nep-for New Economics Papers
on Forecasting
Issue of 2006‒05‒20
four papers chosen by
Rob J Hyndman
Monash University

  1. Generalized Dynamic Factor Model + GARCH <br> Exploiting Multivariate Information for Univariate Prediction By Lucia Alessi; Matteo Barigozzi; Marco Capasso
  2. A Mixture Multiplicative Error Model for Realized Volatility By Markku Lanne
  3. Institutional and Individual Sentiment: Smart Money and Noise Trader Risk By Schmeling, Maik
  4. Currency hedging of global portfolios - a closer examination of some of the ingredients By D. Johannes Juttner; Wayne Leung

  1. By: Lucia Alessi; Matteo Barigozzi; Marco Capasso
    Abstract: We propose a new model for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM)and the GARCH model. The GDFM, applied to a huge number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns. In this financial analysis, both these components are modeled as a GARCH. We compare GDFM+GARCH and standard GARCH performance on samples up to 475 series, predicting both levels and volatility of returns. While results on levels are not significantly different, on volatility the GDFM+GARCH model outperforms the standard GARCH in most cases. These results are robust with respect to different volatility proxies.
    Keywords: Dynamic Factors, GARCH, Volatility Forecasting
    Date: 2006–05–13
  2. By: Markku Lanne
    Abstract: A multiplicative error model with time-varying parameters and an error term following a mixture of gamma distributions is introduced. The model is fitted to the daily realized volatility series of Deutschemark/Dollar and Yen/Dollar returns and is shown to capture the conditional distribution of these variables better than the commonly used ARFIMA model. The forecasting performance of the new model is found to be, in general, superior to that of the set of volatility models recently considered by Andersen et al. (2003) for the same data.
    Keywords: Mixture model, Realized volatility, Gamma distribution
    JEL: C22 C52 C53 G15
    Date: 2006
  3. By: Schmeling, Maik
    Abstract: Using a new data set on investor sentiment we show that institutional and individual sentiment proxy for smart money and noise trader risk, respectively. First, using bias-adjusted long-horizon regressions, we document that institutional sentiment forecasts stock market returns at intermediate horizons correctly, whereas individuals consistently get the direction wrong. Second, VEC models show that institutional sentiment forecasts mean-reversion whereas individuals forecast trend continuation. Finally, institutional investors take into account expected individual sentiment when forming their expectations in a way that higher (lower) expected sentiment of individuals lowers (increases) institutional return forecasts. Individuals neglect the information contained in institutional sentiment.
    Keywords: investor sentiment, predictive regressions, noise trader, smart money
    JEL: G11 G12 G14
    Date: 2006–05
  4. By: D. Johannes Juttner (Department of Economics, Macquarie University); Wayne Leung (Department of Economics, Macquarie University)
    Abstract: The paper analyzes some of the ingredients of currency hedging and portfolio construction against the framework of mean-variance efficient portfolios. The currency hedging analysis is based on a range of exchange rates, consisting of the domestic dollar vis-à-vis the US dollar, the euro, the yen, the pound and Hong Kong dollar mainly from an Australian perspective. Our analysis focuses on the following input factors into the hedging process of foreign assets/liabilities. We explore the implications of the secular downward trend of the real trade-weighted exchange rate index of the domestic dollar for hedging effectiveness. The hedging costs resulting from unexpected cash flows and portfolio adjustments are in part estimated through a simulated forward contract hedging technique. The relevant inputs into the variance-covariance matrix of the optimal portfolio selection process are estimated on the basis of historical data. Comparing the forecast errors of share index and currency volatilities, using historical, implied and GARCH methods, provides mixed results. The paper also investigates a select number of forecasting methods that may be applied to other hedging inputs.
    Date: 2004–10

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