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Finally, in the last decade of the twentieth century, the other classes of derivatives were introduced: credit derivatives, catastrophe derivatives, weather derivatives and property derivatives. The development of the theory and practice of risk management is closely related to the emergence of different risks. The rules proposed by the Basel Committee initially covered credit risk, but then grew to include market risk, operational risk and liquidity risk. This refers in particular to equity derivatives, currency derivatives, interest rate derivatives and commodity derivatives in the form of options, futures, forwards and swaps. [Extracted from the article]

With the occurrence of the global financial crisis in 2008, the U.S. unconventional monetary policy affected the Chinese market. Based on a monthly data sample from 2008M1 to 2015M12, in this paper we identify U.S. and Chinese monetary policy shocks by using a structural vector autoregression (SVAR) model with multi-external instrumental variables along with principal component analysis (PCA) combined with high-frequency financial market data. The empirical results show that the unconventional monetary policies had a negative effect on China's inflation and output due to the signal effect, and China's stock and commodity markets increased in the short term. During the same period, China's monetary policy had a greater impact on the domestic economy and financial markets. The conclusion of this paper provides a significant reference for relevant departments to make decisions amidst the new wave of unconventional U.S. monetary policies due to the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper presents the bibliometrics of a Keynesian and neoclassical discussion about the multiplier–accelerator effect. Having its oldest roots in the 1930s, there was a special emphasis in the 1960s and 1970s on discussions regarding the dependence of current investment on economic growth (the accelerator effect). Through a bibliometric analysis, we also consider the Hicks–Samuelson contribution, also known as the multiplier–accelerator model. We identified, among other things, the most relevant authors on the topics, the economic areas that have been contributed to the most through keyword analysis, and the most notable contributions through citation analysis. We concluded that several areas in economics have taken advantage of the discussion around the multiplier–accelerator effect, especially the discussion on the business cycle, structural dynamics, and public finance. [ABSTRACT FROM AUTHOR]

This study extends the concept of granularity from firms to cities, examining how large cities influence national economic dynamics beyond their relative size. By applying Zipf's law, which describes the power law distribution of city sizes, we investigate the interplay between granularity and business cycles. Our aim is to test the granular hypothesis that large cities have a significant impact on the business cycle beyond their relative size. We analyze data from American and Brazilian cities between 2003 and 2019 assessing the granular residuals and their explanatory power. Our findings reveal that in the United States, the granular city size is three metropolitan areas or five counties when redefined. In Brazil, it equates to three municipalities. These results emphasize the substantial role large cities play in national economic fluctuations, suggesting that policy interventions that target infrastructure, education, and innovation in major urban centers could have widespread economic benefits. This paper's contribution to the literature is to highlight a spatial component of granularity not considered so far. [ABSTRACT FROM AUTHOR]

This paper aims to examine the importance of financial intermediation in economic activity. We also explore the effects of monetary factors and financial frictions on the relationship between financial intermediation and economic growth, the drivers of business cycles, and how shocks spread through the intermediation process. Financial intermediaries improve fund allocation, minimize monitoring costs, minimize liquidity risk, simplify risk management, and facilitate portfolio diversification and resource allocation to more productive activities. In addition, financial intermediaries collect and analyze information about investment projects, allocating resources and managing information more efficiently than individual investors. We conclude that financial intermediation is significant for economic growth. In addition, we show that financial market frictions can amplify exogenous shocks, affecting investment, economic growth rates, and macroeconomic stability. Reducing financial frictions through intermediation is crucial. [ABSTRACT FROM AUTHOR]

Changes in interest rates, inflation, and exchange rates are the main components of macroeconomic risks (financial risks) in projects evaluation. However, the conduct of monetary policy as well as its impact on the economic environment is seldom considered as an important component of macroeconomic risks. In this paper, we offer a simple framework to analyze the conduct of monetary policy. We examine the stabilizing properties of monetary policy, its impact, and the parallels in the monetary policy approaches taken in the Czech Republic, Hungary, and Poland until the pandemic. We provide a simple theoretical background to motivate the main elements of the debate and the choice of policy strategy. We then rationalize the adoption of a form of flexible inflation targeting (FIT). It is characterized by an explicit concern over exchange rates. The empirical evidence, consisting of calibrated and extended Taylor rules, together with local projections estimates, suggests that monetary policy has been practiced with considerable flexibility by all three central banks and has contributed to business cycle stabilization in the region. Most notably, the exchange rate plays an important role in the conduct of monetary policy. [ABSTRACT FROM AUTHOR]

Canada's universities each receive an annual operating grant from their provincial government to partially finance operating expenses. This paper estimates the sensitivity of provincial operating grants to the business cycle by disentangling the effects of procyclical income on government revenue and the countercyclical effect on student demand by utilizing an economic regression model composed of three equations. Our panel data include the total real operating grant paid to all universities within a province, total student enrolment, real per capita government revenue, and real per capita gross domestic product for Canada's ten provinces over the 1992–2019 sample period. The results confirm that real per capita government revenues are procyclical and that full-time equivalent student enrolments are counter-cyclical. The total real operating grant is only weakly associated with cyclical changes in provincial government revenue. Instead, the total real operating grant is mainly determined by countercyclical changes in student demand. This partially offsets the potential reduction in funding to universities during an economic downturn. Provincial governments in Canada can smooth the total allocation over the business cycle by adjusting other expenditures and using debt financing. Our results suggest they do this to some extent, but not enough to avoid a net reduction in real operating grants during an economic downturn. [ABSTRACT FROM AUTHOR]

