nep-for New Economics Papers
on Forecasting
Issue of 2019‒12‒16
ten papers chosen by
Rob J Hyndman
Monash University

  1. Enhancing load, wind and solar generation forecasts in day-ahead forecasting of spot and intraday electricity prices By Katarzyna Maciejowska; Weronika Nitka; Tomasz Weron
  2. A simplified seasonal forecasting strategy, applied to wind and solar power in Europe By Bett, Philip E; Thornton, Hazel E.; Troccoli, Alberto; De Felice, Matteo; Suckling, Emma; Dubus, Laurent; Saint-Drenan, Yves-Marie; Brayshaw, David J.
  3. Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility By Dimos Kambouroudis; David McMillan; Katerina Tsakou
  4. "Incredible India"-an empirical confrimation from the Box-Jenkins ARIMA technique By NYONI, THABANI
  5. Financial Market Directional Forecasting With Stacked Denoising Autoencoder By Shaogao Lv; Yongchao Hou; Hongwei Zhou
  6. Machine Learning et nouvelles sources de données pour le scoring de crédit By Christophe Hurlin; Christophe Pérignon
  7. Merging structural and reduced-form models for forecasting: opening the DSGE-VAR box By Martínez-Martin, Jaime; Morris, Richard; Onorante, Luca; Piersanti, Fabio M.
  8. Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 By Omer Berat Sezer; Mehmet Ugur Gudelek; Ahmet Murat Ozbayoglu
  9. High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration By Stephan Smeekes; Etienne Wijler
  10. Predicting bubble bursts in oil prices using mixed causal-noncausal models By Alain Hecq; Elisa Voisin

  1. By: Katarzyna Maciejowska; Weronika Nitka; Tomasz Weron
    Abstract: In recent years, a rapid development of renewable energy sources (RES) has been observed across the world. Intermittent energy sources, which depend strongly on weather conditions, induce additional uncertainty to the system and impact the level and variability of electricity prices. Predictions of RES, together with the level of demand, have been recognized as one of the most important determinants of future electricity prices. In this research, it is shown that forecasts of these fundamental variables, which are published by Transmission System Operators (TSO), are biased and could be improved with simple regression models. Enhanced predictions are next used for forecasting of spot and intraday prices in Germany. The results indicate that improving the forecasts of fundamentals does not bring any gains in case of the spot market, but leads to more accurate predictions of intraday prices. Finally, it is demonstrated that utilization of enhanced forecasts is helpful in a day-ahead choice of a market (spot or intraday) and results in a substantial increase of profits.
    Keywords: Renewables; Electricity prices; Day-ahead market; Intraday market; Forecasting
    JEL: C51 C52 C53 G11 Q41 Q47
    Date: 2019–12–10
  2. By: Bett, Philip E (Met Office); Thornton, Hazel E.; Troccoli, Alberto; De Felice, Matteo; Suckling, Emma; Dubus, Laurent; Saint-Drenan, Yves-Marie; Brayshaw, David J.
    Abstract: We demonstrate the current levels of skill for seasonal forecasts of wind and irradiance in Europe, using forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that a simple linear regression-based method, using the hindcast and forecast ensemble means, provides a straightforward approach to produce reliable probabilistic seasonal forecasts in the cases where there is skill. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, providing opportunities to improve the skill in some cases. We further demonstrate that taking a seasonal average and a regional (e.g. national) average means that wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance): the detailed non-linear transformations from meteorological variables to energy variables, which can be essential for precision on weather forecasting timescales and for climatological studies, are usually not necessary when producing seasonal forecasts of these average quantities. Together, our results demonstrate that, in the cases where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, it can be very straightforward to forecast seasonal mean wind and solar power generation based on those climate variables, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.
