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on Forecasting |
| By: | Gonçalo Faria (Católica Porto Business School and CEGE, Universidade Católica Portuguesa); Fabio Verona (Bank of Finland and CEF.UP) |
| Abstract: | We show that the out-of-sample forecast of the equity risk premium can be significantly improved by taking into account the frequency-domain relationship between the equity risk premium and several potential predictors. We consider fifteen predictors from the existing literature, for the out-of-sample forecasting period from January 1990 to December 2014. The best result achieved for individual predictors is a monthly out-of-sample R2 of 2.98 % and utility gains of 549 basis points per year for a mean-variance investor. This performance is improved even further when the individual forecasts from the frequency- decomposed predictors are combined. These results are robust for different subsamples, including the Great Moderation period, the Great Financial Crisis period and, more generically, periods of bad, normal and good economic growth. The strong and robust performance of this method comes from its ability to disentangle the information aggregated in the original time series of each variable, which allows to isolate the frequencies of the predictors with the highest predictive power from the noisy parts. |
| Keywords: | predictability, equity risk premium, frequency domain, discrete wavelets |
| JEL: | C58 G11 G12 G17 |
| Date: | 2016–12 |
| URL: | http://d.repec.org/n?u=RePEc:cap:wpaper:062016&r=for |
| By: | Davide Delle Monache (Bank of Italy); Ivan Petrella (WBS; CEPR) |
| Abstract: | This paper introduces an adaptive algorithm for time-varying autoregressive models in the presence of heavy tails. The evolution of the parameters is determined by the score of the conditional distribution, the resulting model is observation-driven and is estimated by classical methods. In particular, we consider time variation in both coefficients and volatility, emphasizing how the two interact with each other. Meaningful restrictions are imposed on the model parameters so as to attain local stationarity and bounded mean values. The model is applied to the analysis of inflation dynamics with the following results: allowing for heavy tails leads to significant improvements in terms of fit and forecast, and the adoption of the Student-t distribution proves to be crucial in order to obtain well calibrated density forecasts. These results are obtained using the US CPI inflation rate and are confirmed by other inflation indicators, as well as for CPI inflation of the other G7 countries. |
| Keywords: | adaptive algorithms, inflation, score-driven models, student-t, time-varying parameters. |
| JEL: | C22 C51 C53 E31 |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:bbk:bbkcam:1603&r=for |
| By: | Marcin Kolasa; Michal Rubaszek |
| Abstract: | This paper evaluates the forecasting performance of several small open economy DSGE models relative to a closed economy benchmark using a long span of data for Australia, Canada and the United Kingdom. We find that opening the economy does not improve, and even deteriorates the quality of point and density forecasts for key domestic variables. We show that this result can be to a large extent attributed to an increase in forecast error due to a more sophisticated structure of the extended setup. This claim is based on a Monte Carlo experiment, in which an open economy model fails to consistently beat its closed economy benchmark even if it is the true data generating process. |
| Keywords: | Forecasting, DSGE models, New Open Economy Macroeconomics, Bayesian estimation |
| JEL: | D58 E17 F41 F47 |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:sgh:kaewps:2016022&r=for |
| By: | Smeekes, Stephan (QE / Econometrics); Wijler, Etiënne (QE / Econometrics) |
| Abstract: | We study the suitability of lasso-type penalized regression techniques when applied to macroeconomic forecasting with high-dimensional datasets. We consider performance of the lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumption underlying penalized regression methods. We also investigate how the methods handle unit roots and cointegration in the data. In an extensive simulation study we find that penalized regression methods are morerobust to mis-specification than factor models estimated by principal components, even if the underlying DGP is a factor model. Furthermore, the penalized regression methods are demonstrated to deliver forecast improvements over traditional approaches when applied to non-stationary data containing cointegrated variables, despite a deterioration of the selective capabilities. Finally, we also consider an empirical application to a large macroeconomic U.S. dataset and demonstrate that, in line with our simulations, penalized regression methods attain the best forecast accuracy most frequently. |
| Keywords: | Forecasting, Lasso, Factor Models, High-Dimensional Data, Cointegration |
| JEL: | C22 C53 E17 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:unm:umagsb:2016039&r=for |
| By: | Francesco Ravazzolo; Tommy Sveen; Sepideh K. Zahiri |
| Abstract: | This paper analyzes the extent to which information in commodity futures markets is useful for out-of-sample forecasting of commodity currencies. In the earlier literature, commodity price changes are documented to be weak out-of-sample predictors of commodity currency return. In contrast, we find that the basis of several commodities may contain useful information, but the usefulness of any particular commodity basis varies over time and depends on the nature of the commodity. In particular, it seems the basis of commodities with relatively high storage costs tend to be more useful. We argue that high storage costs will tend to make the basis more prone to fluctuations in commodity risk and therefore provide information about the risk premium for commodity currencies. We implement forecast combination strategies that take full advantage |
| Keywords: | Exchange rate predictability, commodity futures market, commodity currencies, forecast combinations |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:bny:wpaper:0047&r=for |