The existence of fluctuations is part of the narrative, especially when there is a slowdown (or worse, a contraction) in economic activity. The presence of long waves with a period of about 50 years as proposed by Kondratieff is one of the most controversial and fascinating theories about economic cycles. This paper analyses both the original Kondratieff data (from which the hypothesis started) and a dataset that includes GDP per capita for several significant countries. By applying the wavelet analysis (WA), the main objective of the paper is to understand whether it is plausible to support the existence of periodic fluctuations consistent with long cycles theory. The outcomes for Kondratieff's original dataset do not show the presence of a coherent periodicity for most cases. The same conclusion can be drawn for all the GDP per capita series. [ABSTRACT FROM AUTHOR]

Motivated by the observation that land supplied by the Chinese government is highly counter-cyclical with GDP fluctuations, this paper constructs a DSGE model to study the relationship between China's land supply policy and economic fluctuations, and further evaluate the welfare effects. By way of counterfactual exercises, this paper finds that endogenous land supply policy has "direct effect (production input channel)" and "indirect effect (intermediate goods channel)" on GDP fluctuations, and both tend to dampen economic fluctuations in China's macroeconomy. Specifically, GDP fluctuations increase by 63.35% without the "indirect effect", increase by 66.75% without the "direct effect", and increase by 66.79% without both effects. In addition, endogenous land supply policy can increase social welfare by about 1.38%. Verifying by the stylized facts in China, this paper argues that endogenous land supply is an efficient macro-control policy to smooth the economy and increase social welfare. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between the gross value added (GVA) of Greece's agricultural sector and the GVAs of the other sectors. The research considers both the relationship between value levels and the cycles of GVAs. Dynamic analysis using ARDL modeling shows that there is no cointegration between agricultural GVA and the other GVAs. However, there is an estimated cointegrating relationship between business cycles of agriculture and those of the rest of the economic sectors, with the cycles of services being the significant variable. Moreover, econometric analysis using NARDL modeling shows that there is a cointegrating relationship between the levels of GVAs as well, when asymmetricity—with respect to GVA changes of the services sector—is introduced. [ABSTRACT FROM AUTHOR]

Heritage tourism in Egypt, differentiated by its distinctive ancient wonders and cultural prosperity, has faced numerous challenges through its history, with political unrest, economic fluctuations, and, most recently, the global COVID-19 pandemic. This research paper investigates the dynamic interplay between planned and adopted resilience within the hotel sector in Egyptian heritage sites and their consequential effects on both social and economic sustainability. A quantitative research method was employed to empirically explore these dynamics. A structured questionnaire was distributed to 550 top and middle managers in hotels located in heritage sites, capturing insights into their perspectives on planned and adopted resilience. The collected data underwent rigorous analysis utilizing "partial least squares structural equation modeling" (PLS-SEM), providing a robust foundation for drawing meaningful conclusions. Findings from the research underscore the necessity of aligning planned and adopted resilience to generate sustainable social and economic performance. The synthesis of planned and adopted resilience was revealed to be pivotal in generating sustainable social and economic performance for hotels. This synthesis catalyzes the hotels' ability to mitigate uncertainties, adjust to changing environment, and ensure long-term viability. This research might contribute to the current literature by suggesting industry-specific awareness for the reciprocal relationship between planned and adopted resilience in the hotel businesses and their combined influence on both sides of sustainability (social and economic). The findings provide actionable recommendations for hotel management, policymakers, and industry stakeholders to enhance resilience, foster social cohesion, and ensure the economic sustainability of heritage tourism in an everchanging environment. [ABSTRACT FROM AUTHOR]

The results of previous research suggest that the elasticity of employment with respect to output is not constant within each phase of the business cycle and might depend on the maturity of that phase. Nevertheless, empirical evidence is almost non-existent. Using the unemployment gap as the proxy for the maturity of the business cycle phase, this paper seeks to determine heterogeneous elasticity across different business cycle phases. Furthermore, we aim to evaluate specific elasticities for separate demographic groups, considering gender, age, and educational attainment level, to identify the most vulnerable to jobless growth. Our specification is based on the employment version of Okun's law, and estimates are provided for the whole EU-27 panel covering the period from 2000 to 2022. Our results suggest that elasticity is higher when the unemployment gap is positive and increasing and lower when the gap decreases, regardless of the business cycle phase. Thus, it can be argued that the possibility of growth increasing employment is very limited when the economy operates at its potential level (full employment) for all demographic groups. [ABSTRACT FROM AUTHOR]

