nep-for New Economics Papers
on Forecasting
Issue of 2022‒02‒07
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Stock Market Volatility with Regime-Switching GARCH-MIDAS: The Role of Geopolitical Risks By Mawuli Segnon; Rangan Gupta
  2. Do inflation expectations improve model-based inflation forecasts? By Bańbura, Marta; Leiva-León, Danilo; Menz, Jan-Oliver
  3. The DONUT Approach to EnsembleCombination Forecasting By Lars Lien Ankile; Kjartan Krange
  4. Predicting housing prices. A long term housing price path for Spanish regions By Paloma Taltavull de La Paz
  5. LSTM Architecture for Oil Stocks Prices Prediction By Javad T. Firouzjaee; Pouriya Khaliliyan

  1. By: Mawuli Segnon (Department of Economics, Institute for Econometric and Economic Statistics and Chair of Empirical Economics, University of Munster, Germany); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: We investigate the role of geopolitical risks (GPR) in forecasting stock market volatility in a robust autoregressive Markov-switching GARCH mixed data sampling (ARMSGARCH-MIDAS) framework that accounts for structural breaks through regime switching and allows us to disentangle short- and long-run volatility components driven by geopolitical risks. An empirical out-of-sample forecasting exercise is conducted using unique data sets on Dow Jones Industrial Average (DJIA) index and geopolitical risks that cover the time period from January 3, 1899 to December 31, 2020. We find that geopolitical risks as explanatory variables can help to improve the accuracy of stock market volatility forecasts. Furthermore, our empirical results show that the macroeconomic variables such as output measured by recessions, inflation and interest rates contain information that is complementary to the one included in the geopolitical risks.
    Keywords: Geopolitical risks, Volatility forecasts, Markov-switching GARCH-MIDAS
    JEL: C52 C53 C58
    Date: 2022–01
  2. By: Bańbura, Marta; Leiva-León, Danilo; Menz, Jan-Oliver
    Abstract: Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB's SPF and Consensus Economics compared to their counterparts that do not. The gains in forecast accuracy from incorporating inflation expectations are typically not large but significant in some periods. Both short- and long-term expectations provide useful information. By contrast, incorporating expectations derived from financial market prices or those of firms and households does not lead to systematic improvements in forecast performance. Individual models we consider are typically better than univariate benchmarks but for the euro area the professional forecasters are more accurate, especially in recent years (not always for the countries). The analysis is undertaken for headline inflation and inflation excluding energy and food and both point and density forecast are evaluated using real-time data vintages over 2001-2019.
    Keywords: Forecasting,Inflation,Inflation expectations,Phillips curve,BayesianVAR
    JEL: C53 E31 E37
    Date: 2021
  3. By: Lars Lien Ankile; Kjartan Krange
    Abstract: This paper presents an ensemble forecasting method that shows strong results on the M4Competition dataset by decreasing feature and model selection assumptions, termed DONUT(DO Not UTilize human assumptions). Our assumption reductions, consisting mainly of auto-generated features and a more diverse model pool for the ensemble, significantly outperforms the statistical-feature-based ensemble method FFORMA by Montero-Manso et al. (2020). Furthermore, we investigate feature extraction with a Long short-term memory Network(LSTM) Autoencoder and find that such features contain crucial information not captured by traditional statistical feature approaches. The ensemble weighting model uses both LSTM features and statistical features to combine the models accurately. Analysis of feature importance and interaction show a slight superiority for LSTM features over the statistical ones alone. Clustering analysis shows that different essential LSTM features are different from most statistical features and each other. We also find that increasing the solution space of the weighting model by augmenting the ensemble with new models is something the weighting model learns to use, explaining part of the accuracy gains. Lastly, we present a formal ex-post-facto analysis of optimal combination and selection for ensembles, quantifying differences through linear optimization on the M4 dataset. We also include a short proof that model combination is superior to model selection, a posteriori.
    Date: 2022–01
  4. By: Paloma Taltavull de La Paz
    Abstract: This paper aims to forecast the long term trend of housing prices in the Spanish cities with more than 25 thousand inhabitants, a total of 275 individual municipalities. Based on a causal model explaining housing prices based on six fundamental variables (changes in population, income, number of mortgages, interest rates, vacant and housing prices), a pool VECM technique is used to estimate a housing price model and calculate the 'stable long term price', a central concept defined in the formal valuation process. The model covers the period 1995-2020, and the long term is approached from 2000 to 2026, so the prediction exercise includes backcast and forecast period allowing to extract the long term cycle housing price have followed during last 20 years and project it further six years. The analytical process follows three steps. Firstly, it identifies the cities following a common pattern in their housing market by clustering twice the cities: (1) using house price time series and (2) using a machine learning approach with the six fundamental variables. Results give a comprehensible evolution of the long term component of housing prices and the model also permits the understanding of the main drivers of housing prices in each Spanish region. Clustering cities with two statistical tools give pretty similar results in some cities but is different in others. The challenge of finding the correct grouping is critical to understanding the housing market and forecasting their prices.
    Keywords: Error correction models; Forecast; Housing Prices; Housing valuation; Machine Learning; Time Series
    JEL: R3
    Date: 2021–01–01
  5. By: Javad T. Firouzjaee; Pouriya Khaliliyan
    Abstract: Oil companies are among the largest companies in the world whose economic indicators in the global stock market have a great impact on the world economy and market due to their relation to gold, crude oil, and the dollar. To quantify these relations we use the correlation feature and the relationships between stocks with the dollar, crude oil, gold, and major oil company stock indices, we create datasets and compare the results of forecasts with real data. To predict the stocks of different companies, we use Recurrent Neural Networks (RNNs) and LSTM, because these stocks change in time series. We carry on empirical experiments and perform on the stock indices dataset to evaluate the prediction performance in terms of several common error metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The received results are promising and present a reasonably accurate prediction for the price of oil companies' stocks in the near future. The results show that RNNs do not have the interpretability, and we cannot improve the model by adding any correlated data.
    Date: 2022–01

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