Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.8
Journals
JRFM
Special Issues
Forecasting and Time Series Analysis
share announcement

Forecasting and Time Series Analysis

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 7735

Special Issue Editor

Dr. Heni Boubaker

E-Mail Website
Guest Editor
1. Department of Quantitative Methods, Institute of High Commercial Studies (IHEC) of Sousse, LaREMFiQ, B.P. 40, 4054 Sousse, Tunisia
2. Department of Finance, IPAG Business School, IPAG LAB, 184 Boulevard Saint-Germain, 75006 Paris, France
Interests: econometric theory; financial econometrics; time series and panel data econometrics; applied mathematics; artificial intelligence methods; signal processing

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue of JRFM, entitled "Forecasting and Time Series Analysis", is to explore new approaches to time series forecasting that deepen our understanding of forecasting techniques. The modelling and prediction of time series are crucial prerequisites for risk management, the development of pricing formulas for investment analysis, the negotiation of short- and long-term trading positions, and the construction of hedging strategies. Indeed, several parametric, non-parametric, and/or hybrid forecasting approaches have emerged in recent years. The most suitable prediction technique is the one that reproduces the inherent characteristics of the process under investigation and achieves a better prediction performance across different horizons. As contributions to this field of research, papers submitted to this Special Issue will aim to assist decision makers in the forecasting of suitable responses to major events and processes. Contributions that focus on novel hybrid forecasting methodologies and neural network approaches are encouraged, as are those drawing on parsimonious modelling techniques.

Dr. Heni Boubaker
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. Journal of Risk and Financial Management is an international peer-reviewed open access monthly 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 1400 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

  • time series
  • modelling methods
  • neural networks
  • hybrid models
  • forecasting approach
  • prediction performance

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 921 KiB  
Article
Interdependence between the BRICS Stock Markets and the Oil Price since the Onset of Financial and Economic Crises
by Narjess Bouslama
J. Risk Financial Manag. 2023, 16(7), 316; https://doi.org/10.3390/jrfm16070316 - 29 Jun 2023
Viewed by 1529
Abstract
In this paper, we use a copula to examine the relationship and dynamic dependence structure between the crude oil market and the BRICS countries’ stock indices expressed through financial crises, from the 2008 global financial crisis to COVID-19, based on daily data. We [...] Read more.
In this paper, we use a copula to examine the relationship and dynamic dependence structure between the crude oil market and the BRICS countries’ stock indices expressed through financial crises, from the 2008 global financial crisis to COVID-19, based on daily data. We characterize the long-term relationship as well as the short-term dynamics and represent the interdependence between them. We also study the short-run conditional links through the considered variables under the effects of long-run interactions and the asymmetric volatility spillover relationship. In addition, we establish that the volatility transmission is stubborn and that the impact of the crises and our empirical findings prove that there is fractional co-integration between crude oil and financial markets. We notice that there are lengthy correlations between the variables, as we detect significant bidirectional causal links. In particular, we see positive short-run links and use an optimal copula coefficient to measure the risk spillovers between oil markets and financial markets that represent the dependence structure. For robustness purposes, based on a sliding-window analysis, we complement our investigation with VaR analysis. Full article
(This article belongs to the Special Issue Forecasting and Time Series Analysis)
►▼ Show Figures