The aim of the paper is to analyze changes and peculiarities of behavior of economic agents with bounded rationality in the New Keynesian model, in which imported equipment and technology are one of the factors of production, and households consume only domestic products. The formation of output gap and inflation expectations by agents is based on stationary values of these variables and on extrapolation of the latest available data on inflation and the output gap. The weight shares of agents applying these rules change endogenously. Histograms of the frequency distribution of the degree of buoyancy and the impulse responses of monetary policy shocks and technology shocks to the variables under study show that a less open economy tends to go through an economic cycle with a smaller amplitude than a more rigid economy. Analyses of the trade-offs between the volatility of inflation and the output gap at different parameter values in the Taylor rule show their non-linear nature (in contrast to standard models with rational expectations). An important result obtained in this analysis is that the rational expectations hypothesis is more consistent with a closed economy than with an open one. [ABSTRACT FROM AUTHOR]

Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%. [ABSTRACT FROM AUTHOR]

Fiscal policy is one of the most crucial areas of government economic policy, and it has the potential to influence the economic growth of any nation. According to traditional Keynesian and Ricardian theories, fiscal policy should not be pro-cyclical, and counter-cyclical fiscal policy is the most effective alternative. Furthermore, the periodicity of fiscal policy is also heavily influenced by the quality of political institutions and democracies. Thus, this paper examines the relationship between fiscal policy and economic cycle in Vietnam, a developing economy with dramatic change since 2000. The results support the causal relationship between the set of fiscal policy factors, such as public debt, government tax revenues, and government expenditures, by analyzing quarterly data over a twenty-year period beginning in 2000 by using the Vector Error-Correction Model (VECM). Therefore, the adaptation of fiscal policy to the phases of the economic cycle and the effective deployment of fiscal policy tools help the sustainability of public finances and stimulate economic growth. [ABSTRACT FROM AUTHOR]

Some financial disruptions that started in California, U.S., in March 2023, resulting in the closure of several medium-size U.S. banks, shed new light on the role of liquidity in business cycle dynamics. In the normal path of the business cycle, liquidity and output mutually interact. Small shocks generally lead to mean reversion through market forces, as a low degree of liquidity dissipation does not significantly disrupt the economic dynamics. However, larger shocks and greater liquidity dissipation arising from runs on financial institutions and contagion effects can trigger tipping points, financial disruptions, and economic downturns. The latter poses severe challenges for Central Banks, which during normal times, usually maintain a hands-off approach with soft regulation and monitoring, allowing the market to operate. However, in severe times of liquidity dissipation, they must swiftly restore liquidity flows and rebuild trust in stability to avoid further disruptions and meltdowns. In this paper, we present a nonlinear model of the liquidity–macro interaction and econometrically explore those types of dynamic features with data from the U.S. economy. Guided by a theoretical model, we use nonlinear econometric methods of a Smooth Transition Regression type to study those features, which provide and suggest further regulation and monitoring guidelines and institutional enforcement of rules. [ABSTRACT FROM AUTHOR]

Functional data analysis has significantly enriched the landscape of existing data analysis methodologies, providing a new framework for comprehending data structures and extracting valuable insights. This paper is dedicated to addressing functional data clustering—a pivotal challenge within functional data analysis. Our contribution to this field manifests through the introduction of innovative clustering methodologies tailored specifically to functional curves. Initially, we present a proximity measure algorithm designed for functional curve clustering. This innovative clustering approach offers the flexibility to redefine measurement points on continuous functions, adapting to either equidistant or nonuniform arrangements, as dictated by the demands of the proximity measure. Central to this method is the "proximity threshold", a critical parameter that governs the cluster count, and its selection is thoroughly explored. Subsequently, we propose a time-shift clustering algorithm designed for time-series data. This approach identifies historical data segments that share patterns similar to those observed in the present. To evaluate the effectiveness of our methodologies, we conduct comparisons with the classic K-means clustering method and apply them to simulated data, yielding encouraging simulation results. Moving beyond simulation, we apply the proposed proximity measure algorithm to COVID-19 data, yielding notable clustering accuracy. Additionally, the time-shift clustering algorithm is employed to analyse NASDAQ Composite data, successfully revealing underlying economic cycles. [ABSTRACT FROM AUTHOR]

The distributed energy system (DES) represents an innovative approach to energy generation and distribution that promotes decentralization and diversification of energy sources. DESs can offer numerous benefits, including increased resiliency, reduced transmission losses, improved efficiency, and lower carbon emissions. The optimal design of a DES requires careful consideration of various factors such as geographical location, climate conditions, and energy demand patterns. This paper utilizes a multi-objective genetic algorithm to optimize the combination of technologies and their corresponding sizes in a distributed energy system for three types of commercial buildings—hospitals, large offices, and large hotels across eight different climate zones in the U.S. A range of technologies are considered for integration into the DES. These technologies include photovoltaic systems, wind turbines, combined heat and power systems, solar thermal collectors, and electrical and thermal energy storage. The two objectives considered are maximizing the reduction in carbon dioxide emissions and minimizing the life cycle costs for the DES. The purpose of this study is to optimize and evaluate the multi-objective design of distributed energy systems aimed at decentralizing and diversifying energy sources. The analysis of optimized DES designs across all 24 case scenarios shows that a balance between cost saving and emission reduction has been achieved. Although this study primarily focuses on specific buildings and climate zones, the methods and findings can be adapted for a wider variety of building types across different geographical locations, thus paving the way for more widespread adoption of optimized distributed energy systems. [ABSTRACT FROM AUTHOR]

