nep-for New Economics Papers
on Forecasting
Issue of 2020‒03‒09
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Realized Volatility Matrix With Copula-Based Models By Wenjing Wang; Minjing Tao
  2. Do Financial Analysts Herd? By Jin Yeub Kim; Yongjun Kim; Myungkyu Shim
  3. On the external validity of experimental inflation forecasts : a comparison with five categories of field expectations By Camille Cornand; Julien Pillot
  4. Tourism Demand Forecasting with Tourist Attention: An Ensemble Deep Learning Approach By Shaolong Sun; Yanzhao Li; Shouyang Wang; Ju-e Guo
  5. Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach By Shaolong Suna; Dan Bi; Ju-e Guo; Shouyang Wang

  1. By: Wenjing Wang; Minjing Tao
    Abstract: Multivariate volatility modeling and forecasting are crucial in financial economics. This paper develops a copula-based approach to model and forecast realized volatility matrices. The proposed copula-based time series models can capture the hidden dependence structure of realized volatility matrices. Also, this approach can automatically guarantee the positive definiteness of the forecasts through either Cholesky decomposition or matrix logarithm transformation. In this paper we consider both multivariate and bivariate copulas; the types of copulas include Student's t, Clayton and Gumbel copulas. In an empirical application, we find that for one-day ahead volatility matrix forecasting, these copula-based models can achieve significant performance both in terms of statistical precision as well as creating economically mean-variance efficient portfolio. Among the copulas we considered, the multivariate-t copula performs better in statistical precision, while bivariate-t copula has better economical performance.
    Date: 2020–02
  2. By: Jin Yeub Kim (Yonsei Univ); Yongjun Kim (Univ of Seoul); Myungkyu Shim (Yonsei Univ)
    Abstract: Financial analysts may have strategic incentives to herd or to anti-herd when issuing forecasts of firms' earnings. This paper develops and implements a new test to examine whether such incentives exist and to identify the form of strategic behavior. We use the equilibrium property of the finite-player forecasting game of Kim and Shim (2019) that forecast dispersion decreases as the number of forecasters increases if and only if there is strategic complementarity in their forecasts. Using the I/B/E/S database, we find strong evidence that supports strategic herding behavior of financial analysts. This finding is robust to different forecast horizons and sequential forecast release.
    Keywords: financial analysts, earnings forecasting, finite-player forecasting game, strate- gic interaction, herding
    JEL: D83 E37 G17
    Date: 2019–12
  3. By: Camille Cornand (Université de Lyon, CNRS, Gate OFCE, Sciences Po, Paris, France); Julien Pillot (Université Paris Saclay)
    Abstract: Establishing the external validity of laboratory experiments in terms of inflation forecasts is crucial for policy initiatives to be valid outside the laboratory. Our contribution is to document whether different measures of inflation expectations based on various categories of agents (participants to experiments, households, industry forecasters, professional forecasters, financial market participants and central bankers) share common patterns by analyzing: the forecasting performances of these different categories of data; the information rigidities to which they are subject; the determination of expectations. Overall, the different categories of forecasts exhibit common features: forecast errors are comparably large and autocorrelated, forecast errors and forecast revisions are predictable from past information, which suggests the presence of information frictions. Finally, the standard lagged inflation determinant of inflation expectations is robust to the data sets. There is nevertheless some heterogeneity among the six different sets. If experimental forecasts are relatively comparable to survey and financial market data, central bank forecasts seem to be superior.
    Keywords: Inflation expectations, experimental forecasts, survey forecasts, market based forecasts, central bank forecasts
    JEL: E3 E5
    Date: 2019–02
  4. By: Shaolong Sun; Yanzhao Li; Shouyang Wang; Ju-e Guo
    Abstract: The large amount of tourism-related data presents a series of challenges for tourism demand forecasting, including data deficiencies, multicollinearity and long calculation time. A Bagging-based multivariate ensemble deep learning model, integrating Stacked Autoencoders and KELM (B-SAKE) is proposed to address these challenges in this study. We forecast tourist arrivals arriving in Beijing from four countries adopting historical data on tourist arrivals arriving in Beijing, economic indicators and tourist online behavior variables. The results from the cases of four origin countries suggest that our proposed B-SAKE model outperforms than benchmark models whether in horizontal accuracy, directional accuracy or statistical significance. Both Bagging and Stacked Autoencoder can improve the forecasting performance of the models. Moreover, the forecasting performance of the models is evaluated with consistent results by means of the multi-step-ahead forecasting scheme.
    Date: 2020–02
  5. By: Shaolong Suna; Dan Bi; Ju-e Guo; Shouyang Wang
    Abstract: The accurate seasonal and trend forecasting of tourist arrivals is a very challenging task. In the view of the importance of seasonal and trend forecasting of tourist arrivals, and limited research work paid attention to these previously. In this study, a new adaptive multiscale ensemble (AME) learning approach incorporating variational mode decomposition (VMD) and least square support vector regression (LSSVR) is developed for short-, medium-, and long-term seasonal and trend forecasting of tourist arrivals. In the formulation of our developed AME learning approach, the original tourist arrivals series are first decomposed into the trend, seasonal and remainders volatility components. Then, the ARIMA is used to forecast the trend component, the SARIMA is used to forecast seasonal component with a 12-month cycle, while the LSSVR is used to forecast remainder volatility components. Finally, the forecasting results of the three components are aggregated to generate an ensemble forecasting of tourist arrivals by the LSSVR based nonlinear ensemble approach. Furthermore, a direct strategy is used to implement multi-step-ahead forecasting. Taking two accuracy measures and the Diebold-Mariano test, the empirical results demonstrate that our proposed AME learning approach can achieve higher level and directional forecasting accuracy compared with other benchmarks used in this study, indicating that our proposed approach is a promising model for forecasting tourist arrivals with high seasonality and volatility.
    Date: 2020–02

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