nep-for New Economics Papers
on Forecasting
Issue of 2009‒12‒11
eleven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting electricity spot market prices with a k-factor GIGARCH process By Abdou Kâ Diongue; Dominique Guegan; Bertrand Vignal
  2. GDP nowcasting with ragged-edge data : A semi-parametric modelling By Laurent Ferrara; Dominique Guegan; Patrick Rakotomarolahy
  3. Forecasting Realized Volatility with Linear and Nonlinear Models By McAleer, M.; Medeiros, M.C.
  4. Forecasting Macroeconomic Time Series With Locally Adaptive Signal Extraction By Giordani, Paolo; Villani, Mattias
  5. Estimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factors. By Lucia Alessi; Matteo Barigozzi; Marco Capasso
  6. Comparing univariate and multivariate models to forecast portfolio value-at-risk By Andre A. P.; Francisco J. Nogales; Esther Ruiz
  7. VaR Forecast and Dynamic Conditional Correlations for Spot and Futures Returns on Stocks and Bonds By Hakim, A.; McAleer, M.
  8. Forecasting chaotic systems : The role of local Lyapunov exponents By Dominique Guegan; Justin Leroux
  10. Realising the future: forecasting with high frequency based volatility (HEAVY) models By Neil Shephard; Kevin Sheppard
  11. The skew pattern of implied volatility in the DAX index options market By Silvia Muzzioli

  1. By: Abdou Kâ Diongue (UFR SAT - Université Gaston Berger - Université Gaston Berger de Saint-Louis); Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); 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: 2009–04
  2. By: Laurent Ferrara (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, Banque de France - Business Conditions and Macroeconomic Forecasting Directorate); Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); Patrick Rakotomarolahy (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I)
    Abstract: This papier formalizes the process of forecasting unbalanced monthly data sets in order to obtain robust nowcasts and forecasts of quarterly GDP growth rate through a semi-parametric modelling. This innovative approach lies on the use on non-parametric methods, based on nearest neighbors and on radial basis function approaches, ti forecast the monthly variables involved in the parametric modelling of GDP using bridge equations. A real-time experience is carried out on Euro area vintage data in order to anticipate, with an advance ranging from six to one months, the GDP flash estimate for the whole zone.
    Keywords: Euro area GDP, real-time nowcasting, forecasting, non-parametric models.
    Date: 2009–11
  3. By: McAleer, M.; Medeiros, M.C. (Erasmus Econometric Institute)
    Abstract: In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.
    Keywords: financial econometrics;volatility forecasting;neural networks;nonlinear models;realized volatility;bagging
    Date: 2009–11–24
  4. By: Giordani, Paolo (Research Department, Central Bank of Sweden); Villani, Mattias (Research Department, Central Bank of Sweden)
    Abstract: We introduce a non-Gaussian dynamic mixture model for macroeconomic forecasting. The Locally Adaptive Signal Extraction and Regression (LASER) model is designed to capture relatively persistent AR processes (signal) contaminated by high frequency noise. The distribution of the innovations in both noise and signal is robustly modeled using mixtures of normals. The mean of the process and the variances of the signal and noise are allowed to shift suddenly or gradually at unknown locations and number of times. The model is then capable of capturing movements in the mean and conditional variance of a series as well as in the signal-to-noise ratio. Four versions of the model are used to forecast six quarterly US and Swedish macroeconomic series. We conclude that (i) allowing for infrequent and large shifts in mean while imposing normal iid errors often leads to erratic forecasts, (ii) such shifts/breaks versions of the model can forecast well if robustified by allowing for non-normal errors and time varying variances, (iii) infrequent and large shifts in error variances outperform smooth and continuous shifts substantially when it comes to interval coverage, (iv) for point forecasts, robust time varying specifications improve slightly upon fixed parameter specifications on average, but the relative performances can differ sizably in various sub-samples, v) for interval forecasts, robust versions that allow for infrequent shifts in variances perform substantially and consistently better than time invariant specifications.
    Keywords: Bayesian inferene; Foreast evaluation; Regime swithing; State-space modeling; Dynamic Mixture models
    JEL: C11 C53
    Date: 2009–10–01
  5. By: Lucia Alessi (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); Matteo Barigozzi (European Center for the Advanced Research in Economics and Statistics (ECARES), Université libre de Bruxelles, Belgium.); Marco Capasso (Utrecht School of Economics, Utrecht University,  P.O. Box 80.115,  3508 TC  Utrecht, The Netherlands.)
    Abstract: We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. The information contained in large datasets is captured by few dynamic common factors, which we assume being conditionally heteroskedastic. After presenting the model, we propose a multi-step estimation technique which combines asymptotic principal components and multivariate GARCH. We also prove consistency of the estimated conditional covariances. We present simulation results in order to assess the finite sample properties of the estimation technique. Finally, we carry out two empirical applications respectively on macroeconomic series, with a particular focus on different measures of inflation, and on financial asset returns. Our model outperforms the benchmarks in forecasting the inflation level, its conditional variance and the volatility of returns. Moreover, we are able to predict all the conditional covariances among the observable series. JEL Classification: C52, C53.