This paper investigates whether business cycles cause financial cycles or vice versa. We also assess whether the US plays a leading role in causing the domestic business and financial cycles of other countries. The literature has established that business and financial cycles are linked through several channels such as credit constraints, the real effects of financial information and the reversal of overoptimistic expectations. Our analysis evaluates the direction of Granger causality using a novel approach based on the mixed-frequency vector autoregression model for the G7 countries. Our approach exploits the fact that real economic activity measured by industrial production is observed at a higher frequency than aggregate credit. We find strong evidence of bidirectional causality between the business and financial cycles, especially in recessions. Furthermore, the US is a global leader since the US business cycle significantly affects other countries' business cycles, especially in terms of expansions. [ABSTRACT FROM AUTHOR]

This paper discusses the future of the Stability and Growth Pact (hereafter SGP). Although Neoclassical economic models argue that strict fiscal and monetary rules minimize moral hazard and crowding out, in practice many governments adopt fiscal expansion (in recent years in the form of non-standard monetary measures) to mitigate market failures, consequently rethinking monetary rules and targets. Government spending and countercyclical policies are essential tools for soothing business cycles and other market failures. To this end, we empirically test whether current and past forms of the SGP have led to greater convergence, while we critically assess and investigate a possible SGP reform. By adopting more flexible rules, in terms of government spending and fiscal expansion, the Economic and Monetary Union (hereafter EMU) could yield multiple positive spillover effects in long-term economic growth under specific terms and conditions, such as green conditionalities. We conclude that to mitigate the triple crisis threat (economic, environmental and health), what is mostly needed are reforms in the form of fiscal federalism, such as common debt issuance (Eurobonds) that enhance the ability of the EMU to tackle the consequences of the aforementioned crises. [ABSTRACT FROM AUTHOR]

The evolution of the convergence among the European countries, including both Eurozone as well as non-Eurozone economies, is investigated in this paper. To do so, we construct correlation-based networks and study them by employing the Threshold Weighted-Minimum Dominating Set (TW–MDS) algorithm and analyzing standard quantitative performance graph theory metrics. Each country is represented by a network node, while the edges represent the cross-correlations calculated for a specific macroeconomic variable, for a given time window. To study the intertemporal evolution of the network's interconnections, we examine its structure in three consecutive time intervals: 1999–2004, 2005–2010 and 2011–2019. The empirical findings provide a mixed pattern. The European countries exhibit a common behavior over time for some macroeconomic variables, but not for all of them. [ABSTRACT FROM AUTHOR]

The short- and long-term volatility of oil and gas prices has a wide-ranging impact on both parties of petroleum contractual agreements, thus affecting the profitability of the project at any stage. Therefore, the government (first party) and the international oil company (second party) set the parameters of their contracts in a way that reduces the uncertainty. The effect of price fluctuations on economic indicators is investigated in this paper. The Taguchi method is used for the first time to find the best-agreement parameters, which are the "A" and "B" factors, in the standard Libyan agreement. There are four "A" components from "A1" to "A4", and four "B" components from "B1" to "B4". The purpose is to reduce the variability in the response variables, which are the company take (the percent of net cash flow for the international company) and average value of the second-party percent share of production (ASPS). The noise factors considered in this paper are oil, liquefied hydrocarbon byproduct (LHP), and gas prices. The method was applied to a case study of oil field development in Libya. The results showed that "A3" and "A4" were the most important control factors that affect the ASPS, while "B2" and "B3" are the most important factors affecting the company take. To obtain robust results, the most important factors to reduce variability were also determined. The effect of control parameters on the average NPV may be worth more than USD 22 MM in the 1-billion-barrel oilfield case study. The results showed that, for a given combination of "A" and "B" factors with a certain company take, the mean absolute deviation (MAD) of the NPV of the second party was reduced by 18% if the optimal combinations of the levels were used. [ABSTRACT FROM AUTHOR]

A large literature in behavioral finance explores how gambling sentiments influences trading in stocks. This paper considers the reverse phenomena; the impact of financial market outcomes on aggregate gambling expenditures. We expect the wealth effect of higher realized stock returns will increase gambling (entertainment good). Similarly, we expect rising volatility will attract gamblers to equity markets seeking thrill and skewed payouts. Utilizing novel horse wagering data (1934–2020), we study the impact of these forces on gambling expenditures. Using corporate bond spreads as a proxy for business cycles, we find that, in addition to financial market outcomes, price of wagering, incomes, and availability of competing betting products are important drivers of gambling. We also find that, ceteris paribus, gambling rises during recessions. Our findings will be of interest to policy makers and the finance industry, particularly as day trading, sports betting, online casinos, and other gambling gains broad public acceptance. [ABSTRACT FROM AUTHOR]

The GARCH model is one of the most influential models for characterizing and predicting fluctuations in economic and financial studies. However, most traditional GARCH models commonly use daily frequency data to predict the return, correlation, and risk indicator of financial assets, without taking data with other frequencies into account. Hence, financial market information may not be sufficiently applied to the estimation of GARCH-type models. To partially solve this problem, this paper introduces intraday high-frequency data to improve estimation of the volatility function of a semiparametric GARCH model. To achieve this objective, a semiparametric volatility proxy model was proposed, which includes both symmetric and asymmetric cases. Under mild conditions, the asymptotic normality of estimators was established. Furthermore, we also discuss the impact of different volatility proxies on estimation precision. Both the simulation and empirical results showed that estimation of the volatility function could be improved by the introduction of high-frequency data. [ABSTRACT FROM AUTHOR]

