nep-for New Economics Papers
on Forecasting
Issue of 2007‒08‒27
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting recessions: the puzzle of the enduring power of the yield curve By Glenn D. Rudebusch; John C. Williams
  2. Forecasting realized volatility models:the benefits of bagging and nonlinear specifications By Eric Hillebrand; Marcelo Cunha Medeiros
  3. Exchange Rate Models Are Not as Bad as You Think By Charles Engel; Nelson C. Mark; Kenneth D. West
  4. Modeling and predicting the CBOE market volatility index By Marcelo Fernandes; Marcelo Cunha Medeiros; MArcelo Scharth
  5. Investigating time-variation in the marginal predictive power of the yield spread. By Luca Benati; Charles Goodhart

  1. By: Glenn D. Rudebusch; John C. Williams
    Abstract: We show that professional forecasters have essentially no ability to predict future recessions a few quarters ahead. This is particularly puzzling because, for at least the past two decades, researchers have provided much evidence that the yield curve, specifically the spread between long- and short-term interest rates, does contain useful information at that forecast horizon for predicting aggregate economic activity and, especially, for signaling future recessions. We document this puzzle and suggest that forecasters have generally placed too little weight on yield curve information when projecting declines in the aggregate economy.
    Keywords: Economic forecasting ; Recessions
    Date: 2007
  2. By: Eric Hillebrand (DEPARTMENT OF ECONOMICS, LOUISIANA STATE UNIVERSITY); Marcelo Cunha Medeiros (Department of Economics, PUC-Rio)
    Abstract: We forecast daily realized volatilities with linear and nonlinear models and evaluate the benefits of bootstrap aggregation (bagging) in producing more precise forecasts. We consider the linear autoregressive (AR) model, the Heterogeneous Autoregressive model (HAR), and a non-linear HAR model based on a neural network specification that allows for logistic transition effects (NNHAR). The models and the bagging schemes are applied to the realized volatility time series of the S&P500 index from 3-Jan-2000 through 30-Dec-2005. Our main findings are: (1) For the HAR model, bagging successfully averages over the randomness of variable selection; however, when the NN model is considered, there is no clear benefit from using bagging; (2) including past returns in the models improves the forecast precision; and (3) the NNHAR model outperforms the linear alternatives.
    Date: 2007–08
  3. By: Charles Engel; Nelson C. Mark; Kenneth D. West
    Abstract: Standard models of exchange rates, based on macroeconomic variables such as prices, interest rates, output, etc., are thought by many researchers to have failed empirically. We present evidence to the contrary. First, we emphasize the point that "beating a random walk" in forecasting is too strong a criterion for accepting an exchange rate model. Typically models should have low forecasting power of this type. We then propose a number of alternative ways to evaluate models. We examine in-sample fit, but emphasize the importance of the monetary policy rule, and its effects on expectations, in determining exchange rates. Next we present evidence that exchange rates incorporate news about future macroeconomic fundamentals, as the models imply. We demonstrate that the models might well be able to account for observed exchange-rate volatility. We discuss studies that examine the response of exchange rates to announcements of economic data. Then we present estimates of exchange-rate models in which expected present values of fundamentals are calculated from survey forecasts. Finally, we show that out-of-sample forecasting power of models can be increased by focusing on panel estimation and long-horizon forecasts.
    JEL: F31 F41
    Date: 2007–08
  4. By: Marcelo Fernandes (Queen Mary, University of London); Marcelo Cunha Medeiros (Department of Economics, PUC-Rio); MArcelo Scharth
    Abstract: This paper performs a thorough statistical examination of the time-series properties of the market volatility index (VIX) from the Chicago Board Options Exchange (CBOE). The motivation lies on the widespread consensus that the VIX is a barometer to the overall market sentiment as to what concerns risk appetite. To assess the statistical behavior of the time series, we run a series of preliminary analyses whose results suggest there is some long-range dependence in the VIX index. This is consistent with the strong empirical evidence in the literature supporting long memory in both options-implied and realized volatilities. We thus resort to linear and nonlinear heterogeneous autoregressive (HAR) processes, including smooth transition and threshold HAR-type models, as well as to smooth transition autoregressive trees (START) for modeling and forecasting purposes. The in-sample results for the HAR-type indicate that they cope with the long-range dependence in the VIX time series as well as the more popular ARFIMA model. In addition, the highly nonlinear START specification also does a god job in controlling for the long memory. The out-of-sample analysis evince that the linear ARMA and ARFIMA models perform very well in the short run and very poorly in the long-run, whereas the START model entails by far the best results for the longer horizon despite of failing at shorter horizons. In contrast, the HAR-type models entail reasonable relative performances in most horizons. Finally, we also show how a simple forecast combination brings about great improvements in terms of predictive ability for most horizons.
    Keywords: heterogeneous autoregression, implied volatility, smooth transition, VIX.
    JEL: G12 C22 C53 E44
    Date: 2007–08
  5. By: Luca Benati (European Central Bank, Kaiserstraße 29, 60311 Frankfurt, Germany.); Charles Goodhart (London School of Economics and Political Science, Room R414, Houghton Street, London WC2A 2AE, United Kingdom.)
    Abstract: We use Bayesian time-varying parameters VARs with stochastic volatility to investigate changes in the marginal predictive content of the yield spread for output growth in the United States and the United Kingdom, since the Gold Standard era, and in the Eurozone, Canada, and Australia over the post-WWII period. Overall, our evidence does not provide much support for either of the two dominant explanations why the yield spread may contain predictive power for output growth, the monetary policy-based one, and Harvey’s (1988) ‘real yield curve’ one. Instead, we offer a new conjecture. Journal of Economic Dynamics and Control, forthcoming. JEL Classification: E42, E43, E47.
    Keywords: Bayesian VARs, stochastic volatility, time-varying parameters, medianunbiased estimation, Monte Carlo integration.
    Date: 2007–08

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