nep-for New Economics Papers
on Forecasting
Issue of 2018‒03‒12
eight papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting GDP of OPEC: The role of oil price By Afees A. Salisu; Umar B. Ndako; Idris Adediran
  2. Forecasting Stock Returns: A Predictor-Constrained Approach By Davide Pettenuzzo; Zhiyuan Pan; Yudong Wang
  3. Forecasting CO2 emissions: Does the choice of estimator matter? By Afees A. Salisu; Lateef O. Akanni; Ahamuefula Ephraim Ogbonna
  4. A novel approach to modelling the distribution of financial returns By Yuzhi Cai; Guodong Li
  5. Forecasting Methods in Finance By Timmermann, Allan G
  6. The threshold GARCH model: estimation and density forecasting for financial returns By Yuzhi Cai; Julian Stander
  7. Forecasting Inflation Uncertainty in the G7 Countries By Mawuli Segnon; Stelios Bekiros; Bernd Wilfling
  8. Risk Everywhere: Modeling and Managing Volatility By Bollerslev, Tim; Hood, Benjamin; Huss, John; Pedersen, Lasse Heje

  1. By: Afees A. Salisu; Umar B. Ndako (Monetary Policy Department, Central Bank of Nigeria, Nigeria.); Idris Adediran (Department of Economics, Obafemi Awolowo University, Nigeria.)
    Abstract: In this paper, we examine the role of oil in GDP forecast of selected OPEC member countries using the Autoregressive Distributed Lag Mixed Data Sampling (ADL-MIDAS) approach. Both the in-sample and out-of-sample forecasts of this approach are evaluated and compared with some competing models namely AR(1), ARFIMA, ARIMA and ARDL models. We find that allowing for high frequency oil price data in the predictive model of GDP will enhance its forecast performance. The ADL-MIDAS is found to out-perform all the competing models for both the in-sample and out-of-sample forecast. In addition, we find that the higher the data frequency of oil price, the better the forecast performance. These results are robust to different data frequencies, multiple forecast horizons, and alternative proxies for oil price and measures of forecast performance.
    Keywords: Oil price; GDP, ADL-MIDAS; Linear time series models; Forecast evaluation
    JEL: C12 C22 Q42 Q43 Q47
    Date: 2018–02
  2. By: Davide Pettenuzzo (Brandeis University); Zhiyuan Pan (Southwestern University of Finance and Economics, Institute of Chinese Financial Studies); Yudong Wang (School of Economics and Management, Nanjing University of Science, Technology, and Economics)
    Abstract: We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it at zero whenever its value falls below the variable's past 12-month high. Empirically, we find that relative to standard unconstrained predictive regressions, our approach leads tosignificantly larger forecasting gains, both in statistical and economic terms. We also show how a simple equal-weighted combination of the constrained forecasts leads to further improvements in forecast accuracy, with predictions that are more precise than those obtained either using the Campbell and Thompson (2008) or Pettenuzzo, Timmermann, and Valkanov (2014) methods. Subsample analysis and a large battery of robustness checks confirm that these findings are robust to the presence of model instabilities and structural breaks.
    Keywords: Equity premium; Predictive regressions; Predictor constraints; 24-month high and low; Model combinations
    JEL: C11 C22 G11 G12
    Date: 2017–10
  3. By: Afees A. Salisu; Lateef O. Akanni (Department of Economics, University of Lagos,Akoka, Lagos, Nigeria); Ahamuefula Ephraim Ogbonna (Centre for Econometric and Allied Research, University of Ibadan)
    Abstract: Extant studies in the literature on carbon emissions have done so using numerous methodologies. However, the Environmental Kuznets Curve has remained the workhorse for modelling the link between development and emissions. This study sets out to test the predictability of the EKC hypothesis for CO2 emissions in the US and consequently offers to answer two key questions. First, does the choice of estimator matter for the predictability of EKC in forecasting CO2 emissions? Second, are the results sensitive to any of the following: measures of CO2 emission and output and multiple forecast periods? The results uphold the stance of the inverted U-shaped relationship postulated by the EKC hypothesis. Also, the choice of estimator matters for accurate forecast performance of EKC for CO2 measures. More importantly, any estimator that ignores the inherent statistical properties of the predictors such as endogeneity, conditional heteroscedasticity and persistence, among others, may produce less desirable forecasts than the time series models. This conclusion is valid regardless of the proxies for CO2 emissions and output.
