Forecasting papers 2009-05-04

In this issue we have: Forecasting electricity spot market prices with a k-factor Gigarch process , The Forecast Performance of Competing Implied Volatility Measures, Forecasting VaR and Expected Shortfall using Dynamical Systems, Predicting Betas: Two new methods and more.

    1. Forecasting electricity spot market prices with a k-factor GIGARCH process
    1. Date:
    2009-04
    By: Abdou K̢ Diongue (UFR SAT РUniversit̩ Gaston Berger РUniversit̩ 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 РEDF РRecherche et D̩veloppement)
    URL: http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00307606_v1&r=for
    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
    1. The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks
    1. Date:
    2009-03-19
    By: Tsiaras, Leonidas (Department of Business Studies, Aarhus School of Business)
    URL: http://d.repec.org/n?u=RePEc:hhb:aarbfi:2009-02&r=for
    This study examines the information content of alternative implied volatility measures for the 30 components of the Dow Jones Industrial Average Index from 1996 until 2007. Along with the popular Black-Scholes and "model-free" implied volatility expectations, the recently proposed corridor implied volatility (CIV) measures are explored. For all pair-wise comparisons, it is found that a CIV measure that is closely related to the model-free implied volatility, nearly always delivers the most accurate forecasts for the majority of the firms. This finding remains consistent for different forecast horizons, volatility definitions, loss functions and forecast evaluation settings.
    Keywords: No keywords;
    1. Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy
    1. Date:
    2009-04
    By: Cyril Caillault (Fortis Investments – Fortis investments)
    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)
    URL: http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00375765_v1&r=for
    Using non-parametric and parametric models, we show that the bivariate distribution of an Asian portfolio is not stable along all the period under study. We suggest several dynamic models to compute two market risk measures, the Value at Risk and the Expected Shortfall: the RiskMetrics methodology, the Multivariate GARCH models, the Multivariate Markov-Switching models, the empirical histogram and the dynamic copulas. We discuss the choice of the best method with respect to the policy management of bank supervisors. The copula approach seems to be a good compromise between all these models. It permits taking financial crises into account and obtaining a low capital requirement during the most important crises.
    Keywords: Value at Risk ; Expected Shortfall ; Copulas ; Risk management ; GARCH models ; Markov switching models
    1. Predicting Betas: Two new methods.
    1. Date:
    2009-04-21
    By: Mª Victoria Esteban González (Facultad de CC. EE. y Empresariales, UPV/EHU)
    Fernando Tusell Palmer (Facultad de CC. EE. y Empresariales, UPV/EHU)
    URL: http://d.repec.org/n?u=RePEc:ehu:biltok:200901&r=for
    Betas play a central role in modern finance. The estimation of betas from historical data and their extrapolation into the future is of considerable practical interest. We propose two new methods: the first is a direct generalization of the method in Blume (1975), and the second is based on Procrustes rotation in phase space. We compare their performance with various competitors and draw some conclusions.
    Keywords: risk prediction, systematic risk, beta coefficients, Procustes rotation
    JEL: G11
    1. A High-Low Model of Daily Stock Price Ranges
    1. Date:
    2009-01
    By: Yan-Leung Cheung (City University of Hong Kong)
    Yin-Wong Cheung (University of California, Santa Cruz)
    Alan T. K. Wan (City University of Hong Kong)
    URL: http://d.repec.org/n?u=RePEc:hkm:wpaper:032009&r=for
    We observe that daily highs and lows of stock prices do not diverge over time and, hence, adopt the cointegration concept and the related vector error correction model (VECM) to model the daily high, the daily low, and the associated daily range data. The in-sample results attest the importance of incorporating high-low interactions in modeling the range variable. In evaluating the out-of-sample forecast performance using both mean-squared forecast error and direction of change criteria, it is found that the VECM-based low and high forecasts offer some advantages over some alternative forecasts. The VECM-based range forecasts, on the other hand, do not always dominate – the forecast rankings depend on the choice of evaluation criterion and the variables being forecasted.
    Keywords: Daily High, Daily Low, VECM Model, Forecast Performance, Implied Volatility
    JEL: C32
  • Taken from the NEP-FOR mailing list edited by Rob Hyndman.