nep-for New Economics Papers
on Forecasting
Issue of 2020‒05‒25
six papers chosen by
Rob J Hyndman
Monash University

  1. Investing in VIX futures based on rolling GARCH models forecasts By Oleh Bilyk; Paweł Sakowski; Robert Ślepaczuk
  2. CBO's Oil Price Forecasting Record: Working Paper 2020-03 By Ron Gecan
  3. Bayesian dynamic variable selection in high dimensions By Gary Koop; Dimitris Korobilis
  4. Energy Markets and Global Economic Conditions By Christiane Baumeister; Dimitris Korobilis; Thomas K. Lee
  5. Dynamic shrinkage in time-varying parameter stochastic volatility in mean models By Florian Huber; Michael Pfarrhofer
  6. Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data By Emir Zunic; Kemal Korjenic; Kerim Hodzic; Dzenana Donko

  1. By: Oleh Bilyk; Paweł Sakowski (Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw); Robert Ślepaczuk (Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw)
    Abstract: The aim of this work is to compare the performance of VIX futures trading strategies built across different GARCH model volatility forecasting techniques. Long and short signals for VIX futures are produced by comparing one-day ahead volatility forecasts with current historical volatility. We found out that using the daily data over the seven-year period (2013-2019), strategy based on the fGARCH-TGARCH and GJR-GARCH specifications outperformed those of the GARCH and EGARCH models, and performed slightly below the “buy-and-hold” S&P 500 strategy. For the base GARCH(1,1) model, the training window size and the type gave stable results, whereas the performance across refit frequency, conditional distribution of returns, and historical volatility estimators varies significantly. Despite non-robustness of some investment strategies and some space for improvements, the presented strategies show their potential in competing with the equity and volatility benchmarks.
    Keywords: GARCH, VIX index, volatility futures, rolling forecasting, volatility, investment strategies, volatility exposure
    JEL: C4 C45 C61 C15 G14 G17
    Date: 2020
  2. By: Ron Gecan
    Abstract: CBO forecasts benchmark prices of oil to support its economic and budgetary projections. This paper describes the method CBO uses to forecast oil prices and assesses the quality of the agency's projections during the 1993–2019 period, including how that quality compares with that of other forecasts.
    JEL: C00 G17 Q02 Q47
    Date: 2020–05–14
  3. By: Gary Koop; Dimitris Korobilis
    Abstract: This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we specify a new dynamic variable/model selection strategy for TVP dynamic regression models in the presence of a large number of predictors. This strategy allows for assessing in individual time periods which predictors are relevant (or not) for forecasting the dependent variable. The new algorithm is evaluated numerically using synthetic data and its computational advantages are established. Using macroeconomic data for the US we find that regression models that combine time-varying parameters with the information in many predictors have the potential to improve forecasts of price inflation over a number of alternative forecasting models.
    Keywords: dynamic linear model; approximate posterior inference; dynamic variable selection; forecasting
    JEL: C11 C13 C52 C53 C61
    Date: 2020–05
  4. By: Christiane Baumeister; Dimitris Korobilis; Thomas K. Lee
    Abstract: This paper evaluates alternative indicators of global economic activity and other market fundamentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of the most useful indicators that has been proposed in the literature. However, by combining measures from a number of different sources we can do even better. Our analysis results in a new index of global economic conditions and new measures for assessing future tightness of energy demand and expected oil price pressures.
    Keywords: Energy demand, forecasting, stochastic volatility, oil price pressures, petroleum consumption, state of the world economy
    JEL: C11 C32 C52 Q41 Q47
    Date: 2020–02
  5. By: Florian Huber; Michael Pfarrhofer
    Abstract: Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this note, we modify the stochastic volatility in mean (SVM) model proposed in Chan (2017) by introducing state-of-the-art shrinkage techniques that allow for time-variation in the degree of shrinkage. Using a real-time inflation forecast exercise, we show that employing more flexible prior distributions on several key parameters slightly improves forecast performance for the United States (US), the United Kingdom (UK) and the Euro Area (EA). Comparing in-sample results reveals that our proposed model yields qualitatively similar insights to the original version of the model.
    Date: 2020–05
  6. By: Emir Zunic; Kemal Korjenic; Kerim Hodzic; Dzenana Donko
    Abstract: This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.
    Date: 2020–05

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