nep-for New Economics Papers
on Forecasting
Issue of 2015‒05‒30
twelve papers chosen by
Rob J Hyndman
Monash University

  1. Forecaster overconfidence and market survey performance By Deaves, Richard; Lei, Jin; Schröder, Michael
  2. Conditional Term Structure of Inflation Forecast Uncertainty: The Copula Approach By Wojciech Charemza; Carlos Díaz; Svetlana Makarova
  3. Ex-post Inflation Forecast Uncertainty and Skew Normal Distribution: ‘Back from the Future’ Approach By Wojciech Charemza; Carlos Díaz; Svetlana Makarova
  4. Asymptotic Inference in the Lee-Carter Model for Modelling Mortality Rates By Reese, Simon
  5. Volatility forecasting using global stochastic financial trends extracted from non-synchronous data By Grigoryeva, Lyudmila; Ortega, Juan-Pablo; Peresetsky, Anatoly
  6. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution" By Makoto Takahashi; Toshiaki Watanabe; Yasuhiro Omori
  7. “Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis” By Oscar Claveria; Enric Monte; Salvador Torra
  9. Do central bank forecasts matter for professional forecasters? By Jacek Kotłowski
  10. Improving profitability forecasts with information on earnings quality By Demmer, Matthias
  11. Prediction of air pollution peaks generated by urban transport networks By Bell, Margaret; Bergantino, Angela S.; Catalano, Mario; Galatioto, Fabio
  12. Autocorrelation in an unobservable global trend: Does it help to forecast market returns? By Peresetsky, Anatoly; Yakubov, Ruslan

  1. By: Deaves, Richard; Lei, Jin; Schröder, Michael
    Abstract: We document using the ZEW panel of German stock market forecasters that weak forecasters tend to be overconfident in the sense that they provide extreme forecasts and their confidence intervals are less likely to contain eventual realizations. Moderate filters based on forecast accuracy over short rolling windows are somewhat successful in improving predictability. While poor performance can be due to various factors, a filter based on a prior tendency to provide extreme forecasts also improves predictability.
    Keywords: Overconfidence,Forecasting Performance,Stock Market
    JEL: G02 G17
    Date: 2015
  2. By: Wojciech Charemza; Carlos Díaz; Svetlana Makarova
    Abstract: The paper introduces the concept of conditional inflation forecast uncertainty. It is proposed that the joint and conditional distributions of the bivariate forecast uncertainty can be derived from estimation unconditional distributions of these uncertainties and applying appropriate copula function. Empirical results have been obtained for Canada and US. Term structure has been evaluated in the form of unconditional and conditional probabilities of hitting the inflation range of ±1% around the Canadian inflation target. The paper suggests a new measure of inflation forecast uncertainty that accounts for possible inter-country dependence. It is shown that evaluation of targeting precision can be effectively improved with the use of ex-ante formulated conditional and unconditional probabilities of inflation being within the pre-defined band around the target.
    Keywords: Macroeconomic Forecasting, Inflation, Uncertainty, Non-normality, Density Forecasting, Forecast Term Structure, Copula Modelling
    JEL: C53 E37 E52
    Date: 2015–05
  3. By: Wojciech Charemza; Carlos Díaz; Svetlana Makarova
    Abstract: Empirical evaluation of macroeconomic uncertainty and its use for probabilistic forecasting are investigated. New indicators of forecast uncertainty, which either include or exclude effects of macroeconomic policy, are developed. These indicators are derived from the weighted skew normal distribution proposed in this paper, which parameters are interpretable in relation to monetary policy outcomes and actions. This distribution is fitted to forecast errors, obtained recursively, of annual inflation recorded monthly for 38 countries. Forecast uncertainty term structure is evaluated for U.K. and U.S. using new indicators and compared with earlier results. This paper has supplementary material.
    Keywords: forecast term structure, macroeconomic forecasting, monetary policy, non-normality
    JEL: C54 E37 E52
    Date: 2015–05
  4. By: Reese, Simon (Department of Economics, Lund University)
    Abstract: The most popular approach to modelling and forecasting mortality rates is the model of Lee and Carter (Modeling and Forecasting U. S. Mortality, Journal of the American Statistical Association, 87, 659–671, 1992). The popularity of the model rests mainly on its good fit to the data, its theoretical properties being obscure. The present paper provides asymptotic results for the Lee-Carter model and illustrates its inherent weaknesses formally. Requirements on the underlying data are established and variance estimators are presented in order to allow hypothesis testing and the computation of confidence intervals.
