nep-for New Economics Papers
on Forecasting
Issue of 2022‒06‒27
six papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting a commodity-exporting small open developing economy using DSGE and DSGE-BVAR By Erlan Konebayev
  2. Probabilistic forecasting of German electricity imbalance prices By Micha{\l} Narajewski
  3. HARNet: A Convolutional Neural Network for Realized Volatility Forecasting By Rafael Reisenhofer; Xandro Bayer; Nikolaus Hautsch
  4. Stock Return Predictability: Evaluation based on interval forecasts By Amélie Charles; Jae Kim; Olivier Darné
  5. Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model By Bhattacharjee, Arnab; Kohns, David
  6. Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction By Vishal Kuber; Divakar Yadav; Arun Kr Yadav

  1. By: Erlan Konebayev (NAC Analytica, Nazarbayev University)
    Abstract: In this paper, we assess the forecasting performance of three types of structural models - DSGE, BVAR with Minnesota priors, and DSGE-BVAR - in the context of a commodity-exporting small open developing economy using the data for Kazakhstan. We find that BVAR and DSGE-BVAR models generally produce point forecasts that are more accurate and less biased compared to those of DSGE in the short term, and that BVAR forecasts rapidly deteriorate in quality as the length of the forecast horizon increases. The density forecast analysis shows that when all variables are considered, one of the BVAR models performs better than DSGE at the one quarter horizon, and when financial sector variables are omitted, one DSGE-BVAR and both BVAR models demonstrate superior performance in the short term.
    Keywords: DSGE; DSGE-BVAR; Bayesian estimation; forecasting; small open economy
    JEL: C11 E17 E32 E37
    Date: 2022–04
  2. By: Micha{\l} Narajewski
    Abstract: The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. To address this challenge, the energy exchanges have been developing further trading possibilities, especially the intraday and balancing markets. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. Therefore, in this manuscript we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. The analysis was conducted on the German market, however it could be easily applied to any other market of similar structure.
    Date: 2022–05
  3. By: Rafael Reisenhofer; Xandro Bayer; Nikolaus Hautsch
    Abstract: Despite the impressive success of deep neural networks in many application areas, neural network models have so far not been widely adopted in the context of volatility forecasting. In this work, we aim to bridge the conceptual gap between established time series approaches, such as the Heterogeneous Autoregressive (HAR) model, and state-of-the-art deep neural network models. The newly introduced HARNet is based on a hierarchy of dilated convolutional layers, which facilitates an exponential growth of the receptive field of the model in the number of model parameters. HARNets allow for an explicit initialization scheme such that before optimization, a HARNet yields identical predictions as the respective baseline HAR model. Particularly when considering the QLIKE error as a loss function, we find that this approach significantly stabilizes the optimization of HARNets. We evaluate the performance of HARNets with respect to three different stock market indexes. Based on this evaluation, we formulate clear guidelines for the optimization of HARNets and show that HARNets can substantially improve upon the forecasting accuracy of their respective HAR baseline models. In a qualitative analysis of the filter weights learnt by a HARNet, we report clear patterns regarding the predictive power of past information. Among information from the previous week, yesterday and the day before, yesterday's volatility makes by far the most contribution to today's realized volatility forecast. Moroever, within the previous month, the importance of single weeks diminishes almost linearly when moving further into the past.
    Date: 2022–05
  4. By: Amélie Charles (Audencia Business School); Jae Kim (La Trobe University [Melbourne]); Olivier Darné (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université - IUML - FR 3473 Institut universitaire Mer et Littoral - Nantes Université - pôle Sciences et technologie - Nantes Univ - Nantes Université - UBS - Université de Bretagne Sud - UM - Le Mans Université - UA - Université d'Angers - CNRS - Centre National de la Recherche Scientifique - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - Nantes Univ - ECN - Nantes Université - École Centrale de Nantes - Nantes Univ - Nantes Université)
    Abstract: This paper evaluates the predictability of monthly stock return using out-of-sample interval forecasts. Past studies exclusively use point forecasts, which are of limited value since they carry no information about intrinsic predictive uncertainty. We compare the empirical performance of alternative interval forecasts for stock return generated from a naïve model, univariate autoregressive model, and multivariate model (predictive regression and VAR), using U.S. data from 1926. It is found that neither univariate nor multivariate interval forecasts outperform naïve forecasts. This strongly suggests that the U.S. stock market has been informationally efficient in the weak-form as well as in the semi-strong form.
    Keywords: Autoregressive Model,Bootstrapping,Financial Ratios,Forecasting,Interval Score,Market Efficiency
    Date: 2022–04
  5. By: Bhattacharjee, Arnab; Kohns, David
    Abstract: This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normal-inverse-gamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense data-generating-processes. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects.
    Keywords: global-local priors, Google trends, non-centred state space, shrinkage
    JEL: C11 C22 C55 E37 E66
    Date: 2022–05
  6. By: Vishal Kuber; Divakar Yadav; Arun Kr Yadav
    Abstract: Designing robust and accurate prediction models has been a viable research area since a long time. While proponents of a well-functioning market predictors believe that it is difficult to accurately predict market prices but many scholars disagree. Robust and accurate prediction systems will not only be helpful to the businesses but also to the individuals in making their financial investments. This paper presents an LSTM model with two different input approaches for predicting the short-term stock prices of two Indian companies, Reliance Industries and Infosys Ltd. Ten years of historic data (2012-2021) is taken from the yahoo finance website to carry out analysis of proposed approaches. In the first approach, closing prices of two selected companies are directly applied on univariate LSTM model. For the approach second, technical indicators values are calculated from the closing prices and then collectively applied on Multivariate LSTM model. Short term market behaviour for upcoming days is evaluated. Experimental outcomes revel that approach one is useful to determine the future trend but multivariate LSTM model with technical indicators found to be useful in accurately predicting the future price behaviours.
    Date: 2022–05

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