    Keywords: US, Environmental Kuznets Curve, CO2 Emissions, Forecast evaluation
    JEL: C53 Q51
    Date: 2018–02
  4. By: Yuzhi Cai (School of Management, Swansea University); Guodong Li (University of Hong Kong)
    Abstract: We develop a novel quantile function threshold GARCH model for studying the distribution function, rather than the volatility function, of financial returns that follow a threshold GARCH model. We propose a Bayesian method to do estimation and forecasting simultaneously, which allows us to handle multiple thresholds easily and ensures the forecasts can take account of the variation of model parameters. We apply the method to simulated data and Nasdaq returns. We show that our model is robust to model specification errors and outperforms some commonly used threshold GARCH models.
    Keywords: Density forecasts, financial returns, quantile function, threshold GARCH
    JEL: C10 C51 C53
    Date: 2018–02–27
  5. By: Timmermann, Allan G
    Abstract: Our review highlights some of the key challenges in financial forecasting problems along with opportunities arising from the unique features of financiall data. We analyze the difficulty of establishing predictability in an environment with a low signal-to-noise ratio, persistent predictors, and instability in predictive relations arising from competitive pressures and investors' learning. We discuss approaches for forecasting the mean, variance, and probability distribution of asset returns. Finally, we cover how to evaluate financial forecasts while accounting for the possibility that numerous forecasting models may have been considered, leading to concerns of data mining.
    Date: 2018–02
  6. By: Yuzhi Cai (School of Management, Swansea University); Julian Stander (Plymouth University)
    Abstract: This paper develops a novel density forecasting method for financial time series following a threshold GARCH model that does not require the estimation of the model itself. Instead, Bayesian inference is performed about an induced multiple threshold one-step ahead value-at-risk process at a single quantile level. This is achieved by a quasi-likelihood approach that uses quantile information. We describe simulation studies that provide insight into our method and illustrate it using empirical work on market returns. The results show that our forecasting method outperforms some benchmark models for density forecasting of financial returns.
    Keywords: Density forecasting, multiple thresholds, one-step ahead value-at-risk (VaR), quantile regression, quasi-likelihood.
    JEL: C1 C5
    Date: 2018–02–27
  7. By: Mawuli Segnon; Stelios Bekiros; Bernd Wilfling
    Abstract: There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [STARFIMA(p; d; q)-MSM(k)] for modeling and forecasting inflation uncertainty. We first provide the statistical properties of the process and investigate the finite-sample properties of the maximum likelihood estimators through simulation. Second, we evaluate the out-of-sample forecast performance of the model in forecasting inflation uncertainty in the G7 countries. Our empirical analysis demonstrates the superiority of the new model over the alternative STARFIMA(p; d; q)-GARCH-type models in forecasting inflation uncertainty.
    Keywords: Inflation uncertainty, Smooth transition, Multifractal processes, GARCH processes
    JEL: C22 E31
    Date: 2018–03
  8. By: Bollerslev, Tim; Hood, Benjamin; Huss, John; Pedersen, Lasse Heje
    Abstract: Based on a unique high-frequency dataset for more than fifty commodities, currencies, equity indices, and fixed income instruments spanning more than two decades, we document strong similarities in realized volatilities patterns across assets and asset classes. Exploiting these similarities within and across asset classes in panel-based estimation of new realized volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and more conventional procedures that do not incorporate the information in the high-frequency intraday data and/or the similarities in the volatilities. A utility-based framework designed to evaluate the economic gains from risk modeling highlights the interplay between parsimony of model specification, transaction costs, and speed of trading in the practical implementation of the different risk models.
    Keywords: high-frequency data; Market and volatility risk; realized utility; realized volatility; risk modeling and forecasting; risk targeting; volatility trading
    JEL: C22 C51 C53 C58
    Date: 2018–02

This nep-for issue is ©2018 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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.