Special Issue "Developments in Risk Measurement, with Applications in Climate Change Finance and Economics"

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

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editors

Prof. Dr. Michael McAleer
Website
Guest Editor
Department of Finance, College of Management, Asia University, Taichung 41354, Taiwan Discipline of Business Analytics, University of Sydney Business School, Sydney 2006, Australia Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 Rotterdam, The Netherlands Department of Economic Analysis and ICAE, Complutense University of Madrid, 28040 Madrid, Spain Department of Mathematics and Statistics, University of Canterbury, Christchurch 8041, New Zealand Institute of Advanced Sciences, Yokohama National University, Yokohama 240-8501, Japan
Interests: theoretical and applied econometrics; financial econometrics; financial economics; finance, theoretical and applied statistics; time series analysis; forecasting; risk management; energy economics and finance; applied mathematics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Risk measures play a vital role in many fields with regards to climate change, global warming, environmental analysis, economics, and finance.

Using different risk measures could compare the performances of different variables through the analysis of empirical real-world data. For example, risk measures could help to form effective climate change, global warming, environmental analytic, monetary and fiscal policy policies, and to develop pricing models for financial assets, such as climate change risk, weather derivatives, equities, bonds, currencies, and financial derivative securities.

A Special Issue of “Risk Measures, with Applications in Climate Change Finance and Economics” will be devoted to advancements in the mathematical and statistical development of risk measures, with applications in climate change finance and economics. This Special Issue will bring together theory, practice and applications of risk measures on a topic that is of vital concern for climate change, global warming, environmental analysis, finance and economics.

We invite investigators to contribute original research articles in theory and applications of risk measures in climate change, global warming, environmental analysis, finance and economics.

All submissions must contain original unpublished work not being considered for publication elsewhere.

Prof. Chia-Lin Chang
Prof. Michael McAleer
Guest Editors 

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 papers will be 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 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

  • Climate change
  • global warming
  • environmental analysis
  • finance
  • economics
  • weather derivatives
  • financial derivatives

Published Papers (4 papers)

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Research

Open AccessArticle
Analysis of Risk Factors Affecting Firms’ Financial Performance—Support for Managerial Decision-Making
Sustainability 2019, 11(18), 4838; https://doi.org/10.3390/su11184838 - 04 Sep 2019
Cited by 1
Abstract
This paper aims to investigate how financial variables and exogenous crises influence firms’ financial performance, and how these factors may help managers in decision-making to increase their firm’s wealth. The dynamic interactions among variables were studied by applying a panel vector autoregressive model [...] Read more.
This paper aims to investigate how financial variables and exogenous crises influence firms’ financial performance, and how these factors may help managers in decision-making to increase their firm’s wealth. The dynamic interactions among variables were studied by applying a panel vector autoregressive model using annual data for a sample of non-financial firms from European countries. Results indicate that liquidity, leverage and productivity positively affect firm performance, while solvency and asset turnover are positive and statistically significant only in the case of return on equity. Labour productivity induces that firms tend to display larger efforts to keep financial performance in face of a crisis, considering that the crisis reveals a negative statistical impact over return on assets. Full article
Open AccessArticle
The Synergy of Financial Volatility between China and the United States and the Risk Conduction Paths
Sustainability 2019, 11(15), 4151; https://doi.org/10.3390/su11154151 - 01 Aug 2019
Abstract
Based on monthly data of six major financial variables from January 1996 to December 2018, this paper employs a structural vector autoregressive model to synthesize financial conditions indices in China and the United States, investigates fluctuation characteristics and the synergy of financial volatility [...] Read more.
Based on monthly data of six major financial variables from January 1996 to December 2018, this paper employs a structural vector autoregressive model to synthesize financial conditions indices in China and the United States, investigates fluctuation characteristics and the synergy of financial volatility using a Markov regime switching model, and further analyzes the transmission paths of the financial risk by using threshold regression. The results show that there is an approximately three-year cycle in the financial fluctuations of both China and the United States, and such fluctuations have a distinct asymmetry. Two thresholds were applied (i.e., 0.361 and 0.583), taking the synergy index (SI) as the threshold variable. The impact of the trade factor is significant across all thresholds and is the basis of financial linkages. When the SI is less than 0.361, the exchange rate factor is the main cause of the financial cycle comovement change. As the financial volatility synergy increases, the asset factor and interest rate factor start to become the primary causes. When the level of synergy breaks through 0.583, the capital factor based on stock prices and house price is still the main path of financial market linkage and risk transmission, but the linkage of monetary policy shows a restraining effect on synergy. Therefore, it is necessary to monitor the financial cycle and pay attention to the coordination between countries in terms of policy regulation. Full article
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Open AccessArticle
Application of Wavelet-Based Maximum Likelihood Estimator in Measuring Market Risk for Fossil Fuel
Sustainability 2019, 11(10), 2843; https://doi.org/10.3390/su11102843 - 18 May 2019
Cited by 2
Abstract
Energy commodity prices are inherently volatile, since they are determined by the volatile global demand and supply of fossil fuel extractions, which in the long-run will affect the observed climate patterns. Measuring the risk associated with energy price changes, therefore, ultimately provides us [...] Read more.
Energy commodity prices are inherently volatile, since they are determined by the volatile global demand and supply of fossil fuel extractions, which in the long-run will affect the observed climate patterns. Measuring the risk associated with energy price changes, therefore, ultimately provides us with an important tool to study the economic drivers of climate changes. This study examines the potential use of long-memory estimation methods in capturing such risk. In particular, we are interested in investigating the energy markets’ efficiency at the aggregated level, using a novel wavelet-based maximum likelihood estimator (waveMLE). We first compare the performance of various conventional estimators with this new method. Our simulated results show that waveMLE in general outperforms these previously well-established estimators. Additionally, we document that while energy returns realizations follow a white-noise and are generally independent, volatility processes exhibits a certain degree of long-range dependence. Full article
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Open AccessArticle
Debt Risk Evaluation of Toll Freeways in Mainland China Using the Grey Approach
Sustainability 2019, 11(5), 1430; https://doi.org/10.3390/su11051430 - 07 Mar 2019
Abstract
With a proactive loan policy to raise construction funds, a large number of toll freeways have been built in Mainland China in the past three decades. However, it brought about a long-term heavy debt burden for most provincial governments. To ensure financial sustainability [...] Read more.
With a proactive loan policy to raise construction funds, a large number of toll freeways have been built in Mainland China in the past three decades. However, it brought about a long-term heavy debt burden for most provincial governments. To ensure financial sustainability of toll freeways, an accurate and appropriate debt risk evaluation has become necessary. This research aims to explore debt risk factors and calculate the overall debt risk levels of toll freeways using the grey approach. Debt risk factors were identified as belonging to five categories—debt scale, debt structure, debt management, external environment, and solvency—and three new debt risk factors were added for specific concern of toll freeways—toll revenue, free cash flow, and earnings before interest, tax, depreciation, and amortization (EBITDA) margin. Debt risk levels of toll freeways in 29 provinces in Mainland China were evaluated by the proposed method and classified into three groups–low debt risk, medium debt risk, and high debt risk according to grey possibility degree ranges. Calculation results show that six provinces have low debt risk, 10 provinces have medium debt risk, and 13 provinces have high debt risk. Additionally, some specific policies to reduce toll freeway debt risk were provided based on the evaluation findings. Full article
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