    Keywords: Dynamic Factor Models, Multivariate GARCH, Conditional Covariance, Inflation Forecasting, Volatility Forecasting.
    Date: 2009–11
  6. By: Andre A. P.; Francisco J. Nogales; Esther Ruiz
    Abstract: This article addresses the problem of forecasting portfolio value-at-risk (VaR) with multivariate GARCH models vis-à-vis univariate models. Existing literature has tried to answer this question by analyzing only small portfolios and using a testing framework not appropriate for ranking VaR models. In this work we provide a more comprehensive look at the problem of portfolio VaR forecasting by using more appropriate statistical tests of comparative predictive ability. Moreover, we compare univariate vs. multivariate VaR models in the context of diversified portfolios containing a large number of assets and also provide evidence based on Monte Carlo experiments. We conclude that, if the sample size is moderately large, multivariate models outperform univariate counterparts on an out-of-sample basis.
    Keywords: Market risk, Backtesting, Conditional predictive ability, GARCH, Volatility, Capital requirements, Basel II
    Date: 2009–11
  7. By: Hakim, A.; McAleer, M. (Erasmus Econometric Institute)
    Abstract: The paper investigates the interdependence and conditional correlations between futures contracts and their underlying assets, both for stock and bond markets, and the impact of the interdependence and conditional correlations on VaR forecasts. The paper finds evidence of volatility spillovers from spot (futures) to futures (spot) markets, and time-varying conditional correlations between futures and their underlying assets. It also finds evidence that the DCC model of Engle (2002) provides slightly better VaR forecasts as compared with the CCC model of Bollerslev (1990) and the BEKK model of Engle and Kroner (1995).
    Keywords: interdependence;dynamic conditional correlations;spot;futures;stocks;bonds;VaR
    Date: 2009–11–23
  8. By: Dominique Guegan (EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris, CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Justin Leroux (Institute for Applied Economics - HEC MONTRÉAL)
    Abstract: We propose a novel methodology for forecasting chaotic systems which is based on exploiting the information conveyed by the local Lyapunov exponents of a system. This information is used to correct for the inevitable bias of most non-parametric predictors. Using simulated data, we show that gains in prediction accuracy can be substantial.
    Keywords: chaotic systems
    Date: 2009–09
  9. By: Yoichi Okita (National Graduate Institute for Policy Studies); Wade D. Pfau (National Graduate Institute for Policy Studies); Giang Thanh Long (National Economics University (NEU))
    Abstract: Obtaining appropriate forecasts for the future population is a vital component of public policy analysis for issues ranging from government budgets to pension systems. Traditionally, demographic forecasters rely on a deterministic approach with various scenarios informed by expert opinion. This approach has been widely criticized, and we apply an alternative stochastic modeling framework that can provide a probability distribution for forecasts of the Japanese population. We find the potential for much greater variability in the future demographic situation for Japan than implied by existing deterministic forecasts. This demands greater flexibility from policy makers when confronting population aging issues.
    Keywords: stochastic population forecasts, Japan, Lee-Carter method
    JEL: J1 C53
    Date: 2009–05
  10. By: Neil Shephard (Oxford-Man Institute and Department of Economics, University of Oxford); Kevin Sheppard (Department of Economics and Oxford-Man Institute, University of Oxford)
    Abstract: This paper studies in some detail a class of high frequency based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realized measures constructed from high frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model based bootstrap which allow us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models.
    Keywords: ARCH models; bootstrap; missing data; multiplicative error model; multistep ahead prediction; non-nested likelihood ratio test; realised kernel; realised volatility.
    Date: 2009–07–10
  11. By: Silvia Muzzioli
    Abstract: The aim of this paper is twofold: to investigate how the information content of implied volatility varies according to moneyness and option type, and to compare option-based forecasts with historical volatility in order to see if they subsume all the information contained in historical volatility. The different information content of implied volatility is examined for the most liquid at-the-money and out-of-the-money options: put (call) options for strikes below (above) the current underlying asset price, i.e. the ones that are usually used as inputs for the computation of the smile function. In particular, since at-the-money implied volatilities are usually inserted in the smile function by computing some average of both call and put implied ones, we investigate the performance of a weighted average of at-the-money call and put implied volatilities with weights proportional to trading volume. Two hypotheses are tested: unbiasedness and efficiency of the different volatility forecasts. The investigation is pursued in the Dax index options market, by using synchronous prices matched in a one-minute interval. It was found that the information content of implied volatility has a humped shape, with out-of-the-money options being less informative than at-the-money ones. Overall, the best forecast is at-the-money put implied volatility: it is unbiased (after a constant adjustment) and efficient, in that it subsumes all the information contained in historical volatility.
    Keywords: Implied Volatility; Volatility Smile; Volatility forecasting; Option type
    JEL: G13 G14
    Date: 2009–12

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