
|By:||Diersen, Matthew A.|
|Higher prices for major crops (e.g., corn, soybeans and wheat) have received considerable attention by analysts, researchers, and producers. A common perception is that acres can be readily bid away from other crops to quickly return to equilibrium price levels. Seldom mentioned are crops that do not trade on a national platform. Principal among these crops probably would be hay from alfalfa and grass. A balance sheet model is developed at the state level for South Dakota. As a state with typically large carryover stocks of hay and multiple markets served, South Dakota presents a stark contrast to states with more stable production, supply, and use. Several structural relations and equations are presented to forecast acres, supply, and price through an inverse demand function. A discussion follows on how to update the price forecast as additional information is obtained. Suggestions are also offered on extending th e model to other states.|
|Keywords:||alfalfa price, feed demand, perennial crop, hay stocks, Agricultural Finance,|
Taken from the NEP-FOR mailing list edited by Rob Hyndman.