nep-for New Economics Papers
on Forecasting
Issue of 2007‒04‒28
five papers chosen by
Rob J Hyndman
Monash University

  1. Online Forecast Combination for Dependent Heterogeneous Data By Sancetta, A.
  2. Dynamic Modeling of Large Dimensional Covariance Matrices By Valeri Voev
  3. On forecasting the term structure of credit spreads By C.N.V. Krishnan; Peter H. Ritchken; James B. Thomson
  4. Bayesian Estimation of the Markov-Switching GARCH(1,1) Model with Student-t Innovations By David Ardia
  5. Costs of Climate Change — The Effects of Rising Temperatures on Health and Productivity By Michael Hübler; Gernot Klepper; Sonja Peterson

  1. By: Sancetta, A.
    Abstract: This paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and no conditions are imposed on the data sequences and the individual forecasts apart from a tail condition. The theoretical results show that the bounds are also valid in the case of time varying combination weights, under specific conditions on the nature of time variation. Some experimental evidence to confirm the results is provided.
    Keywords: Forecast Combination, Model Selection, Multiplicative Update, Non-asymptotic Bound, On-line Learning.
    JEL: C53 C14
    Date: 2007–04
  2. By: Valeri Voev (University of Konstanz)
    Abstract: Modelling and forecasting the covariance of financial return series has always been a challange due to the so-called "curse of dimensionality". This paper proposes a methodology that is applicable in large dimensional cases and is based on a time series of realized covariance matrices. Some solutions are also presented to the problem of non-positive definite forecasts. This methodology is then compared to some traditional models on the basis of its forecasting performance employing Diebold-Mariano tests. We show that our approach is better suited to capture the dynamic features of volatilities and covolatilities compared to the sample covariance based models.
    Date: 2007–02–01
  3. By: C.N.V. Krishnan; Peter H. Ritchken; James B. Thomson
    Abstract: Predictions of firm-by-firm term structures of credit spreads based on current spot and forward values can be improved upon by exploiting information contained in the shape of the credit-spread curve. However, the current credit-spread curve is not a sufficient statistic for predicting future credit spreads; the explanatory power can be increased further by exploiting information contained in the shape of the riskless-yield curve. In the presence of credit-spread and riskless factors, other macroeconomic, marketwide, and firm-specific risk variables do not significantly improve predictions of credit spreads. Current credit-spread and riskless-yield curves impound essentially all marketwide and firm-specific information necessary for predicting future credit spreads.
    Keywords: Corporate bond ; Rate of return
    Date: 2007
  4. By: David Ardia (Department of Quantitative Economics)
    Abstract: This article proposes the Bayesian estimation of the MSGARCH model with Student-t innovations. We introduce a new MCMC scheme which generates the GARCH parameters by block, the vector of state variables in a multi-move manner and the degrees of freedom parameter of the Student-t distribution using an efficient rejection technique. Our methodology is fully automatic and avoids the time-consuming and difficult task of choosing and tuning a sampling algorithm. As an application, we fit a single-regime GARCH model and a MSGARCH model to SMI log-returns. We use the random permutation sampler to find suitable identification constraints for the MSGARCH model and show the presence of two distinct volatility regimes in the time series. By using the Deviance information criterion and estimating the model likelihoods we show the in-sample superiority of the MSGARCH model. Finally, we test the forecasting performance of the competing models based on the VaR and document the superiority of the Markov-switching specification.
    Keywords: Bayesian;MCMC;Markov-switching;HMM;GARCH;DIC;Marginal likelihood;Model likelihood;VaR;SMI
    JEL: C11 C13 C15 C22 C52 C53
    Date: 2007–04–12
  5. By: Michael Hübler; Gernot Klepper; Sonja Peterson
    Abstract: The aim of the study is to quantify climate induced health risks for Germany. Based on high resolution climate scenarios for the period 2071 to 2100 we forecast the number of days with heat load and cold stress. The heat frequency and intensity increases overall but more in the south. Referring to empirical studies on heat induced health effects we estimate an average increase in the number of heat induced casualties by a factor of more than 3. Heat related hospitalization costs increase 6-fold not including the cost of ambulant treatment. Heat also reduces the work performance resulting in an estimated output loss of between 0.12 % and 0.48 % of GDP.
    Keywords: Costs of climate change, health effects, heat waves, mortality, hospitalization costs, labor productivity
    JEL: I10 Q51 Q54
    Date: 2007–04

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