nep-for New Economics Papers
on Forecasting
Issue of 2016‒05‒08
six papers chosen by
Rob J Hyndman
Monash University

  1. Density forecasting comparison of volatility models By Leopoldo Catania; Nima Nonejad
  2. Predicting Recessions With Boosted Regression Trees By Jörg Döpke; Ulrich Fritsche; Christian Pierdzioch
  3. A Framework for Predictive Analysis of Stock Market Indices : A Study of the Indian Auto Sector By Jaydip Sen; Tamal Datta Chaudhuri
  4. Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms By Yongchen Zhao
  5. Dynamic Factor model with infinite dimensional factor space: forecasting By Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi
  6. On the Surprising Explanatory Power of Higher Realized Moments in Practice By Keren Shen; Jianfeng Yao; Wai Keung Li

  1. By: Leopoldo Catania; Nima Nonejad
    Abstract: We compare the predictive ability of several volatility models for a long series of weekly log-returns of the Dow Jones Industrial Average Index from 1902 to 2016. Our focus is particularly on predicting one and multi-step ahead conditional and aggregated conditional densities. Our set of competing models includes: Well-known GARCH specifications, Markov switching GARCH, sempiparametric GARCH, Generalised Autoregressive Score (GAS), the plain stochastic volatility (SV) as well as its more flexible extensions such as SV with leverage, in-mean effects and Student-t distributed errors. We find that: (i) SV models generally outperform the GARCH specifications, (ii): The SV model with leverage effect provides very strong out-of-sample performance in terms of one and multi-steps ahead density prediction, (iii) Differences in terms of Value-at-Risk (VaR) predictions accuracy are less evident. Thus, our results have an important implication: the best performing model depends on the evaluation criterion
    Date: 2016–05
  2. By: Jörg Döpke (University of Applied Sciences Merseburg); Ulrich Fritsche (University Hamburg); Christian Pierdzioch (Helmut-Schmidt-University Hamburg)
    Abstract: We use a machine-learning approach known as Boosted Regression Trees (BRT) to reexamine the usefulness of selected leading indicators for predicting recessions. We estimate the BRT approach on German data and study the relative importance of the indicators and their marginal effects on the probability of a recession. We then use receiver operating characteristic (ROC) curves to study the accuracy of forecasts. Results show that the short-term interest rate and the term spread are important leading indicators, but also that the stock market has some predictive value. The recession probability is a nonlinear function of these leading indicators. The BRT approach also helps to recover how the recession probability depends on the interactions of the leading indicators. While the predictive power of the short-term interest rates has declined over time, the term spread and the stock market have gained in importance. We also study how the shape of a forecaster’s utility function affects the optimal choice of a cutoff value above which the estimated recession probability should be interpreted as a signal of a recession. The BRT approach shows a competitive out-of-sample performance compared to popular Probit approaches
    Keywords: : Recession forecasting; Boosting; Regression trees; ROC analysis
    JEL: C52 C53 E32 E37
    Date: 2015–12
  3. By: Jaydip Sen; Tamal Datta Chaudhuri
    Abstract: Analysis and prediction of stock market time series data has attracted considerable interest from the research community over the last decade. Rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, and availability of high-performance hardware has made it possible to process and analyze high volume stock market time series data effectively, in real-time. Among many other important characteristics and behavior of such data, forecasting is an area which has witnessed considerable focus. In this work, we have used time series of the index values of the Auto sector in India during January 2010 to December 2015 for a deeper understanding of the behavior of its three constituent components, e.g., the trend, the seasonal component, and the random component. Based on this structural analysis, we have also designed five approaches for forecasting and also computed their accuracy in prediction using suitably chosen training and test data sets. Extensive results are presented to demonstrate the effectiveness of our proposed decomposition approaches of time series and the efficiency of our forecasting techniques, even in presence of a random component and a sharply changing trend component in the time-series.
    Date: 2016–04
  4. By: Yongchen Zhao (Towson University)
    Abstract: Based on a set of carefully designed Monte Carlo exercises, this paper document the behavior and performance of several newly developed advanced forecast combination algorithms in unstable environments, where performance of candidate forecasts are cross-sectionally heterogeneous and dynamically evolving over time. Results from these exercises provide guidelines regarding the selection of forecast combination method based on the nature, frequency, and magnitude of instabilities in forecasts as well as the target variable. Following these guidelines, a simple forecast combination exercise using the U.S. Survey of Professional Forecasters, where combined forecasters are shown to have superior performance that is not only statistically significant but also of practical importance.
    Keywords: Forecast combination; exponential re-weighting; shrinkage; estimation error; performance stability; real-time data
    JEL: C53 C22 C15
    Date: 2015–12
  5. By: Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi
    Abstract: The paper compares the pseudo real-time forecasting performance of three Dynamic Factor Models: (i) The standard principal-component model, Stock and Watson (2002a), (ii) The model based on generalized principal components, Forni et al. (2005), (iii) The model recently proposed in Forni et al. (2015) and Forni et al. (2016). We employ a large monthly dataset of macroeconomic and financial time series for the US economy, which includes the Great Moderation, the Great Recession and the subsequent recovery. Using a rolling window for estimation and prediction, we find that (iii) neatly outperforms (i) and (ii) in the Great Moderation period for both Industrial Production and Inflation, and for Inflation over the full sample. However, (iii) is outperformed by (i) and (ii) over the full sample for Industrial Production.
    Date: 2016–04
  6. By: Keren Shen; Jianfeng Yao; Wai Keung Li
    Abstract: Realized moments of higher order computed from intraday returns are introduced in recent years. The literature indicates that realized skewness is an important factor in explaining future asset returns. However, the literature mainly focuses on the whole market and on the monthly or weekly scale. In this paper, we conduct an extensive empirical analysis to investigate the forecasting abilities of realized skewness and realized kurtosis towards individual stock's future return and variance in the daily scale. It is found that realized kurtosis possesses significant forecasting power for the stock's future variance. In the meanwhile, realized skewness is lack of explanatory power for the future daily return for individual stocks with a short horizon, in contrast with the existing literature.
    Date: 2016–04

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