nep-for New Economics Papers
on Forecasting
Issue of 2007‒12‒01
eight papers chosen by
Rob J Hyndman
Monash University

  1. Bayesian forecast combination for VAR models By Andersson, Michael K; Karlsson, Sune
  2. Forecasting Bonds Yields in the Brazilian Fixed Income Market By Jose Vicente; Benjamin M. Tabak
  3. Forecasting electricity spot market prices with a k-factor GIGARCH process By Abdou Kâ Diongue; Dominique Guégan; Bertrand Vignal
  4. NoVaS Transformations: Flexible Inference for Volatility Forecasting By Dimitris Politis; Dimitrios Thomakos
  5. Robust Value at Risk Prediction By Loriano Mancini; Fabio Trojani
  6. 'Optimal' Probabilistic Predictions for Financial Returns By Dimitrios Thomakos; Tao Wang
  7. The Curse of Irving Fisher (Professional Forecasters' Version) By Gregor W. Smith; James Yetman
  8. Gauging the uncertainty of the economic outlook from historical forecasting errors By David Reifschneider; Peter Tulip

  1. By: Andersson, Michael K (Monetary Policy Department, Central Bank of Sweden); Karlsson, Sune (Department of Business, Economics, Statistics and Informatics)
    Abstract: We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty regarding which endogenous variables to include in the model. That is, all models include the forecast variables, but may otherwise have differing sets of endogenous variables. This is a difficult problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the variables of interest and we construct model weights from the predictive likelihood of the forecast variables. The procedure is evaluated in a small simulation study and found to perform competitively in applications to real world data.
    Keywords: Bayesian model averaging; Predictive likelihood; GDP forecasts
    JEL: C11 C15 C32 C52 C53
    Date: 2007–11–01
  2. By: Jose Vicente; Benjamin M. Tabak
    Abstract: This paper studies the predictive ability of a variety of models in forecasting the yield curve for the Brazilian fixed income market. We compare affine term structure models with a variation of the Nelson-Siegel exponential framework developed by Diebold and Li (2006). Empirical results suggest that forecasts made with the latter methodology are superior and appear accurate at long horizons when compared to different benchmark forecasts. These results are important for policy makers, portfolio and risk managers. Further research could study the predictive ability of such models in other emerging markets.
    Date: 2007–08
  3. By: Abdou Kâ Diongue (UFR SAT - Université Gaston Berger - Université Gaston Berger de Saint-Louis); Dominique Guégan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Bertrand Vignal (EDF - EDF - Recherche et Développement)
    Abstract: In this article, we investigate conditional mean and variance forecasts using a dynamic model following a k-factor GIGARCH process. We are particularly interested in calculating the conditional variance of the prediction error. We apply this method to electricity prices and test spot prices forecasts until one month ahead forecast. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria.
    Keywords: Conditional mean, conditional variance, forecast, electricity prices, GIGARCH process.
    Date: 2007–11
  4. By: Dimitris Politis; Dimitrios Thomakos
    Abstract: In this paper we contribute several new results on the NoVaS transformation approach for volatility forecasting introduced by Politis (2003a,b, 2007). In particular: (a) we introduce an alternative target distribution (uniform); (b) we present a new method for volatility forecasting using NoVaS ; (c) we show that the NoVaS methodology is applicable in situations where (global) stationarity fails such as the cases of local stationarity and/or structural breaks; (d) we show how to apply the NoVaS ideas in the case of returns with asymmetric distribution; and finally (e) we discuss the application of NoVaS to the problem of estimating value at risk (VaR). The NoVaS methodology allows for a flexible approach to inference and has immediate applications in the context of short time series and series that exhibit local behavior (e.g. breaks, regime switching etc.) We conduct an extensive simulation study on the predictive ability of the NoVaS approach and and that NoVaS forecasts lead to a much `tighter' distribution of the forecasting performance measure for all data generating processes. This is especially relevant in the context of volatility predictions for risk management. We further illustrate the use of NoVaS for a number of real datasets and compare the forecasting performance of NoVaS -based volatility forecasts with realized and range-based volatility measures.
    Keywords: ARCH, GARCH, local stationarity, structural breaks, VaR, volatility.
