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Sustainability
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Forecasting Financial Markets and Financial Crisis
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Forecasting Financial Markets and Financial Crisis

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 12204

Special Issue Editor

Prof. Dr. Giovanni De Luca

E-Mail Website
Guest Editor
Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy
Interests: analysis and forecasting of time series; dependence structure; estimation of the tail dependence; road transport emissions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The prediction of the most important financial market variables has a long tradition and literature is full of valuable contributions.

However, for many researchers the recent financial crisis has represented a serious challenge. Some popular and consolidated statistical models for forecasting market variables, e.g. volatility, have not revealed robust to such a global and unexpected crisis. The issue is relevant both for developed country economies as well as for emerging markets economies.

The purpose of this Special Issue of Sustainability is to collect works that present interesting empirical applications in financial markets conducted with rigorous procedures and effective statistical methods. In particular, forecast uncertainty has to be evaluated in an accurate way.

We invite to submit original research articles on this topic not being considered for publication elsewhere.

Prof. Dr. Giovanni De Luca
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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • financial markets
  • financial crisis
  • asset returns
  • volatility
  • forecasting

Published Papers (4 papers)

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Research

15 pages, 1330 KiB  
Article
Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions
by Jianxu Liu, Quanrui Song, Yang Qi, Sanzidur Rahman and Songsak Sriboonchitta
Sustainability 2020, 12(10), 4000; https://doi.org/10.3390/su12104000 - 14 May 2020
Cited by 8 | Viewed by 3094
Abstract
The global financial crisis in 2008 spurred the need to study systemic risk in financial markets, which is of interest to both academics and practitioners alike. We first aimed to measure and forecast systemic risk in global financial markets and then to construct [...] Read more.
The global financial crisis in 2008 spurred the need to study systemic risk in financial markets, which is of interest to both academics and practitioners alike. We first aimed to measure and forecast systemic risk in global financial markets and then to construct a trade decision model for investors and financial institutions to assist them in forecasting risk and potential returns based on the results of the analysis of systemic risk. The factor copula-generalized autoregressive conditional heteroskedasticity (GARCH) models and component expected shortfall (CES) were combined for the first time in this study to measure systemic risk and the contribution of individual countries to global systemic risk in global financial markets. The use of factor copula-based models enabled the estimation of joint models in stages, thereby considerably reducing computational burden. A high-dimensional dataset of daily stock market indices of 43 countries covering the period 2003 to 2019 was used to represent global financial markets. The CES portfolios developed in this study, based on the forecasting results of systemic risk, not only allow spreading of systemic risk but may also enable investors and financial institutions to make profits. The main policy implication of our study is that forecasting systemic risk of global financial markets and developing portfolios can provide valuable insights for financial institutions and policy makers to diversify portfolios and spread risk for future investments and trade. Full article
(This article belongs to the Special Issue Forecasting Financial Markets and Financial Crisis)
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22 pages, 11080 KiB  
Article
Assessment of Conditional Dependence Structures in Commodity Futures Markets Using Copula-GARCH Models and Fuzzy Clustering Methods
by Małgorzata Just and Aleksandra Łuczak
Sustainability 2020, 12(6), 2571; https://doi.org/10.3390/su12062571 - 24 Mar 2020
Cited by 10 | Viewed by 3420
Abstract
The dynamic development of commodity derivatives markets has been observed since the mid-2000s. It is related to the development of e-commerce, the inflow of financial investors’ capital, and the emergence of exchange-traded funds and passively managed index funds focused on commodities. These advances [...] Read more.
