nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒09‒05
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Modelling and Forecasting Multivariate Realized Volatility By Roxana Chiriac; Valeri Voev
  2. Semiparametric Inference in a GARCH-in-Mean Model By Bent Jesper Christensen; Christian M. Dahl; Emma M. Iglesias
  3. The cyclical component factor model By Christian M. Dahl; Henrik Hansen; John Smidt
  4. Asymmetric Multivariate Normal Mixture GARCH By Markus Haas; Stefan Mittnik; Mark S. Paolella
  5. On The Cyclicality of Real Wages and Wage Differentials By Otrok, Christopher; Pourpourides, Panayiotis M.
  6. Improving forecast accuracy by combining recursive and rolling forecasts By Todd E. Clark; Michael W. McCracken
  7. Averaging forecasts from VARs with uncertain instabilities By Todd E. Clark; Michael W. McCracken

  1. By: Roxana Chiriac; Valeri Voev (School of Economics and Management, University of Aarhus, Denmark)
    Abstract: This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model’s forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model.
    Keywords: Forecasting, Fractional integration, Stochastic dominance, Portfolio optimization, Realized covariance
    JEL: C32 C53 G11
    Date: 2008–09–02
  2. By: Bent Jesper Christensen; Christian M. Dahl; Emma M. Iglesias (School of Economics and Management, University of Aarhus, Denmark)
    Abstract: A new semiparametric estimator for an empirical asset pricing model with general nonpara- metric risk-return tradeoff and a GARCH process for the underlying volatility is introduced. The estimator does not rely on any initial parametric estimator of the conditional mean func- tion, and this feature facilitates the derivation of asymptotic theory under possible nonlinearity of unspecified form of the risk-return tradeoff. Besides the nonlinear GARCH-in-mean effect, our specification accommodates exogenous regressors that are typically used as conditioning variables entering linearly in the mean equation, such as the dividend yield. Using the profile likelihood approach, we show that our estimator under stated conditions is consistent, asymp- totically normal, and efficient, i.e. it achieves the semiparametric lower bound. A sampling experiment provides evidence on finite sample properties as well as comparisons with the fully parametric approach and the iterative semiparametric approach using a parametric initial esti- mate proposed by Conrad and Mammen (2008). An empirical application to the daily S&P 500 stock market returns suggests that the linear relation between conditional expected return and conditional variance of returns from the literature is misspecified, and this could be the reason for the disagreement on the sign of the relation.
    Keywords: Efficiency bound, GARCH-M model, Profile likelihood, Risk-return relation, Semiparametric inference
    JEL: C13 C14 C22 G12
    Date: 2008–09–02
  3. By: Christian M. Dahl; Henrik Hansen; John Smidt (School of Economics and Management, University of Aarhus, Denmark)
    Abstract: Forecasting using factor models based on large data sets have received ample attention due to the models’ ability to increase forecast accuracy with respect to a range of key macroeconomic variables in the US and the UK. However, forecasts based on such factor models do not uniformly outperform the simple autoregressive model when using data from other countries. In this paper we propose to estimate the factors based on the pure cyclical components of the series entering the large data set. Monte Carlo evidence and an empirical illustration using Danish data shows that this procedure can indeed improve on pseudo real time forecast accuracy.
    Keywords: Factor model, Cyclical components, Estimation, Real time forecasting
    JEL: C13 C22 C52
    Date: 2008–09–02
  4. By: Markus Haas (University of Munich, Institute of Statistics); Stefan Mittnik (Department of Statistics, University of Munich, Center for Financial Studies, Frankfurt, and Ifo Institute for Economic Research, Munich); Mark S. Paolella (Swiss Banking Institute, University of Zurich, Switzerland)
    Abstract: An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic correlation structure of the process are provided. In an application to stock market returns, it is shown that the disaggregation of the conditional (co)variance process generated by the model provides substantial intuition. Moreover, the model exhibits a strong performance in calculating out–of–sample Value–at–Risk measures.
    Keywords: Conditional Volatility, Finite Normal Mixtures, Multivariate GARCH, Leverage Effect
    JEL: C32 C51 G10 G11
    Date: 2008–01–18
  5. By: Otrok, Christopher; Pourpourides, Panayiotis M. (Cardiff Business School)
    Abstract: Using longitudinal microdata on real wages we estimate a Bayesian dynamic latent factor model to measure the cyclical properties of real wages. We find that the comovement of real wages can be related to a common factor that exhibits a strong correlation with the national unemployment rate. However, our findings indicate that the common factor explains, on average, no more than 9% of wage variation. Furthermore, roughly half of the wages move procyclically while half move countercyclically. These facts are inconsistent with claims of a strong systematic relationship between real wages and the business cycle. We show that these wage dynamics are consistent with models of labor contracting, and inconsistent with a Walrasian labor market. We also confirm findings of previous studies in which skilled and unskilled wages exhibit roughly the same degree of cyclical variation.
    Keywords: Wages; Wage Differentials; Business Cycles; Bayesian Analysis
    JEL: C11 C13 C22 C23 C81 C82 J31
    Date: 2008–08
  6. By: Todd E. Clark; Michael W. McCracken
    Abstract: This paper presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias-variance tradeoff faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width.
    Keywords: Economic forecasting ; Econometric models
    Date: 2008
  7. By: Todd E. Clark; Michael W. McCracken
    Abstract: Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combining various models of instability in improving VAR forecasts made with real-time data.
    Keywords: Econometric models ; Economic forecasting
    Date: 2008

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