nep-for New Economics Papers
on Forecasting
Issue of 2019‒01‒21
five papers chosen by
Rob J Hyndman
Monash University

  1. Quantile-based Inflation Risk Models By Eric Ghysels; Leonardo Iania; Jonas Striaukas
  2. Model instability in predictive exchange rate regressions By Niko Hauzenberger; Florian Huber
  3. Forecasting economic decisions under risk: The predictive importance of choice-process data By Steffen Q. Mueller; Patrick Ring; Maria Schmidt
  4. New testing approaches for mean-variance predictability By Gabriele Fiorentini; Enrique Sentana
  5. Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson‐Siegel Models By Massimo Guidolin; Manuela Pedio

  1. By: Eric Ghysels (Department of Economics and Kenan-Flagler Business School, University of North Carolina Chapel Hill and CEPR.); Leonardo Iania (Louvain School of Management and IMMAQ (CORE and LFIN), Universite catholique de Louvain.); Jonas Striaukas (Universite catholique de Louvain. Research Fellow at F.R.S. - FNRS)
    Abstract: This paper proposes a new approach to extract quantile-based in ation risk mea- sures using Quantile Autoregressive Distributed Lag Mixed-Frequency Data Sampling (QADL-MIDAS) regression models. We compare our models to a standard Quantile Auto-Regression (QAR) model and show that it delivers better quantile forecasts at several forecasting horizons. We use the QADL-MIDAS model to construct in ation risk measures proxying for uncertainty, third-moment dynamics and the risk of ex- treme in ation realizations. We nd that these risk measures are linked to the future evolution of in ation and changes in the e ective federal funds rate.
    Keywords: regression quantiles, in ation risk, quantile forecasting
    JEL: C53 C54 E37
    Date: 2018–10
  2. By: Niko Hauzenberger (Vienna University of Economics and Business, Department of Economics); Florian Huber (Paris Lodron University of Salzburg, Salzburg Centre of European Union Studies)
    Abstract: In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered.
    Keywords: Empirical exchange rate models, exchange rate fundamentals, Markov switching
    JEL: C30 E32 E52 F31
    Date: 2018–12
  3. By: Steffen Q. Mueller (Chair for Economic Policy, University of Hamburg); Patrick Ring (Social and Behavioral Approaches to Global Problems, Kiel Institute for the World Economy); Maria Schmidt (Department of Psychology, Kiel University)
    Abstract: We investigate various statistical methods for forecasting risky choices and identify important decision predictors. Subjects (n=44) are presented a series of 50/50 gambles that each involves a potential gain and a potential loss, and subjects can choose to either accept or reject a displayed lottery. From this data, we use information on 8800 individual lottery gambles and specify four predictor-sets that include different combinations of input categories: lottery design, socioeconomic characteristics, past gambling behavior, eye-movements, and various psychophysiological measures that are recorded during the first three seconds of lottery-information processing. The results of our forecasting experiment show that choice-process data can effectively be used to forecast risky gambling decisions; however, we find large differences among models’ forecasting capabilities with respect to subjects, predictor-sets, and lottery payoff structures.
    Keywords: Forecasting, lottery, risk, choice-process tracing, experiments, machine learning, decision theory
    JEL: C44 C45 C53 D87 D91
    Date: 2019–01–11
  4. By: Gabriele Fiorentini (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Enrique Sentana (Cemfi, Madrid, Spain)
    Abstract: We propose tests for smooth but persistent serial correlation in risk premia and volatilities that exploit the non-normality of financial returns. Our parametric tests are robust to distributional misspecification, while our semiparametric tests are as powerful as if we knew the true return distribution. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises assess their finite sample reliability. We apply our tests to quarterly returns on the five Fama-French factors for international stocks, whose distributions are mostly symmetric and fat-tailed. Our results highlight noticeable differences across regions and factors and confirm the fragility of Gaussian tests.
    Keywords: Financial forecasting, Moment tests, Misspecification, Robustness, Volatility.
    JEL: C12 C22 G17
    Date: 2019–01
  5. By: Massimo Guidolin; Manuela Pedio
    Abstract: We use monthly data on the US riskless yield curve for a 1982-2015 sample to show that mixing simple regime switching dynamics with Nelson-Siegel factor forecasts from time series models extended to encompass variables that summarize the state of monetary policy, leads to superior predictive accuracy. Such spread in forecasting power turns out to be statistically significant even controlling for parameter uncertainty and sample variation. Exploiting regimes, we obtain evidence that the increase in predictive accuracy is stronger during the Great Financial Crisis in 2007-2009, when monetary policy underwent a significant, sudden shift. Although more caution applies when transaction costs are accounted for, we also report that the increase in predictive power owed to the combination of regimes and of monetary variables that capture the stance of unconventional monetary policies is tradeable. We devise and test butterfly strategies that trade on the basis of the forecasts from the models and obtain evidence of riskadjusted profits both per se and in comparisons to simpler models. Key words: Term structure of interest rates, Dynamic Nelson-Siegel factors, regime switching, butterfly strategies, unconventional monetary policy.
    Date: 2019

This nep-for issue is ©2019 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.