Pub Date : 2025-03-18DOI: 10.1016/j.ijforecast.2025.01.007
Stavros Degiannakis , Eleftheria Kafousaki
The present study highlights the economic profits of markets’ participants, accumulated from the disaggregated forecasts of the stock market’s implied volatility, generated from an ensemble modelling architecture. We incorporate six decomposition techniques, namely, the EMD, the EEMD, the SSA, the HVD, the EWT and the VMD and four different model frameworks that of AR, HAR, HW and LSTM, which are tested against a trading strategy. We diverge from quantifying forecast accuracy solely on statistical loss functions and report the cumulative returns of short or long exposure on roll adjusted VIX futures. The findings show that decomposing a time series into its intrinsic modes prior to modelling and forecasting, can result in generating forecast gains that are translated into improved profits for trading horizons of 1 to 22 days ahead. Important trading implications are drawn from these results.
{"title":"Disaggregating VIX","authors":"Stavros Degiannakis , Eleftheria Kafousaki","doi":"10.1016/j.ijforecast.2025.01.007","DOIUrl":"10.1016/j.ijforecast.2025.01.007","url":null,"abstract":"<div><div><span><span>The present study highlights the economic profits of markets’ participants, accumulated from the disaggregated forecasts of the stock market’s implied volatility, generated from an ensemble modelling architecture. We incorporate six decomposition techniques, namely, the EMD, the EEMD, the </span>SSA, the HVD, the EWT and the VMD and four different model frameworks that of AR, HAR, HW and </span>LSTM<span>, which are tested against a trading strategy. We diverge from quantifying forecast accuracy solely on statistical loss functions and report the cumulative returns of short or long exposure on roll adjusted VIX futures. The findings show that decomposing a time series into its intrinsic modes prior to modelling and forecasting, can result in generating forecast gains that are translated into improved profits for trading horizons of 1 to 22 days ahead. Important trading implications are drawn from these results.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1559-1588"},"PeriodicalIF":7.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1016/j.ijforecast.2025.02.006
Michael S. Lewis-Beck , John Kenny , Debra Leiter , Andreas Erwin Murr , Onyinye B. Ogili , Mary Stegmaier , Charles Tien
We draw globally on a major election forecasting tool, political economy models. Vote intention polls in pre-election public surveys are a widely known approach; however, the lesser-known political economy models take a different scientific tack, relying on regression analysis and voting theory, particularly the force of “fundamentals.” We begin our discussion with two advanced industrial democracies, the US and UK. We then examine two less frequently forecasted cases, Mexico and Ghana, to highlight the potential for political-economic forecasting and the challenges faced. In evaluating the performance of political economy models, we argue for their accuracy but do not neglect lead time, parsimony, and transparency. Furthermore, we suggest how the political economic approach can be adapted to the changing landscape that democratic electorates face.
{"title":"Election forecasting: Political economy models","authors":"Michael S. Lewis-Beck , John Kenny , Debra Leiter , Andreas Erwin Murr , Onyinye B. Ogili , Mary Stegmaier , Charles Tien","doi":"10.1016/j.ijforecast.2025.02.006","DOIUrl":"10.1016/j.ijforecast.2025.02.006","url":null,"abstract":"<div><div><span>We draw globally on a major election forecasting tool, political economy models. Vote intention polls in pre-election public surveys are a widely known approach; however, the lesser-known political economy models take a different scientific tack, relying on regression analysis and voting theory, particularly the force of “fundamentals.” We begin our discussion with two advanced industrial democracies, the US and UK. We then examine two less frequently forecasted cases, Mexico and Ghana, to highlight the potential for political-economic forecasting and the challenges faced. In evaluating the performance of political economy models, we argue for their accuracy but do not neglect lead time, parsimony, and transparency. Furthermore, we suggest how the </span>political economic approach can be adapted to the changing landscape that democratic electorates face.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1655-1665"},"PeriodicalIF":7.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.ijforecast.2025.02.009
Amor Aniss Benmoussa , Reinhard Ellwanger , Stephen Snudden
This paper proposes methods to include information from the underlying nominal daily series in model-based forecasts of average real series. We apply these methods to forecasts of the real price of crude oil. Models utilizing information from daily prices yield large forecast improvements and, in some cases, almost halve the forecast error compared to current specifications. We demonstrate for the first time that model-based forecasts of the real price of crude oil can outperform the traditional random walk forecast, that is, the end-of-month no-change forecast, at short forecast horizons.
{"title":"Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?","authors":"Amor Aniss Benmoussa , Reinhard Ellwanger , Stephen Snudden","doi":"10.1016/j.ijforecast.2025.02.009","DOIUrl":"10.1016/j.ijforecast.2025.02.009","url":null,"abstract":"<div><div>This paper proposes methods to include information from the underlying nominal daily series in model-based forecasts of average real series. We apply these methods to forecasts of the real price of crude oil. Models utilizing information from daily prices yield large forecast improvements and, in some cases, almost halve the forecast error compared to current specifications. We demonstrate for the first time that model-based forecasts of the real price of crude oil can outperform the traditional random walk forecast, that is, the end-of-month no-change forecast, at short forecast horizons.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"42 1","pages":"Pages 281-295"},"PeriodicalIF":7.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-05DOI: 10.1016/j.ijforecast.2025.02.004
Rodrigo Alves
Football (also known as soccer or association football) is the most popular sport in the world. It is a blend of skill and luck, making it highly unpredictable. To address this unpredictability, there has been a surge in popularity over the past decade in employing machine learning techniques for forecasting football-related features. This trend aligns with the growing professionalism in football analytics. Despite this progress, the existing body of work remains in its early stages, lacking the depth required to capture the intricate nuances of the sport. In this study, we introduce a convolutional approach designed to predict the occurrence of the next event in a football match, such as a goal or a corner kick, relying solely on easy-to-access past events for predictions. Our methodology adopts an online approach, meaning predictions can be computed during a live match. To validate our approach, we conduct a comprehensive evaluation against five baseline models, utilizing data from various elite European football leagues. Additionally, an ablation study is performed to understand the underlying mechanisms of our method. Finally, we present practical applications and interpretable aspects of our proposed approach.
{"title":"SCORE: A convolutional approach for football event forecasting","authors":"Rodrigo Alves","doi":"10.1016/j.ijforecast.2025.02.004","DOIUrl":"10.1016/j.ijforecast.2025.02.004","url":null,"abstract":"<div><div>Football (also known as soccer or association football) is the most popular sport in the world. It is a blend of skill and luck, making it highly unpredictable. To address this unpredictability, there has been a surge in popularity over the past decade in employing machine learning techniques<span><span> for forecasting football-related features. This trend aligns with the growing professionalism in football analytics. Despite this progress, the existing body of work remains in its early stages, lacking the depth required to capture the intricate nuances of the sport. In this study, we introduce a convolutional approach designed to predict the occurrence of the next event in a football match, such as a goal or a corner kick, relying solely on easy-to-access past events for predictions. Our methodology adopts an online approach, meaning predictions can be computed during a live match. To validate our approach, we conduct a comprehensive evaluation against five </span>baseline models, utilizing data from various elite European football leagues. Additionally, an ablation study is performed to understand the underlying mechanisms of our method. Finally, we present practical applications and interpretable aspects of our proposed approach.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1636-1652"},"PeriodicalIF":7.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1016/j.ijforecast.2025.02.001
Luis Gruber, Gregor Kastner
Vector autoregressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods—more concretely, shrinkage priors—have been shown to be successful at improving prediction performance. In the present paper, we introduce the semi-global framework, in which we replace the traditional global shrinkage parameter with group-specific shrinkage parameters. We show how this framework can be applied to various shrinkage priors, such as global–local priors and stochastic search variable selection priors. We demonstrate the virtues of the proposed framework in an extensive simulation study and in an empirical application forecasting data on the US economy. Further, we shed more light on the ongoing ‘illusion of sparsity’ debate, finding that forecasting performances under sparse/dense priors vary across evaluated economic variables and across time frames. Dynamic model averaging, however, can combine the merits of both worlds.
{"title":"Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!","authors":"Luis Gruber, Gregor Kastner","doi":"10.1016/j.ijforecast.2025.02.001","DOIUrl":"10.1016/j.ijforecast.2025.02.001","url":null,"abstract":"<div><div>Vector autoregressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods—more concretely, shrinkage priors—have been shown to be successful at improving prediction performance. In the present paper, we introduce the semi-global framework, in which we replace the traditional global shrinkage parameter with group-specific shrinkage parameters. We show how this framework can be applied to various shrinkage priors, such as global–local priors and stochastic search variable selection priors. We demonstrate the virtues of the proposed framework in an extensive simulation study and in an empirical application forecasting data on the US economy. Further, we shed more light on the ongoing ‘illusion of sparsity’ debate, finding that forecasting performances under sparse/dense priors vary across evaluated economic variables and across time frames. Dynamic model averaging, however, can combine the merits of both worlds.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1589-1619"},"PeriodicalIF":7.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1016/j.ijforecast.2025.01.005
Filip Staněk
This article describes the methods that achieved fourth and sixth place in the forecasting and investment challenges, respectively, of the M6 competition, ultimately securing first place in the overall duathlon ranking. In the forecasting challenge, we tested a novel meta-learning model that utilizes hypernetworks to design a parametric model tailored to a specific family of forecasting tasks. This approach allowed us to leverage similarities observed across individual forecasting tasks (i.e., assets) while also acknowledging potential heterogeneity in their data generating processes. The model’s training can be directly performed with backpropagation, eliminating the need to rely on higher-order derivatives, and is equivalent to a simultaneous search over the space of parametric functions and their optimal parameter values. The proposed model’s capabilities extend beyond M6, demonstrating superiority over state-of-the-art meta-learning methods in the sinusoidal regression task and outperforming conventional parametric models on time series from the M4 forecasting competition. In the investment challenge, we adjusted portfolio weights to induce greater or smaller correlation between our submission and that of other participants, depending on the current ranking, aiming to maximize the probability of achieving a good rank. While this portfolio strategy can increase the probability of securing a favorable rank, it paradoxically exhibits negative expected returns.
{"title":"Designing time-series models with hypernetworks and adversarial portfolios","authors":"Filip Staněk","doi":"10.1016/j.ijforecast.2025.01.005","DOIUrl":"10.1016/j.ijforecast.2025.01.005","url":null,"abstract":"<div><div><span>This article describes the methods that achieved fourth and sixth place in the forecasting and investment challenges, respectively, of the M6 competition, ultimately securing first place in the overall duathlon ranking. In the forecasting challenge, we tested a novel meta-learning model that utilizes hypernetworks to design a parametric model tailored to a specific family of forecasting tasks. This approach allowed us to leverage similarities observed across individual forecasting tasks (i.e., assets) while also acknowledging potential heterogeneity in their data generating processes. The model’s training can be directly performed with </span>backpropagation<span>, eliminating the need to rely on higher-order derivatives, and is equivalent to a simultaneous search over the space of parametric functions and their optimal parameter values. The proposed model’s capabilities extend beyond M6, demonstrating superiority over state-of-the-art meta-learning methods in the sinusoidal regression task and outperforming conventional parametric models on time series from the M4 forecasting competition. In the investment challenge, we adjusted portfolio weights to induce greater or smaller correlation between our submission and that of other participants, depending on the current ranking, aiming to maximize the probability of achieving a good rank. While this portfolio strategy can increase the probability of securing a favorable rank, it paradoxically exhibits negative expected returns.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1461-1476"},"PeriodicalIF":7.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1016/j.ijforecast.2025.01.006
Rakshitha Godahewa , Christoph Bergmeir , Zeynep Erkin Baz , Chengjun Zhu , Zhangdi Song , Salvador García , Dario Benavides
Forecasts are typically produced in a business context on a regular basis to make downstream decisions. Here, forecasts should not only be as accurate as possible, but also should not change arbitrarily, and be stable in some sense. In this paper, we explore two types of forecast stability that we call vertical stability (for forecasts from different origins for the same target) and horizontal stability (for forecasts from the same origin for different targets). Existing works in the literature are only applicable to certain base models and can only stabilise forecasts vertically. We propose a simple linear-interpolation-based approach to stabilise the forecasts provided by any base model, both vertically and horizontally. Our method makes the trade-off between stability and accuracy explicit, producing forecasts at any point in the spectrum of this trade-off. We used N-BEATS, pooled regression, LightGBM, ETS, and ARIMA as base models in our evaluation across different error and stability measures on four publicly available datasets. On some datasets, the proposed framework achieved forecasts that were both more accurate and stable than the base forecasts. On the others, we achieved forecasts that were slightly less accurate but much more stable.