    Date: 2019–04–01
  3. By: Dimos Kambouroudis (Department of Accounting and Finance, University of Stirling); David McMillan (Department of Accounting and Finance, University of Stirling); Katerina Tsakou (School of Management, Swansea University)
    Abstract: We examine the role of implied volatility, leverage effect, overnight returns and volatility of realized volatility in forecasting realized volatility by extending the heterogeneous autoregressive (HAR) model to include these additional variables. We find that implied volatility is important in forecasting future realized volatility. In most cases a model that accounts for implied volatility provides a significantly better forecast than more sophisticated models that account for other features of volatility, but exclude the information backed out from option prices. This result is consistent over time. We also assess whether leverage effect, overnight returns and volatility of realized volatility carry any incremental information beyond that captured by implied volatility and past realized volatility. We find that while overnight returns and leverage e˙ect are important for some markets, the volatility of realized volatility is of limited value for most stock markets.
    Keywords: HAR model, realized volatility, implied volatility, implied volatility effects, leverage effect, overnight returns, GARCH
    Date: 2019–12–12
    Abstract: “Incredible !ndia”, is India’s tourism maxim. Using the Box – Jenkins ARIMA approach, this study will attempt to examine the validity and suitability of this maxim. Does tourism data conform to this mind-blowing motto? Is India really incredible? What are the subsequent policy directions? The study uses annual time series data covering the period 1981 to 2017. Using annual time series data, ranging over the period 1981 to 2017, the study applied the general ARIMA technique in order to model and forecast tourist arrivals in India. The ADF tests indicate that the foreign tourists arrivals series in I (2). The study, based on the minimum MAPE value, finally presented the ARIMA (2, 2, 5) model as the appropriate model to forecast foreign tourist arrivals in India. Analysis of the residuals of the ARIMA (2, 2, 5) model indicate that the selected model is stable and appropriate for forecasting foreign tourist arrivals in India. The forecasted foreign tourist arrivals over the period 2018 to 2028 show a sharp upward trend. This proves beyond any reasonable doubt that indeed in India is incredible – tourists all over the world are expected to continue flowing to India because India is just incredible! Surely, tourism data conforms to the motto “Atithidevo Bhava”. The study boasts of three policy directions that are envisioned to add more positive changes in India’s tourism sector.
    Keywords: ARIMA; forecasting; foreign tourist arrivals; India; tourism
    JEL: L83
    Date: 2019–11–02
  5. By: Shaogao Lv; Yongchao Hou; Hongwei Zhou
    Abstract: Forecasting stock market direction is always an amazing but challenging problem in finance. Although many popular shallow computational methods (such as Backpropagation Network and Support Vector Machine) have extensively been proposed, most algorithms have not yet attained a desirable level of applicability. In this paper, we present a deep learning model with strong ability to generate high level feature representations for accurate financial prediction. Precisely, a stacked denoising autoencoder (SDAE) from deep learning is applied to predict the daily CSI 300 index, from Shanghai and Shenzhen Stock Exchanges in China. We use six evaluation criteria to evaluate its performance compared with the back propagation network, support vector machine. The experiment shows that the underlying financial model with deep machine technology has a significant advantage for the prediction of the CSI 300 index.
    Date: 2019–12
  6. By: Christophe Hurlin (LEO - Laboratoire d'Économie d'Orleans - CNRS - Centre National de la Recherche Scientifique - Université de Tours - UO - Université d'Orléans); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)
    Abstract: In this article, we discuss the contribution of Machine Learning techniques and new data sources (New Data) to credit-risk modelling. Credit scoring was historically one of the first fields of application of Machine Learning techniques. Today, these techniques permit to exploit new sources of data made available by the digitalization of customer relationships and social networks. The combination of the emergence of new methodologies and new data has structurally changed the credit industry and favored the emergence of new players. First, we analyse the incremental contribution of Machine Learning techniques per se. We show that they lead to significant productivity gains but that the forecasting improvement remains modest. Second, we quantify the contribution of the "datadiversity", whether or not these new data are exploited through Machine Learning. It appears that some of these data contain weak signals that significantly improve the quality of the assessment of borrowers' creditworthiness. At the microeconomic level, these new approaches promote financial inclusion and access to credit for the most vulnerable borrowers. However, Machine Learning applied to these data can also lead to severe biases and discrimination.