Real estate markets are frequently affected by growth and contraction cycles. Given the social and economic impacts of changes on real estate prices, the understanding of these cycles is crucial from a socio-economic perspective, but also, and more importantly, from a public policy view. The literature has provided several contributions focusing on the deconstruction of the main determinants of housing prices. This research focuses on the analysis of housing prices variation with a particular emphasis on the analysis of the impacts of the 2008 financial crisis. Within the existing body of knowledge, few studies have focused on this particular issue, and even fewer have focused on countries where the financial crisis led to an external bailout, as was the case in Portugal. The analysis confirmed that the 2008 financial crisis had a negative impact on real estate prices, and the ex-post growth in GDP and low interest rates had a positive impact. The paper also provides a long-term analysis of housing price trends over the last decades. [ABSTRACT FROM AUTHOR]

In the present paper, we show how uncertainty emanating from fluctuations in economic uncertainty, news-based uncertainty, and geopolitical risks affect the number of containers exported from Thailand via Penang Port, Malaysia. Our sample extends from January 2009 to May 2020 from three main entry points in the Northern Peninsular Malaysia–Thailand Border: Padang Besar, Surat Thani, and Bukit Kayu Hitam. Two modes of transportation of containers are mainly used for export purposes, namely, road and rai. This study examines the nonlinear effect of uncertainty on trade by employing a two-regime Markov regime-switching approach. The empirical results show that, overall, uncertainty significantly affects the movement of containers in the high-uncertainty regime. Therefore, small ports must continue to diversify their client base to cushion the impact of fluctuations in global trade due to uncertainty. [ABSTRACT FROM AUTHOR]

This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk associated with investment decisions is measured by the exponential Rényi entropy criterion, which summarizes the uncertainty in portfolio returns. Assuming asset returns are projected by a regime-switching regression model on the two market risk factors, we develop an entropy-based dynamic portfolio selection model constrained with the wealth surplus being greater than or equal to the shortfall over a target and the probability of shortfall being less than or equal to a specified level. In the empirical analysis, we use the select sector ETFs to test the asset pricing model and examine the portfolio performance. Weekly financial data from 31 December 1998 to 30 December 2018 is employed for the estimation of the hidden Markov model including the asset return parameters, while the out-of-sample period from 3 January 2019 to 30 April 2022 is used for portfolio performance testing. It is found that, under both the empirical Sharpe and return to entropy ratios, the dynamic portfolio under the proposed strategy is much improved in contrast with mean variance models. [ABSTRACT FROM AUTHOR]

Featured Application: The accuracy in long-term prediction of the performance of a photovoltaic (PV) park following several years of operation is essential for a number of reasons, including the extraction of reliable data for the economic analysis, the valid support of claims regarding PV panels' performance degradation and other issues. The specific work was motivated by the need to economically assess future investment in photovoltaic installations during an economic cycle with increasing interest rates. The monitoring data from the actual operation of a 100 kW PV park are employed in the refinement and validation of the prediction methodology. The results are applicable to the profitability analysis of future PV investments in the region of Thessaly. Increased penetration of grid-connected PV systems in modern electricity networks induces uncertainty factors to be considered from several different viewpoints, including the system's protection and management. Accurate short-term prediction of a grid-connected PV park's output is essential for optimal grid control and grid resilience. Out of the numerous types of models employed to this end during the last decade, artificial neural networks, (ANNs) have proven capable of handling the uncertainty issues of solar radiation. Insolation and ambient, or panel temperature, are most commonly employed as the independent variables, and the system's output power is successfully predicted within 3 to 5% error. In this paper, we apply a common type of ANN for the long-term prediction of a 100 kWp grid-connected PV park's output, by exploiting experimental data from the last 8 years of operation. Solar radiation and backsheet temperature were utilized for the ANN training stage. The performance metrics of this model, along with a standard linear regression model, are compared against the actual performance data. The capabilities of the ANN model are exploited in the effort to decouple the fluctuating effect of PV panel soiling which interferes with the efficiency degradation process. The proposed methodology aimed to quantify degradation effects and is additionally employed as a fault diagnosis tool in long-term analysis. [ABSTRACT FROM AUTHOR]

This paper addresses if and how excess debt can be considered as an early warning signal for banks and takes an additional dimension by comparing the excess leverage between Islamic and conventional banks in Indonesia before, during, and after the Global Financial Crisis (GFC). To do so, this research develops an empirical model of banks' capital structure and optimal and excess debt, and it conducts an empirical analysis on the overleveraging of eleven conventional and Islamic banks in Indonesia. Results show that all banks became vulnerable to the GFC in 2007–2009 while credit build-up, i.e., overleveraging, started in 2005. However, for most of the banks in the sample, Islamic banks performed better and leveraged less prior to the GFC, which made them more resilient to the crisis. [ABSTRACT FROM AUTHOR]

This study investigates the impact of financial integration on international dynamics from the perspective of volatility shocks. By incorporating time-varying volatilities, recursive preferences, and a global bank into the IRBC model, it illustrates that volatility shocks trigger precautionary saving incentives, but the specific effects vary based on the type of shock. Financial integration facilitates international capital flows and leads to an unequal distribution of international bank loans between two countries, resulting in greater divergence in their business cycles in the presence of productivity volatilities. In contrast, countries with higher financial integration experience more synchronized business cycles, due to simultaneous fluctuations in the international financial market, ultimately yielding greater synchronization in the face of financial volatilities. Disregarding volatility shocks leads to underestimating the impact of financial integration on the comovement of business cycles across countries. Furthermore, welfare analysis also indicates that financial markets play a crucial role in enhancing social welfare, regardless of the type of volatility. [ABSTRACT FROM AUTHOR]

In this paper, we suggest a holistic explanation for the role currently played by self-employment in less developed countries and, consequently, a reason for its persistence and growth in recent years. At the same time, we link this explanation with current discussions on the role of very small economic units and informality and their relation to unemployment rates. In general, countries where the combined share of self-employment and very small units is high exhibit lower unemployment rates than countries of similar levels of development, but where the small units' share is less significant. Using a rich set of labor data for Mexico, we examined flows between seven different labor statuses. Equilibrium vectors estimated through Markov matrices show that flows are linked to the economic cycle and that there exists a gender difference in behavior in these flows. We found that a dichotomic explanation of self-employment persistence (entrepreneurial reservoir/survival strategy) is not supported by evidence. We also found that strong links exist between the size of the self-employed sector and unemployment rates. With the help of a set of surveys on very small economic units, we identified a three-tier structure for very small economic units in Mexico. Thus, self-employment and small-sized economic units are both a source of income and a source of cheap goods for lower-income sectors. They serve as a buffer against unemployment and help to provide subsistence conditions for the poorest segment of the population. Finally, we discuss some questions that were raised along the text that help to guide future research. [ABSTRACT FROM AUTHOR]

Decarbonization is an ongoing issue in the shipping industry, and electric propulsion systems are being proposed as alternative solutions to satisfy this requirement. To answer the fundamental questions of "Are electric propulsion systems the green solution?" and "Can DC distribution systems contribute to the decarbonization in shipping?", this paper analyzed DC distribution system electrical characteristics, economic feasibility, and environmental evaluation for a full-scale AC-DC hybrid distribution electric propulsion system facility. By applying the actual ship's operating profile as inputs, a DC distribution system with an active front end rectifier and a variable speed generator engine was proven to guarantee the same electric stability as the conventional AC distribution system. The life cycle assessment results achieved economic and life cycle environmental benefits of about 10% (8.9% for Case 1 and 12.4% for Case 2). These research findings offer meaningful insights into the DC distribution system to minimize fuel consumption and emissions toward cleaner shipping. [ABSTRACT FROM AUTHOR]

The green bond market is emerging as an impactful financing mechanism in climate change mitigation efforts. The effectiveness of the financial market for this transition to a low-carbon economy depends on attracting investors and removing financial market roadblocks. This paper investigates the differential bond performance of green vs non-green bonds with (1) a dynamic portfolio model that integrates negative as well as positive externality effects and via (2) econometric analyses of aggregate green bond and corporate energy time-series indices; as well as a cross-sectional set of individual bonds issued between 1 January 2017, and 1 October 2020. The asset pricing model demonstrates that, in the long-run, the positive externalities of green bonds benefit the economy through positive social returns. We use a deterministic and a stochastic version of the dynamic portfolio approach to obtain model-driven results and evaluate those through our empirical evidence using harmonic estimations. The econometric analysis of this study focuses on volatility and the risk–return performance (Sharpe ratio) of green and non-green bonds, and extends recent econometric studies that focused on yield differentials of green and non-green bonds. A modified Sharpe ratio analysis, cross-sectional methods, harmonic estimations, bond pairing estimations, as well as regression tree methodology, indicate that green bonds tend to show lower volatility and deliver superior Sharpe ratios (while the evidence for green premia is mixed). As a result, green bond investment can protect investors and portfolios from oil price and business cycle fluctuations, and stabilize portfolio returns and volatility. Policymakers are encouraged to make use of the financial benefits of green instruments and increase the financial flows towards sustainable economic activities to accelerate a low-carbon transition. [ABSTRACT FROM AUTHOR]

In the burgeoning field of bitcoin research, a cohesive understanding of how knowledge and insights have evolved over time is lacking. This study aims to address this gap through an exploration of 4123 academic articles pertaining to bitcoin. Utilizing co-word analysis and main path analysis (MPA), it uncovers key themes and seminal works that have substantially influenced the field's progression. The identified clusters, including safe haven, internet of things (IoT), proof of work (PoW), market efficiency, sentiment analysis, digital currency, and privacy, shed light on the multifaceted discourse surrounding bitcoin. The MPA, incorporating both forward and backward local paths, traces an evolving narrative, starting from an in-depth exploration of bitcoin's structure, anonymity, and contrasts against traditional financial assets. It tracks the shift in focus to broader market dynamics, volatility, speculative nature, and reactions to economic policy fluctuations. The analysis underscores the transformation of bitcoin research, from its beginnings as a decentralized, privacy-oriented currency to its role in global economics and green financing, revealing a complex narrative of an innovative financial instrument to a multifaceted entity. Implications drawn from this analysis include the need for further research on the potential integration of bitcoin within emerging technologies like AI and cybersecurity, the implications of bitcoin's interplay with traditional financial systems, and the environmental impacts of bitcoin and blockchain utilization. Overall, the current study not only enhances our understanding of the bitcoin field but also charts its dynamic evolution and stimulates further academic inquiry. [ABSTRACT FROM AUTHOR]

The "technology bubble" in the late 1990s, the financial crisis in 2007/2008, and the Eurozone crisis generated significant losses across several asset classes. The objective of this paper is to investigate risk premia factors such as size, value, momentum, carry, quality, and low volatility and their time-variant behavior. The time-variant behavior of these risk premia baskets has been analyzed based on different financial conditions: The business cycle, the yield curve, equity market momentum, and different risk conditions. Factor calculations are based on the MSCI World universe. The monthly data set ranges from January 1995 to September 2017. The results underpin the prevalent observation that equity risk factors consistently outperform the broad market and therefore generate significant alpha. However, the paper shows that a dynamic allocation of risk factors can achieve an attractive return–risk relation. The study shows very clearly how different risk factors behave in different financial conditions and that an allocation to more offensive or more conservative risk factors can outperform a diversified, equally-weighted portfolio. [ABSTRACT FROM AUTHOR]

In the context of increasingly severe resource and environmental constraints, accelerating family farms to take the path of green agricultural development is an urgent practical problem to be solved. The bancassurance interaction, an innovative form of financial support policy for agriculture, can effectively alleviate the risks and credit rationing problems faced by family farms in the operation process, provide new opportunities for green production of family farms, and is of great significance to promoting sustainable agricultural development. This study uses data from 564 planting family farms in Shaanxi Province to analyze the impact of the bancassurance interaction on adopting green production technology in family farms and its mechanism, paying particular attention to the heterogeneous effects of the family life cycle and family economic level. The results of this study show that the bancassurance interaction has a significant positive impact on the adoption of green production technology by family farms. Compared with agricultural credit and insurance, the effect of the bancassurance interaction on adopting green production technologies is more evident. The analysis of the impact mechanism shows that the bancassurance interaction can promote the adoption of green production technology in family farms through three channels: increasing investment in agricultural production, expanding the scale of land management, and strengthening exchanges of green technology. Among them, the effect of increasing agrarian production investment is the most pronounced. The heterogeneity analysis shows that the impact of the bancassurance interaction on the green production technology of family farms varies significantly according to the family life cycle and economic level. Family farms at the dependency stage and low-income level were more willing to adopt green production technologies driven by the bancassurance interaction. Therefore, it is necessary to continuously innovate the interaction mode between banks and insurance companies, rationally allocating agricultural production factors and combining the actual situation of each family farm to strengthen the leading role of the bancassurance interaction in adopting green production technologies and promoting high-quality agricultural development. [ABSTRACT FROM AUTHOR]

Capital and technology gradually replace land and labor factors to become the important production factors of enterprises, especially for artificial intelligence enterprises. How to use capital and technology factors to enhance enterprise value becomes an important topic when artificial intelligence enterprises are affected by internal and external factors. Using data from 55 AI concept companies, a multiple regression model was constructed to verify the mediating role of capital and technology, judge the dynamics of the two elements dialectically, and explore the paths that shape the value enhancement of AI companies. The study found that both degree of enterprise attention and government support positively affect firm value, and that the mediating role of both capital and technology is more pronounced. However, compared to the technology element, the capital element plays a leading role in the value enhancement mechanism of AI companies, with physical capital being the most effective, and strengthening the capital element is a more sensible path choice to increase the value of companies in the short term. However, in the long term, as the business cycle and the external environment change, the technology element is a driving force that cannot be underestimated. [ABSTRACT FROM AUTHOR]

This study investigates the association between economic uncertainty and audit quality in the BRICS nations, examining both input-based (e.g., audit fees, auditor tenure) and output-based (e.g., restatements, total accruals) measures of audit quality. Utilizing a dataset of 83,511 firm-year observations from 1995–2022, it reveals a significant negative impact of economic uncertainty on audit quality. Additionally, the research explores the moderating role of CEO power, employing principal component analysis to merge various indicators of CEO influence. Findings indicate that powerful CEOs can mitigate the adverse effects of economic uncertainty on audit quality, suggesting a U-shaped relationship between CEO power and audit quality. Methodologically robust, employing techniques like two-stage least squares (2SLS) and two-stage system generalized method of moments (system GMM) to address endogeneity, the study offers a comprehensive analysis of audit quality in the context of economic fluctuations and corporate governance, contributing significantly to the understanding of these dynamics in emerging economies, particularly in the diverse and influential BRICS nations. This study's findings have significant implications for stakeholders and policymakers, providing insights that can inform policy decisions and enhance corporate governance frameworks. [ABSTRACT FROM AUTHOR]