    Keywords: Lee-Carter model; mortality; common factor models; panel data
    JEL: C33 C51 C53 J11
    Date: 2015–05–26
  5. By: Grigoryeva, Lyudmila; Ortega, Juan-Pablo; Peresetsky, Anatoly
    Abstract: This paper introduces a method based on the use of various linear and nonlinear state space models that uses non-synchronous data to extract global stochastic financial trends (GST). These models are specifically constructed to take advantage of the intraday arrival of closing information coming from different international markets in order to improve the quality of volatility description and forecasting performances. A set of three major asynchronous international stock market indices is used in order to empirically show that this forecasting scheme is capable of significant performance improvements when compared with those obtained with standard models like the dynamic conditional correlation (DCC) family.
    Keywords: multivariate volatility modeling and forecasting, global stochastic trend, extended Kalman filter, CAPM, dynamic conditional correlations (DCC), non-synchronous data
    JEL: C32 C5
    Date: 2015
  6. By: Makoto Takahashi (Graduate School of Economics, Osaka University); Toshiaki Watanabe (Institute of Economic Research, Hitotsubashi University); Yasuhiro Omori (Faculty of Economics, The University of Tokyo)
    Abstract: The predictive performance of the realized stochastic volatility model of Takahashi, Omori, and Watanabe (2009), which incorporates the asymmetric stochastic volatility model with the realized volatility, is investigated. Considering well known characteristics of nancial returns, heavy tail and negative skewness, the model is extended by employing a wider class distribution, the generalized hyperbolic skew Student's t-distribution, for nancial returns. With the Bayesian estimation scheme via Markov chain Monte Carlo method, the model enables us to estimate the parameters in the return distribution and in the model jointly. It also makes it possible to forecast volatility and return quantiles by sampling from their posterior distributions jointly. The model is applied to quantile forecasts of nancial returns such as value-at-risk and expected shortfall as well as volatility forecasts and those forecasts are evaluated by various tests and performance measures. Empirical results with the US and Japanese stock indices, Dow Jones Industrial Average and Nikkei 225, show that the extended model improves the volatility and quantile forecasts especially in some volatile periods. --
    Date: 2015–05
  7. By: Oscar Claveria (Department of Econometrics. University of Barcelona); Enric Monte (Department of Signal Theory and Communications. Polytechnic University of Catalunya.); Salvador Torra (Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona)
    Abstract: By means of Self-Organizing Maps we cluster fourteen European countries according to the most suitable way to model their agents’ expectations. Using the financial crisis of 2008 as a benchmark, we distinguish between those countries that show a progressive anticipation of the crisis and those where sudden changes in expectations occur. By mapping the trajectory of economic experts’ expectations prior to the recession we find that when there are brisk changes in expectations before impending shocks, Artificial Neural Networks are more suitable than time series models for modelling expectations. Conversely, in countries where expectations show a smooth transition towards recession, ARIMA models show the best forecasting performance. This result demonstrates the usefulness of clustering techniques for selecting the most appropriate method to model and forecast expectations according to their behaviour.
    Keywords: Business surveys; Self-Organizing Maps; Clustering; Forecasting; Neural networks; Time series models; Nonlinear models JEL classification: C02; C22; C45; C63; E27
    Date: 2015–03
  8. By: Prashant Joshi
    Abstract: The study uses three different models: GARCH(1,1), EGARCH(1,1) and GJR-GARCH(1,1) to analyze volatility of Nifty of National Stock Exchange (NSE) of India from January 1, 2010 to July 4, 2014. The results reveal persistence of volatility andthe presence of leverage effect implying impact of good and bad news is not same. To evaluate the models, various model selection and forecasting performance criterion like AIC, SBC, RMSE, MAE, MAPE and TIC criterionare employed. Our results indicate that GARCH (1,1) has better forecasting ability in NSE. JEL Classification: G14, C32 Key words: Volatility clustering, GARCH, EGARCH, TGARCH, RMSE, MAE, MAPE, TIC
    Date: 2014–09
  9. By: Jacek Kotłowski
    Abstract: This paper examines to what extent public information provided by the central bank affects the forecasts formulated by professional forecasters. We investigate empirically whether disclosing GDP and inflation forecasts by Narodowy Bank Polski (the central bank of Poland) reduces the disagreement in professional forecasters’ expectations. The results only partially support the hypothesis on the coordinating role of the central bank existing in the literature. The main finding is that by publishing its projection of future GDP growth, the central bank reduces the dispersion of one-year-ahead GDP forecasts. Moreover our study indicates that the role of the central bank in reducing the forecasts dispersion is strengthening over time. We also find using non-linear STR models that the extent to which the projection release affects the dispersion of GDP forecasts varies over the business cycle. By disclosing its own projection the central bank reduces the disagreement among the forecasters the most in the periods when the economy moves from one phase of the business cycle to another. On the contrary, the release of CPI projection by NBP affects neither the cross-sectional dispersion nor the level of forecasts formulated by professional forecasters.