    Abstract: Dans cet article, nous proposons une réflexion sur l'apport des techniques d'apprentissage automatique (Machine Learning) et des nouvelles sources de données (New Data) pour la modélisation du risque de crédit. Le scoring de crédit fut historiquement l'un des premiers champs d'application des techniques de Machine Learning. Aujourd'hui, ces techniques permettent d'exploiter de « nouvelles » données rendues disponibles par la digitalisation de la relation clientèle et les réseaux sociaux. La conjonction de l'émergence de nouvelles méthodologies et de nouvelles données a ainsi modifié de façon structurelle l'industrie du crédit et favorisé l'émergence de nouveaux acteurs. Premièrement, nous analysons l'apport des algorithmes de Machine Learning à ensemble d'information constant. Nous montrons qu'il existe des gains de productivité liés à ces nouvelles approches mais que les gains de prévision du risque de crédit restent en revanche modestes. Deuxièmement, nous évaluons l'apport de cette « datadiversité », que ces nouvelles données soient exploitées ou non par des techniques de Machine Learning. Il s'avère que certaines de ces données permettent de révéler des signaux faibles qui améliorent sensiblement la qualité de l'évaluation de la solvabilité des emprunteurs. Au niveau microéconomique, ces nouvelles approches favorisent l'inclusion financière et l'accès au crédit des emprunteurs les plus fragiles. Cependant, le Machine Learning appliqué à ces données peut aussi conduire à des biais et à des phénomènes de discrimination.
    Keywords: Machine Learning ML,Credit scoring,New data,Nouvelles données,Scoring de crédit,Apprentissage automatique
    Date: 2019–11–21
  7. By: Martínez-Martin, Jaime; Morris, Richard; Onorante, Luca; Piersanti, Fabio M.
    Abstract: The post-crisis environment has posed important challenges to standard forecasting models. In this paper, we exploit several combinations of a large-scale DSGE structural model with standard reduced-form methods such as (B)VAR (i.e. DSGE-VAR and Augmented-(B)VARDSGE methods) and assess their use for forecasting the Spanish economy. Our empirical findings suggest that: (i) the DSGE model underestimates growth of real variables due to its mean reverting properties in the context of a sample that is difficult to deal with; (ii) in spite of this, reduced-form VARs benefit from the imposition of an economic prior from the structural model; and (iii) pooling information in the form of variables extracted from the structural model with (B)VAR methods does not give rise to any relevant gain in terms of forecasting accuracy. JEL Classification: C54, E37, F3, F41
    Keywords: Bayesian VAR, DSGE models, forecast comparison, real time data
    Date: 2019–12
  8. By: Omer Berat Sezer; Mehmet Ugur Gudelek; Ahmet Murat Ozbayoglu
    Abstract: Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.
    Date: 2019–11
  9. By: Stephan Smeekes; Etienne Wijler
    Abstract: We investigate how the possible presence of unit roots and cointegration affects forecasting with Big Data. As most macroeoconomic time series are very persistent and may contain unit roots, a proper handling of unit roots and cointegration is of paramount importance for macroeconomic forecasting. The high-dimensional nature of Big Data complicates the analysis of unit roots and cointegration in two ways. First, transformations to stationarity require performing many unit root tests, increasing room for errors in the classification. Second, modelling unit roots and cointegration directly is more difficult, as standard high-dimensional techniques such as factor models and penalized regression are not directly applicable to (co)integrated data and need to be adapted. We provide an overview of both issues and review methods proposed to address these issues. These methods are also illustrated with two empirical applications.
    Date: 2019–11
  10. By: Alain Hecq; Elisa Voisin
    Abstract: This paper investigates oil price series using mixed causal-noncausal autoregressive (MAR) models, namely dynamic processes that depend not only on their lags but also on their leads. MAR models have been successfully implemented on commodity prices as they allow to generate nonlinear features such as speculative bubbles. We estimate the probabilities that bubbles in oil price series burst once the series enter an explosive phase. To do so we first evaluate how to adequately detrend nonstationary oil price series while preserving the bubble patterns observed in the raw data. The impact of different filters on the identification of MAR models as well as on forecasting bubble events is investigated using Monte Carlo simulations. We illustrate our findings on WTI and Brent monthly series.
    Date: 2019–11

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