nep-for New Economics Papers
on Forecasting
Issue of 2014‒10‒17
eleven papers chosen by
Rob J Hyndman
Monash University
  1. Forecasting electricity spot prices using time-series models with a double temporal segmentation By Marie Bessec; Julien Fouquau; Sophie Meritet
  2. Forecasting World Metal Prices Creation Date: 1995 By Y. Qiang; E.J. Weber
  3. Forecasting the density of oil futures By Florian Ielpo; Benoît Sévi
  4. Forecasting Medium-Term Distributional Effects of the Economic Crisis: A CGE-Microsimulation Approach By Michael FEIL
  5. Evaluating Density Forecasts with Applications to ESPF By BAN Kanemi; KAWAGOE Masaaki; MATSUOKA Hideaki
  6. Forecasting of International Financial Markets with Neural Networks: a New Approach By Rouhia NOOMENE; Roberto LOPEZ
  7. Accounting for Estimation Risk in CAPM-generated Forecasts of Firm Earnings Growth By Salvatore TERREGROSSA
  8. Signal Diffusion Mapping: Optimal Forecasting with Time Varying Lags By Paul Gaskell; Frank McGroarty; Thanassis Tiropanis
  9. Forecasting Steel Demand in China Creation Date: 1990 By D. Chen; K.W. Clements; E.J. Roberts; E.J. Weber
  10. A Multivariate Analysis of Forecast Disagreement: Confronting Models of Disagreement with SPF Data By Dovern, Jonas
  11. Energy Forecasts: Western Australia, 1992-2010 Creation Date: 1992 By L.R. Charleson; E.J. Weber
  1. By: Marie Bessec; Julien Fouquau; Sophie Meritet
    Abstract: The French wholesale market is set to expand in the next few years under European pressure and national decisions. In this paper, we assess the forecasting ability of several classes of time series models for electricity wholesale spot prices at a day-ahead horizon in France. Electricity spot prices display a strong seasonal pattern, particularly in France given the high share of electric heating in housing during winter time. To deal with this pattern, we implement a double temporal segmentation of the data. For each trading period and season, we use a large number of specifications based on market fundamentals: linear regressions, Markov-switching models, threshold models with a smooth transition. An extensive evaluation on French data shows that modeling each season independently leads to better results. Among non-linear models, MS models designed to capture the sudden and fast-reverting spikes in the price dynamics yield more accurate forecasts. Finally, pooling forecasts gives more reliable results.
    Keywords: Electricity spot prices, forecasting, regime-switching.
    JEL: C22 C24 Q47
    Date: 2014–09–25
    URL: http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-588&r=for
  2. By: Y. Qiang; E.J. Weber
    URL: http://d.repec.org/n?u=RePEc:uwa:wpaper:95-17&r=for
  3. By: Florian Ielpo; Benoît Sévi
    Abstract: Forecasting the density of returns is useful for many purposes in finance, such as risk manage- ment activities, portfolio choice or derivative security pricing. Existing methods to forecast the den- sity of returns either use prices of the asset of interest or option prices on this same asset. The latter method needs to convert the risk-neutral estimate of the density into a physical measure, which is computationally cumbersome. In this paper, we take the view of a practitioner who observes the implied volatility under the form of an index, namely the recent OVX, to forecast the density of oil futures returns for horizons going from 1 to 60 days. Using the recent methodology in Maheu and McCurdy (2011) to compute density predictions, we compare the performance of time series models using implied volatility and either daily or intra-daily futures prices. Our results indicate that models based on implied volatility deliver significantly better density forecasts at all horizons, which is in line with numerous studies delivering the same evidence for volatility point forecast.
    Keywords: mplied volatility,OVX, realized volatility, density forecasting, HAR.
    JEL: C15 C32 C53 G1 Q4
    Date: 2014–09–30
    URL: http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-601&r=for
  4. By: Michael FEIL
    URL: http://d.repec.org/n?u=RePEc:ekd:002596:259600056&r=for
  5. By: BAN Kanemi; KAWAGOE Masaaki; MATSUOKA Hideaki
    Abstract: This paper evaluates density forecasts using micro data from the ESP forecast (ESPF), a monthly survey of Japanese professional forecasters. The ESPF has collected individual density forecasts since June 2008. We employ two approaches, Probability Integral Transform (PIT) and Ranked Probability Score (RPS). First, we apply Berkowitz’s (2001) test to individual density forecasts produced every June. We fail to reject the independency in FY 2010 and 2011 real GDP growth rates. As for CPI inflation rates, we reject the independency in all the samples during FY 2008 to 2011, but fail to reject it if the sample is limited to a half with better forecast performance. The result may ensure individual densities coincide with unobserved true data generation process of the actual outcomes. Second, we calculate RPS, following Kenny, Kostka, and Masera (2012), and compare the Mean Probability Distribution (MPD), the average of individual densities, with three benchmarks -- Uniform, Normal and Naïve distributions -- and individual density forecasts. The MPD turns out to be a “good” density: it beats the benchmarks in most cases and ranks about fifth out of around 35 participants every year. Subjective judgments added to the MPD are likely to deteriorate the performance in the case of CPI inflation rate, but to improve in the case of real GDP growth rate.
    Date: 2013–09
    URL: http://d.repec.org/n?u=RePEc:esj:esridp:302&r=for
  6. By: Rouhia NOOMENE; Roberto LOPEZ
    URL: http://d.repec.org/n?u=RePEc:ekd:000238:23800096&r=for
  7. By: Salvatore TERREGROSSA
    URL: http://d.repec.org/n?u=RePEc:ekd:003306:330600139&r=for
  8. By: Paul Gaskell; Frank McGroarty; Thanassis Tiropanis
    Abstract: We introduce a new methodology for forecasting which we call Signal Diffusion Mapping. Our approach accommodates features of real world financial data which have been ignored historically in existing forecasting methodologies. Our method builds upon well-established and accepted methods from other areas of statistical analysis. We develop and adapt those models for use in forecasting. We also present tests of our model on data in which we demonstrate the efficacy of our approach.
    Date: 2014–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1409.6443&r=for
  9. By: D. Chen; K.W. Clements; E.J. Roberts; E.J. Weber
    URL: http://d.repec.org/n?u=RePEc:uwa:wpaper:90-24&r=for
  10. By: Dovern, Jonas
    Abstract: This paper documents multivariate forecast disagreement among professional forecasters of the Euro area economy and discusses implications for models of heterogeneous expectation formation. Disagreement varies over time and is strongly counter-cyclical. Disagreement is positively correlated with general (economic) uncertainty. Aggregate supply shocks drive disagreement about the long-run state of the economy while aggregate demand shocks have an impact on the level of disagreement about the short-run outlook for the economy. Forecasters disagree about the structure of the economy and the degree to which individual forecasters disagree with the average forecast tends to persist over time. This suggests that models of heterogeneous expectation formation, which are currently not able to generate those last two features, need to be modified. Introducing learning mechanisms and heterogeneous signal-to-noise ratios could reconcile the benchmark model for disagreement with the observed facts.
    Keywords: Macroeconomic expectations; forecasts; noisy information; survey data; disagreement
    Date: 2014–09–19
    URL: http://d.repec.org/n?u=RePEc:awi:wpaper:0571&r=for
  11. By: L.R. Charleson; E.J. Weber
    URL: http://d.repec.org/n?u=RePEc:uwa:wpaper:92-01&r=for
This nep-for issue is ©2014 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at http://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.
This page is being maintained by Thomas Krichel. It was last updated on 2023‒07‒08 at 13:00.