The dynamic development of commodity derivatives markets has been observed since the mid-2000s. It is related to the development of e-commerce, the inflow of financial investors’ capital, and the emergence of exchange-traded funds and passively managed index funds focused on commodities. These advances are accompanied by changes in dependence structure in the markets. The main purpose of this study is to assess the conditional dependence structure in various commodity futures markets (energy, metals, grains and oilseeds, soft commodities, agricultural commodities) in the period from the beginning of 2000 to the end of 2018. The specific purpose is to identify the states of the market corresponding to typical patterns of the conditional dependency structure, and to determine the time of transition from one state to another. The copula-based Multivariate Generalized Autoregressive Conditional Heteroskedasticity models were used to describe the dynamics of dependencies between the rates of return on prices of commodity futures, while the dynamic Kendall’s tau correlation coefficients were applied to measure the strength of dependencies. The daily changes in the conditional dependence structure in the markets (changes in states of the markets) were identified with the fuzzy c-means clustering method. In 2000–2018, the conditional dependence structure in commodity futures markets was not stable, as evidenced by the different states of markets identified (two states in the grains and oilseeds market, the agricultural market, the soft commodities market and the metals market, and three states in the energy market). Full article
(This article belongs to the Special Issue Forecasting Financial Markets and Financial Crisis)
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16 pages, 2745 KiB  
Article
A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network
by Po Yun, Chen Zhang, Yaqi Wu, Xianzi Yang and Zulfiqar Ali Wagan
Sustainability 2020, 12(5), 1869; https://doi.org/10.3390/su12051869 - 02 Mar 2020
Cited by 11 | Viewed by 2069
Abstract
Predicting the carbon price accurately can not only promote the sustainability of the carbon market and the price driving mechanism of carbon emissions, but can also help investors avoid market risks and increase returns. However, previous research has only focused on the low-order [...] Read more.
Predicting the carbon price accurately can not only promote the sustainability of the carbon market and the price driving mechanism of carbon emissions, but can also help investors avoid market risks and increase returns. However, previous research has only focused on the low-order moment perspective of the returns for predicting the carbon price, while ignoring the shock of extreme events and market asymmetry originating from its pricing factor markets. In this paper, a novel extended higher-order moment multi-factor framework (EHM-APT) was formed to improve the prediction and to capture the driving mechanism of the carbon price. Furthermore, a multi-layer and multi-variable Long Short-Term Memory Network (Multi-LSTM) model was constructed so that the parameters and structure could be determined experimentally for testing the performance of the proposed framework. The results show that the pricing framework considers the shock of extreme events and market asymmetry and can improve the prediction compared with a framework that does not consider the shock of higher-order moment terms. Additionally, the Multi-LSTM model is more competitive for prediction than other benchmark models. This conclusion proves the rationality and accuracy of the proposed framework. The application of the pricing framework encourages investors and financial institutions to pay more attention to the pricing factor of extreme events and market asymmetry for accurate price prediction and investment analysis. Full article
(This article belongs to the Special Issue Forecasting Financial Markets and Financial Crisis)
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13 pages, 266 KiB  
Article
Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method
by Zhifeng Dai and Huiting Zhou
Sustainability 2020, 12(2), 541; https://doi.org/10.3390/su12020541 - 10 Jan 2020
Cited by 20 | Viewed by 3096
Abstract
Forecasting stock market returns has great significance to asset allocation, risk management, and asset pricing, but stock return prediction is notoriously difficult. In this paper, we combine the sum-of-the-parts (SOP) method and three kinds of economic constraint methods: non-negative economic constraint strategy, momentum [...] Read more.
Forecasting stock market returns has great significance to asset allocation, risk management, and asset pricing, but stock return prediction is notoriously difficult. In this paper, we combine the sum-of-the-parts (SOP) method and three kinds of economic constraint methods: non-negative economic constraint strategy, momentum of return prediction strategy, and three-sigma strategy to improve prediction performance of stock returns, in which the price-earnings ratio growth rate (gm) is predicted by economic constraint methods. Empirical results suggest that the stock return forecasts by proposed models are both statistically and economically significant. The predictions of proposed models are robust to various robustness tests. Full article
(This article belongs to the Special Issue Forecasting Financial Markets and Financial Crisis)
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