Figure 1

21 pages, 369 KiB  
Article
Risk Measure between Exchange Rate and Oil Price during Crises: Evidence from Oil-Importing and Oil-Exporting Countries
by Mouna Ben Saad Zorgati
J. Risk Financial Manag. 2023, 16(4), 250; https://doi.org/10.3390/jrfm16040250 - 20 Apr 2023
Cited by 1 | Viewed by 1424
Abstract
This study investigates the risk spillover effect between the exchange rate of importing and exporting oil countries and the oil price. The analysis is supported by the utilization of a set of double-long memories. Thereafter, a multivariate GARCH type model is adopted to [...] Read more.
This study investigates the risk spillover effect between the exchange rate of importing and exporting oil countries and the oil price. The analysis is supported by the utilization of a set of double-long memories. Thereafter, a multivariate GARCH type model is adopted to analyze the dynamic conditional correlations. Moreover, the Gumbel copula is employed to define the nonlinear structure of dependence and to evaluate the optimal portfolio. The conditional Value-at-Risk (CoVaR) is adopted as a risk measure. Findings indicate a long-run dependence and asymmetry of bidirectional risk spillover among oil price and exchange rate and confirm that the risk spillover intensity is different between the former and the latter. They show that the oil price has a stronger spillover effect in the case of oil exporting countries and the lowest spillover effect in the case of oil importing countries. Full article
(This article belongs to the Special Issue Forecasting and Time Series Analysis)
22 pages, 1791 KiB  
Article
Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price
by Heni Boubaker and Nawres Bannour
J. Risk Financial Manag. 2023, 16(4), 246; https://doi.org/10.3390/jrfm16040246 - 18 Apr 2023
Cited by 2 | Viewed by 1150
Abstract
This paper aims to evaluate the forecast capability of electricity markets, categorized by numerous major characteristics such as non-stationarity, nonlinearity, highest volatility, high frequency, mean reversion and multiple seasonality, which give multifarious forecasts. To improve it, this investigation proposes a new hybrid approach [...] Read more.
This paper aims to evaluate the forecast capability of electricity markets, categorized by numerous major characteristics such as non-stationarity, nonlinearity, highest volatility, high frequency, mean reversion and multiple seasonality, which give multifarious forecasts. To improve it, this investigation proposes a new hybrid approach that links a dual long-memory process (Gegenbauer autoregressive moving average (GARMA) and generalized long-memory GARCH (G-GARCH)) and the empirical wavelet transform (EWT) and local linear wavelet neural network (LLWNN) approaches, forming the k-factor GARMA-EWLLWNN model. The future hybrid model accomplished is assessed via data from the Polish electricity markets, and it is matched with the generalized long-memory k-factor GARMA-G-GARCH process and the hybrid EWLLWNN, to demonstrate the robustness of our approach. The obtained outcomes show that the suggested model presents important results to define the relevance of the modeling approach that offers a remarkable framework to reproduce the inherent characteristics of the electricity prices. Finally, it is presented that the adopted methodology is the most appropriate one for prediction as it realizes a better prediction performance and may be an answer for forecasting electricity prices. Full article
(This article belongs to the Special Issue Forecasting and Time Series Analysis)
►▼ Show Figures

Figure 1

22 pages, 1562 KiB  
Article
Are Bitcoin and Gold a Safe Haven during COVID-19 and the 2022 Russia–Ukraine War?
by Ihsan Erdem Kayral, Ahmed Jeribi and Sahar Loukil
J. Risk Financial Manag. 2023, 16(4), 222; https://doi.org/10.3390/jrfm16040222 - 02 Apr 2023
Cited by 11 | Viewed by 3025
Abstract
Our investigation strives to unearth the best portfolio hedging strategy for the G7 stock indices through Bitcoin and gold using daily data relevant to the period 2 January 2016 to 5 January 2023. This study uses the DVECH-GARCH model to model dynamic correlation [...] Read more.
Our investigation strives to unearth the best portfolio hedging strategy for the G7 stock indices through Bitcoin and gold using daily data relevant to the period 2 January 2016 to 5 January 2023. This study uses the DVECH-GARCH model to model dynamic correlation and then compute optimal hedge ratios and hedging effectiveness. The empirical findings show that Bitcoin and gold were rather effective hedge assets before COVID-19 and diversifiers during the pandemic and Russia–Ukraine war. From hedging effectiveness perspectives, gold and Bitcoin are safe-haven assets, and the investment risk of G7 stock indices could be hedged by taking a short position during thepandemic period and war except for the pair Nikkei/Gold. Additionally, gold beats Bitcoin in terms of hedging efficiency. We thus demonstrate the central role of Bitcoin and gold as financial market participants, particularly during market turmoil and downward movements. Our findings can be of interest to investors, regulators, and governments to take into consideration the role of Bitcoin in financial markets. Full article
(This article belongs to the Special Issue Forecasting and Time Series Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop