Identifying the Best Financing Sources for Renewable Energy Companies in Latin American Countries

Dınçer, Hasan; Lisin, Anton; Ubay, Gözde Gülseven; Çağlayan, Çağatay

doi:10.1007/978-3-030-76783-9_1

Cited by 9 publications

(4 citation statements)

References 43 publications

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“…Unlike the GARCH model, which is excellent for assessing volatility and has been widely used in analyzing the volatility of financial markets, EXCF and GSADF are specifically designed to detect bubbles and periods of asset overvaluation, possessing the capability to capture more subtle and complex market dynamics that can predict the formation of bubbles. The choice of these methods is based on a careful review of the current state of knowledge in which numerous studies (Phillips et al 2015a;Phillips et al 2015b;Li et al 2020;Liaqat et al 2019;Dinçer et al 2021) have demonstrated the usefulness of these methods in detecting price bubbles.…”

Section: Discussion Conclusion and Policy Recommendationsmentioning

confidence: 99%

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

Nica,

Ionescu,

Delcea

et al. 2024

Risks

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This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments.

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Section: Discussion Conclusion and Policy Recommendationsmentioning

confidence: 99%

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

Nica,

Ionescu,

Delcea

et al. 2024

Risks

View full text Add to dashboard Cite

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“…Studies by Tsao et al (2021) and Bohland and Schwenen (2022) emphasized the temporal nature of subsidization and exposed disputes and tensions in funding, including coverage gaps and disputes among stakeholder groups. In addition to providing valuable insight into renewable energy distribution, innovation and policymaking (Donastorg et al 2022;Dinçer et al 2021), mobilizing financing for renewable energy in developing countries is necessary (Isah 2019). Recent attention has been directed toward renewable energy financing, owing to the imperative for heightened investment in clean energy to address global demand while mitigating environmental consequences (Qadir et al 2021).…”

Section: Sustainable Finance and Renewable Energymentioning

confidence: 99%

Sustainable Financing for Renewable Energy: Examining the Impact of Sectoral Economy on Renewable Energy Consumption

Getachew,

Lakner,

Desalegn

et al. 2024

Economies

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This study examines the effect of international financial flows, including investments and development assistance, on the expansion of renewable energy technologies. It also seeks to investigate the impact of the sectoral economy on the proportion of renewable energy consumption in Ethiopia. This study used an explanatory research design and a quantitative research approach. An autoregressive distributed lag model was applied to explore the long and short-term relationship among variables. A time series of data aggregated and disaggregated ranging from 2000 to 2022 was used. According to this study, sustainable finance programs are essential for advancing and aiding renewable energy projects in the long and short term. Ethiopia’s use of renewable energy will increase as sustainable finance rises. The main economic sectors determining Ethiopia’s consumption of renewable energy in the long and short term include the manufacturing, mining and service industries. This study’s findings imply that policies focusing on providing continuous financial support and fostering international cooperation to promote the development of the manufacturing sector are needed. This could include incentives for adopting renewable energy technologies and investing in renewable energy infrastructure. On the other hand, since the service and mining industries negatively impact renewable energy use, there is a need to diversify renewable energy sources beyond these sectors. This could involve promoting renewable energy projects in other sectors, such as manufacturing, agriculture, construction and trade. Based on the findings of this study, it is suggested that policymakers carefully consider the consequences within each economic sector when formulating decisions related to renewable energy. This study is novel in presenting empirical evidence linking renewable energy use to long- and short-term economic growth.

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“…As for the mobile platform, it is only used with mobile phones through digital banking applications (He 2015). Digital technology provides benefits to customers (Dinçer et al 2021). For customers, banking provides transactions that are convenient in terms of time, place, and cost.…”

Section: Digital Banking Transactionmentioning

confidence: 99%

Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

Saputra

Chaerani

2022

Risks

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The application of industry 4.0 in banking presents many challenges, with several operational risks related to downtime and timeout services due to system failures. One of the operational risk management steps is to estimate the value of the maximum potential losses. The purpose of this study is to estimate the maximum potential losses for digital banking transaction risks. The method used for estimating risks is the EVaR method. There are several steps in this study. The first step is to resample the data using MEBoot. This process is a simulation of the operational risk loss data of digital banking. Next, the threshold value is determined to obtain the extreme data value. Then, a Kolmogorov–Smirnov test is conducted to fit the data with the GPD. Afterward, the GPD parameter is estimated. Then, EVaR is calculated using a portfolio approach to obtain a combination of risk values as maximum potential losses. The analysis results show that the maximum potential loss is IDR144,357,528,750.94. The research results imply that the banks need to pay attention to the maximum potential losses of digital financial transactions as a reference for risk management. Therefore, banks can anticipate the adequacy of reserve funds for these potential risks.

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Identifying the Best Financing Sources for Renewable Energy Companies in Latin American Countries

Cited by 9 publications

References 43 publications

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

Sustainable Financing for Renewable Energy: Examining the Impact of Sectoral Economy on Renewable Energy Consumption

Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

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