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Forecasting
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Forecasting Financial Time Series during Turbulent Times
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Forecasting Financial Time Series during Turbulent Times

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Economics and Management".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 6736

Special Issue Editor

Prof. Dr. Piotr Fiszeder

E-Mail Website
Guest Editor
Department of Econometrics and Statistics, Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Toruń, 87-100 Toruń, Poland
Interests: financial econometrics; volatility modeling; financial risk management; cryptocurrencies

Special Issue Information

Dear Colleagues,

In the last three years, there have been extraordinary events that have had a tremendous impact on financial markets. The coronavirus pandemic significantly impacted many financial assets and comodieties. The Russo-Ukrainian war led to an enormous fluctuation in financial markets. There has been a fall in the value of many stock indices, an increase in the value of the US dollar, and a sharp increase in many commodity prices.

During turbulent times, e.g., the COVID-19 crisis or the outbreak of the Russo-Ukrainian war, the forecasting ability of many models has deteriorated. For this reason, there is a need to look for methods that perform well during the market turmoil and high market uncertainty.

Both methodological papers and interesting empirical applications in financial markets qualify for this Special Issue. The topics of interest include, but are not limited to:

  • Forecasting stock prices, currencies, cryptocurrencies, commodities, and derivatives;
  • Forecasting the volatility of financial time series;
  • Forecasting risk measures;
  • Forecasting correlation;
  • Forecasting with high-frequency financial data.

Prof. Dr. Piotr Fiszeder
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • volatility
  • risk
  • turbulent time
  • COVID-19 crisis
  • war in Ukraine
  • stock prices
  • currencies
  • commodities
  • cryptocurrencies
  • forecasting

Published Papers (3 papers)

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Research

18 pages, 3064 KiB  
Article
State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export
by Yoga Sasmita, Heri Kuswanto and Dedy Dwi Prastyo
Forecasting 2024, 6(1), 152-169; https://doi.org/10.3390/forecast6010009 - 12 Feb 2024
Viewed by 1052
Abstract
Standard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general and flexible scheme in nonlinear modeling. [...] Read more.
Standard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general and flexible scheme in nonlinear modeling. On the other hand, time-series data often exhibit multiple frequency components, such as trends, seasonality, cycles, and noise. These frequency components can be optimized in forecasting using Singular Spectrum Analysis (SSA). Furthermore, the two most widely used approaches in SSA are Linear Recurrent Formula (SSAR) and Vector (SSAV). SSAV has better accuracy and robustness than SSAR, especially in handling structural breaks. Therefore, this research proposes modeling the SSAV coefficient with an SDM approach to take structural breaks called SDM-SSAV. SDM recursively updates the SSAV coefficient to adapt over time and between states using an Extended Kalman Filter (EKF). Empirical results with Indonesian Export data and simulation studies show that the accuracy of SDM-SSAV outperforms SSAR, SSAV, SDM-SSAR, hybrid ARIMA-LSTM, and VARI. Full article
(This article belongs to the Special Issue Forecasting Financial Time Series during Turbulent Times)
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32 pages, 2144 KiB  
Article
Macroeconomic Predictions Using Payments Data and Machine Learning
by James T. E. Chapman and Ajit Desai
Forecasting 2023, 5(4), 652-683; https://doi.org/10.3390/forecast5040036 - 27 Nov 2023
Viewed by 2557
Abstract
This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML) models enhance their predictive value. We [...] Read more.
This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML) models enhance their predictive value. We find payments data with a factor model can help improve accuracy up to 25% in predicting GDP, retail, and wholesale sales; and nonlinear ML models can further improve the accuracy up to 20%. Furthermore, we find the retail payments data are more useful than the data from the wholesale system; and they add more value during crisis and at the nowcasting horizon due to the timeliness. The contribution of the payments data and ML models is small and linear during low and normal economic growth periods. However, their contribution is large, asymmetrical, and nonlinear during crises such as COVID-19. Moreover, we propose a cross-validation approach to mitigate overfitting and use tools to overcome interpretability in the ML models to improve their effectiveness for policy use. Full article
(This article belongs to the Special Issue Forecasting Financial Time Series during Turbulent Times)
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15 pages, 393 KiB  
Article
Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins
by Apostolos Ampountolas
Forecasting 2023, 5(2), 472-486; https://doi.org/10.3390/forecast5020026 - 20 Jun 2023
Cited by 3 | Viewed by 2284
Abstract
This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We [...] Read more.
This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We compare statistical, machine learning, and deep learning forecasting models to evaluate the financial markets, such as the ARIMA, hybrid ETS-ANN, and kNN predictive models. The study results indicate that predicting financial market fluctuations is challenging, and the accuracy levels are generally low in several instances. ARIMA and hybrid ETS-ANN models perform better over extended periods compared to the kNN model, with ARIMA being the best-performing model in 2018–2021 and the hybrid ETS-ANN model being the best-performing model in most of the other subperiods. Still, the kNN model outperforms the others in several periods, depending on the observed accuracy measure. Researchers have advocated using parametric and non-parametric modeling combinations to generate better results. In this study, the results suggest that the hybrid ETS-ANN model is the best-performing model despite its moderate level of accuracy. Thus, the hybrid ETS-ANN model is a promising financial time series forecasting approach. The findings offer financial analysts an additional source that can provide valuable insights for investment decisions. Full article
(This article belongs to the Special Issue Forecasting Financial Time Series during Turbulent Times)
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