New Economics Papers
on Risk Management
Issue of 2005‒02‒06
three papers chosen by

  1. Sources of Predictability of European Stock Markets for High-Technology Firms By Christian Pierdzioch; Andrea Schertler
  3. A better asymmetric model of changing volatility in stock returns: Trend-GARCH By Christian Bauer

  1. By: Christian Pierdzioch; Andrea Schertler
    Abstract: We study return predictability of stock indexes of blue chip firms and smaller hightechnology firms in Germany, France, and the United Kingdom during the second half of the 1990s. We measure return predictability in terms of first-order autocorrelation coefficients, and find evidence for return predictability of stock indexes of smaller hightechnology firms, but no evidence for return predictability of stock indexes of blue chip firms. Our findings suggest that a leading candidate for explaining the economic sources of return predictability of stock indexes of smaller high-technology firms is transaction
    Keywords: Stock markets; Return predictability; High-technology firms
    JEL: G14 N24
    Date: 2005–01
  2. By: Francisco Peñaranda (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: Much of the growing literature on tactical and strategic asset allocation uses vector autoregressive models (VAR) for returns and predictors. Since the portfolio advice they generate may be misleading if those models are not an accurate description of reality, we evaluate the implied joint density forecasts of US monthly excess returns on stocks and bonds. From the point of view of an investor who rebalances monthly, a VAR offers a reasonable description of the data, which is not improved upon by richer models. We also study the relevance of considering time-varying risk premia and parameter uncertainty in density forecasts.
    Keywords: Density forecasts, parameter uncertainty, portfolio choice, probability integral transform, risk premia.
    JEL: G11 C53
    Date: 2004–11
  3. By: Christian Bauer
    Abstract: In this paper we consider the theoretical and empirical relevance of a new family of conditionally heteroskedastic models with a trend dependent conditional variance equation: the Trend-GARCH model. The interest in these models lies in the fact that modern microeco- nomic theory often suggests the connection between the past behavior of time series and the subsequent reaction of market individuals and thereon changes in the future characteristics of the time series. Our results reveal important properties of these models, which are con- sistent with stylized facts in ?financial data sets. They can also be employed for model identifi?cation, estimation, and testing. The em- pirical analysis of a broad variety of asset prices signi?ficantly supports the existence of trend effects. The Trend-GARCH model proves to be superior to alternative models such as EGARCH, AGARCH, or TGARCH in replicating the leverage effect in the conditional variance and in fi?tting the news impact curve.
    Keywords: GARCH, trend, volatility, news impact curve
    JEL: C22 C52 G12
    Date: 2005–02

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.