{"title":"On forecast stability","authors":"Rakshitha Godahewa , Christoph Bergmeir , Zeynep Erkin Baz , Chengjun Zhu , Zhangdi Song , Salvador García , Dario Benavides","doi":"10.1016/j.ijforecast.2025.01.006","DOIUrl":"10.1016/j.ijforecast.2025.01.006","url":null,"abstract":"<div><div>Forecasts are typically produced in a business context on a regular basis to make downstream decisions. Here, forecasts should not only be as accurate as possible, but also should not change arbitrarily, and be stable in some sense. In this paper, we explore two types of forecast stability that we call vertical stability (for forecasts from different origins for the same target) and horizontal stability (for forecasts from the same origin for different targets). Existing works in the literature are only applicable to certain base models and can only stabilise forecasts vertically. We propose a simple linear-interpolation-based approach to stabilise the forecasts provided by any base model, both vertically and horizontally. Our method makes the trade-off between stability and accuracy explicit, producing forecasts at any point in the spectrum of this trade-off. We used N-BEATS, pooled regression, LightGBM, ETS, and ARIMA as base models in our evaluation across different error and stability measures on four publicly available datasets. On some datasets, the proposed framework achieved forecasts that were both more accurate and stable than the base forecasts. On the others, we achieved forecasts that were slightly less accurate but much more stable.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1539-1558"},"PeriodicalIF":7.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-17DOI: 10.1016/j.ijforecast.2025.02.003
{"title":"Acknowledgement to reviewers","authors":"","doi":"10.1016/j.ijforecast.2025.02.003","DOIUrl":"10.1016/j.ijforecast.2025.02.003","url":null,"abstract":"","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 859-861"},"PeriodicalIF":6.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-08DOI: 10.1016/j.ijforecast.2025.01.003
Benedikt Alexander Schuler , Johann Peter Murmann , Marie Beisemann , Ville Satopää
Judgmental forecasting research on superforecasters has demonstrated that individuals differ in their foresight. However, the concept underlying this work focuses on accuracy and does not fully incorporate the time dimension of foresight. We reconceptualize foresight as the ability to predict future states of the world accurately, where accuracy becomes continuously more important over time. To operationalize foresight in forecasting tournaments, we propose various strictly proper scoring rules and compare them with existing scoring rules using a simulation study and real-world forecasting data consisting of 414,168 scores for 9694 forecasters on 498 questions from a four-year geopolitical forecasting tournament. The results suggest that the linear time-weighted Brier score should be the default operationalization of foresight and that probability training and teaming interventions as proposed by prior research may not improve foresight as we conceptualize it. We contribute to judgmental forecasting research by clarifying the concept, operationalization, and correlates of foresight.
{"title":"Individual foresight: Concept, operationalization, and correlates","authors":"Benedikt Alexander Schuler , Johann Peter Murmann , Marie Beisemann , Ville Satopää","doi":"10.1016/j.ijforecast.2025.01.003","DOIUrl":"10.1016/j.ijforecast.2025.01.003","url":null,"abstract":"<div><div>Judgmental forecasting research on superforecasters has demonstrated that individuals differ in their foresight. However, the concept underlying this work focuses on accuracy and does not fully incorporate the time dimension of foresight. We reconceptualize foresight as the ability to predict future states of the world accurately, where accuracy becomes continuously more important over time. To operationalize foresight in forecasting tournaments, we propose various strictly proper scoring rules and compare them with existing scoring rules using a simulation study and real-world forecasting data consisting of 414,168 scores for 9694 forecasters on 498 questions from a four-year geopolitical forecasting tournament. The results suggest that the linear time-weighted Brier score should be the default operationalization of foresight and that probability training and teaming interventions as proposed by prior research may not improve foresight as we conceptualize it. We contribute to judgmental forecasting research by clarifying the concept, operationalization, and correlates of foresight.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1521-1538"},"PeriodicalIF":7.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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