nep-rmg New Economics Papers
on Risk Management
Issue of 2006‒02‒26
six papers chosen by
Stan Miles
York University

  1. Investment and Uncertainty By Christopher F. Baum; Mustafa Caglayan; Oleksandr Talavera
  2. The Dog That Did Not Bark: A Defense of Return Predictability By John H. Cochrane
  3. Five Open Questions About Prediction Markets By Justin Wolfers; Eric Zitzewitz
  4. Modelling Memory of Economic and Financial Time Series By Peter M Robinson
  5. Understanding and Forecasting Stock Price Changes By Pedro N. Rodríguez,; Simón Sosvilla-Rivero
  6. An Elementary Model of Price Dynamics in a Financial Market Distribution, Multiscaling & Entropy By Stefan Reimann

  1. By: Christopher F. Baum (Boston College); Mustafa Caglayan (University of Glasgow); Oleksandr Talavera (DIW Berlin)
    Abstract: In this paper we investigate the linkages between firms' capital investment behavior and uncertainty. In our empirical investigation, we use measures of uncertainty derived from firms' daily stock returns and S\&P 500 index returns along with a CAPM-based risk measure. Using a panel of U.S. manufacturing firm data obtained from COMPUSTAT over the 1984-2003 period, we specifically find that increases in both intrinsic and CAPM-based measures of uncertainty have a significant negative impact on firms' investment spending. Our investigation also provides evidence that the relationship is nonlinear and more complex than previously considered.
    Keywords: capital investment, uncertainty, CAPM, dynamic panel data
    JEL: E22 D81 C23
    Date: 2006–02–15
  2. By: John H. Cochrane
    Abstract: To question the statistical significance of return predictability, we cannot specify a null that simply turns off that predictability, leaving dividend growth predictability at its essentially zero sample value. If neither returns nor dividend growth are predictable, then the dividend-price ratio is a constant. If the null turns off return predictability, it must turn on the predictability of dividend growth, and then confront the evidence against such predictability in the data. I find that the absence of dividend growth predictability gives much stronger statistical evidence against the null, with roughly 1-2% probability values, than does the presence of return predictability, which only gives about 20% probability values. I argue that tests based on long-run return and dividend growth regressions provide the cleanest and most interpretable evidence on return predictability, again delivering about 1-2% probability values against the hypothesis that returns are unpredictable. I show that Goyal and Welch's (2005) finding of poor out-of-sample R2 does not reject return forecastability. Out-of-sample R2 is poor even if all dividend yield variation comes from time-varying expected returns.
    JEL: G0 G1
    Date: 2006–02
  3. By: Justin Wolfers; Eric Zitzewitz
    Abstract: Interest in prediction markets has increased in the last decade, driven in part by the hope that these markets will prove to be valuable tools in forecasting, decision-making and risk management -- in both the public and private sectors. This paper outlines five open questions in the literature, and we argue that resolving these questions is crucial to determining whether current optimism about prediction markets will be realized.
    JEL: C9 D7 D8 G1 M2
    Date: 2006–02
  4. By: Peter M Robinson
    Abstract: Much time series data are recorded on economic and financial variables. Statistical modelling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of 'memory', or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are discussed.
    Keywords: Long memory, short memory, stochastic volatility
    JEL: C22
    Date: 2005–03
  5. By: Pedro N. Rodríguez,; Simón Sosvilla-Rivero
    Abstract: Previous empirical studies have shown that predictive regressions in which model uncertainty is assessed and propagated generate desirable properties when predicting out-of-sample. However, it is still not clear (a) what the important conditioning variables for predicting stock returns out-of-sample are, and (b) how composite weighted ensembles outperform model selection criteria. By comparing the unconditional accuracy of prediction regressions to the conditional accuracy conditioned on specific explanatory variables masked), we find that cross-sectional premium and term spread are robust predictors of future stock returns. Additionally, using the bias-variance decomposition for the 0/1 loss function, the analysis shows that lower bias, and not lower variance, is the fundamental difference between composite weighted ensembles and model selection criteria. This difference, nevertheless, does not necessarily imply that model averaging techniques improve our ability to describe monthly up-and-down movements' behavior in stock markets.
  6. By: Stefan Reimann
    Abstract: Stylized facts of empirical assets log-returns include the existence of semi heavy tailed distributions and a non-linear spectrum of Hurst exponents. Empirical data considered are daily prices from 10 large indices from 01/01/1990 to 12/31/2004. We propose a stylized model of price dynamics which is driven by expectations. The model is a multiplicative random process with a stochastic, state-dependent growth rate which establishes a negative feedback component in the price dynamics. This 0-order model implies that the distribution of log-returns is Laplacian, whose single parameter can be regarded as a measure for the long-time averaged liquidity in the respective market. A comparison with the (more general) Weibull distribution shows that empirical log returns are close to being Laplacian distributed. The spectra of Hurst exponents of both, empirical data and simulated data due to our model, are compared. Due to the finding of non-linear Hurst spectra, the Renyi entropy is considered. An explicit functional form of the RE for an exponential distribution is derived. Theoretical of simulated asset return trails are in good agreement with the estimated from empirical returns.
    Keywords: stylized facts, empirical asset returns, multiscaling, Renyi information
    JEL: C22 C5 G14
    Date: 2006–02

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