    Date: 2007
  5. By: Loriano Mancini (University of Zurich); Fabio Trojani (University of St-Gallen)
    Abstract: We propose a general robust semiparametric bootstrap method to estimate conditional predictive distributions of GARCH-type models. Our approach is based on a robust estimator for the parameters in GARCH-type models and a robustified resampling method for standardized GARCH residuals, which controls the bootstrap instability due to influential observations in the tails of standardized GARCH residuals. Monte Carlo simulation shows that our method consistently provides lower VaR forecast errors, often to a large extent, and in contrast to classical methods never fails validation tests at usual significance levels. We test extensively our approach in the context of real data applications to VaR prediction for market risk, and find that only our robust procedure passes all validation tests at usual confidence levels. Moreover, the smaller tail estimation risk of robust VaR forecasts implies VaR prediction intervals that can be nearly 20% narrower and 50% less volatile over time. This is a further desirable property of our method, which allows to adapt risky positions to VaR limits more smoothly and thus more efficiently.
    Keywords: Backtesting, M-estimator, Extreme Value Theory, Breakdown Point.
    JEL: C14 C15 C23 C59
    Date: 2005–10
  6. By: Dimitrios Thomakos; Tao Wang
    Abstract: We examine the `relative optimality' of sign predictions for financial returns, extending the work of Christoffersen and Diebold (2006) on volatility dynamics and sign predictability. We show that there is a more general decomposition of financial returns than that implied by the sign decomposition and which depends on the choice of the threshold that defines direction. We then show that the choice of the threshold matters and that a threshold of zero (leading to sign predictions) is not necessarily `optimal'. We provide explicit conditions that allow for the choice of a threshold that has maximum responsiveness to changes in volatility dynamics and thus leads to `optimal' probabilistic predictions. Finally, we connect the evolution of volatility to probabilistic predictions and show that the volatility ratio is the crucial variable in this context. Our work strengthens the arguments in favor of accurate volatility measurement and prediction, as volatility dynamics are integrated into the `optimal' threshold. We provide an empirical illustration of our findings using monthly returns and realized volatility for the S&P500 index.
    Date: 2007
  7. By: Gregor W. Smith (Queen's University); James Yetman (University of Hong Kong)
    Abstract: Dynamic Euler equations restrict multivariate forecasts. Thus a range of links between macroeconomic variables can be studied by seeing whether they hold within the multivariate predictions of professional forecasters. We illustrate this novel way of testing theory by studying the links between forecasts of U.S. nominal interest rates, inflation, and real consumption growth since 1981. By using forecast data for both returns and macroeconomic fundamentals, we use the complete cross-section of forecasts, rather than the median. The Survey of Professional Forecasters yields a three-dimensional panel, across quarters, forecasters, and forecast horizons. This approach yields 14727 observations, much greater than the 107 time series observations. The resulting precision reveals a significant, negative relationship between consumption growth and interest rates.
    Keywords: forecast survey, asset pricing, Fisher effect
    JEL: E17 E21 E43
    Date: 2007–11
  8. By: David Reifschneider; Peter Tulip
    Abstract: Participants in meetings of the Federal Open Market Committee (FOMC) regularly produce individual projections of real activity and inflation that are published in summary form. These summaries indicate participants' views about the most likely course for the macroeconomy but, by themselves, are not enough to gauge the full range of possible outcomes -- that is, the uncertainty surrounding the outlook. To this end, FOMC participants will now provide qualitative assessments of how they view the degree of current uncertainty relative to that which prevailed on average in the past. This paper discusses a method for gauging the average magnitude of historical uncertainty using information on the predictive accuracy of a number of private and government forecasters. The results suggest that, if past performance is a reasonable guide to the accuracy of future forecasts, considerable uncertainty surrounds all macroeconomic projections, including those of FOMC participants.
    Date: 2007

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