    Keywords: Monetary policy, inflation targeting, forecasting, central bank communication, survey expectations, forecasts disagreement, STR models.
    JEL: C24 E37 E52 E58
    Date: 2015
  10. By: Demmer, Matthias
    Abstract: Prior literature documents the usefulness of the DuPont disaggregation for predicting firms future profitability, operating income, and stock market returns. In addition, research also emphasizes the importance of earnings quality information. However, there is a lack of research examining how earnings quality affects forecasts of profitability. This paper explores whether different earnings quality factors moderate the accuracy of profitability forecasts. This study contributes to the existing literature along three dimensions. First, contrary to financial statement analysis studies, I find that changes in profit margin provide incremental information for predicting changes in future return on assets. After controlling for earnings quality factors, the incremental usefulness of this accounting signal increases significantly. Second, this paper contributes to the earnings quality literature by providing an approach as how to include this information into forecasts of profitability. In doing so, I incorporate the main drivers of earnings quality (i.e. fundamental performance and the accounting system) into profitability forecasts. Last, the paper adds to the literature on how capital market participants perceive accounting information. I document that both analysts and investors appear to efficiently incorporate earnings quality information in their investment decisions.
    Keywords: financial statement analysis,forecasting profitability,DuPont analysis,earnings quality,conservative accounting,persistence,growth,return on net,operating assets
    JEL: M41
    Date: 2015
  11. By: Bell, Margaret; Bergantino, Angela S.; Catalano, Mario; Galatioto, Fabio
    Abstract: This paper illustrates the first results of an ongoing research for developing novel methods to analyse and simulate the relationship between trasport-related air pollutant concentrations and easily accessible explanatory variables. The final scope of the analysis is to integrate the new models in traditional traffic management decision-support systems for a sustainable mobility of road vehicles in urban areas. This first stage concerns the relationship between the mean hourly concentration of nitrogen dioxide and explanatory factors like traffic and weather conditions, with particular reference to the prediction of pollution peaks, defined as exceedances of normative concentration limits. Two modelling frameworks are explored: the Artificial Neural Network approach and the ARIMAX model. Furthermore, the benefit of a synergic use of both models for air quality forecasting is investigated. The analysis of findings points out that the prediction of extreme pollutant concentrations is best performed by the integration of the two models into an ensemble. The neural network is outperformed by the ARIMAX model in foreseeing peaks, but gives a more realistic representation of the relationships between concentration and wind characteristics. So, it can be exploited to direct the ARIMAX model specification. At last, the study shows that the ability at forecasting exceedances of pollution regulative limits can be enhanced by requiring traffic management actions when the predicted concentration exceeds a threshold that is pretty high but lower than the normative one.
    Date: 2015
  12. By: Peresetsky, Anatoly; Yakubov, Ruslan
    Abstract: In this paper a Kalman-filter type model is used to extract a global stochastic trend from discrete non-synchronous data on daily stock market index returns from different markets . The model allows for the autocorrelation in the global stochastic trend, which means that its increments are predictable. It does not necessarily mean the predictability of market returns, since the global trend is unobservable. The performance of the model for the forecast of market returns is explored for three markets: Japan, UK, US.
    Keywords: financial market integration; stock market returns; state space model; Kalman filter; non-synchronous data; market returns forecast
    JEL: C49 C58 F36 G10 G15
    Date: 2015

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