Economic and financial narratives inform market sentiment through the emotions that are triggered and the subjectivity that gets evoked. There is an important connection between narrative, sentiment, and human decision making. In this study, natural language processing is used to extract market sentiment from the narratives using FinBERT, a Python library that has been pretrained on a large financial corpus. A news sentiment index is constructed and shown to be a leading indicator of systemic risk. A rolling regression shows how the impact of news sentiment on systemic risk changes over time, with the importance of news sentiment increasing in more recent years. Monitoring systemic risk is an important tool used by central banks to proactively identify and manage emerging risks to the financial system; it is also a key input into the credit loss provision quantification at banks. Credit loss provision is a key focus area for auditors because of the risk of material misstatement, but finding appropriate sources of audit evidence is challenging. The causal relationship between news sentiment and systemic risk suggests that news sentiment could serve as an early warning signal of increasing credit risk and an effective indicator of the state of the economic cycle. The news sentiment index is shown to be useful as audit evidence when benchmarking trends in accounting provisions, thus informing financial disclosures and serving as an exogenous variable in econometric forecast models. [ABSTRACT FROM AUTHOR]

In this paper, a peak shaving and frequency regulation coordinated output strategy based on the existing energy storage is proposed to improve the economic problem of energy storage development and increase the economic benefits of energy storage in industrial parks. In the proposed strategy, the profit and cost models of peak shaving and frequency regulation are first established. Second, the benefits brought by the output of energy storage, degradation cost and operation and maintenance costs are considered to establish an economic optimization model, which is used to realize the division of peak shaving and frequency regulation capacity of energy storage based on peak shaving and frequency regulation output optimization. Finally, the intra-day model predictive control method is employed for rolling optimization. An intra-day peak shaving and frequency regulation coordinated output optimization strategy of energy storage is proposed. Through the example simulation, the experiment results show that the electricity cost of the whole day is reduced by 10.96% by using the coordinated output strategy of peak shaving and frequency regulation. The obtained further comparative analysis results and the life cycle economic analysis show that the profit brought by the proposed coordinated output optimization strategy is greater than that for separate peak shaving or frequency modulation of energy storage under the same capacity. [ABSTRACT FROM AUTHOR]

On 11 March 2020, the WHO declared the COVID-19 epidemic to be a global pandemic. This was a consequence of the rapid increase in the number of people with positive test results, the increase in deaths due to COVID-19, and the lack of pharmaceutical drugs. Governments introduced national lockdowns, which have impacted both energy consumption and economies. The purpose of this paper is to answer the following question: do COVID-19 lockdowns affect the business cycle? We used the cycle clock approach to assess the magnitude of decrease in electricity consumption in the three waves of the epidemic, namely, April 2020, November 2021, and April 2021. Additionally, we checked the relation between energy consumption and GDP by means of spectral analysis. Results for selected 28 European countries confirm an impact of the introduced non-pharmaceutical interventions on both energy consumption and business cycle. The reduction of restrictions in subsequent pandemic waves increased electricity consumption, which suggests movement out of the economic recession. [ABSTRACT FROM AUTHOR]

Recently, several studies have argued about the interactions of the real economy and financial system, as well as the importance of financial cycles in business cycle fluctuations. To date, there exists near consensus among central bankers, economists, and other scholars that the financial cycle is an important source of business cycle fluctuations. This has raised the question of whether monetary policy should respond to financial instability and/or imbalances. As a result, we asked the following questions: Should monetary policy lean against the wind by targeting the aggregate financial cycle in South Africa? And what is the role of monetary policy in minimizing financial imbalances and instabilities in South Africa? The present article aims to provide answers to the above-mentioned question. Through the adoption of a multiple-equation generalized method of moments and structural vector autoregressive approaches, this article simultaneously estimates and compares both the finance-augmented and the conventional Taylor rules. It is shown that the South African Reserve Bank has considered developments in the aggregate financial cycle in setting its policy rate. Overall, there is clear evidence to conclude that the South African Reserve Bank can lean against the wind by targeting the aggregate financial cycle, but only as a genuine augmentation not as a fully flagged objective. This article adds new evidence to the South African literature on the prevailing debate of whether monetary policy should respond to developments in the financial system. [ABSTRACT FROM AUTHOR]

The evolving global economic landscape necessitates adaptive monetary policies, especially for economies like South Korea that are deeply integrated with global markets. This research explores the strategic recalibrations of the Bank of Korea's monetary policy amid fluctuations in global economic uncertainty. Utilizing a sophisticated microeconomic theoretical framework, this study employs Bayesian estimation techniques and impulse response analysis to dissect the dynamic effects of these global shocks on South Korea's macroeconomic stability and policy direction. Our findings reveal that the Bank of Korea has adeptly navigated through turbulent economic conditions induced by external shocks through well-coordinated policy adaptations. These adaptations, which include both traditional and innovative monetary tools, have been crucial in stabilizing the financial environment and promoting economic growth. By detailing the tailored application of the Taylor rule within the Korean context and strategic foreign exchange interventions by the central bank, this study contributes significantly to the broader discourse on the efficacy of monetary policy in open economies and offers insights on integrating advanced analytical methods into economic policy analysis. [ABSTRACT FROM AUTHOR]

This research aims to explore the fiscal and public finance viability on climate physical risk externalities cost for building social-economic-environmental sustainability. It analyzes climate physical risk impact on the real business cycle to change the macroeconomic output functions, its regressive cyclic impact alters tax revenue income and public expenditure function; This research also analyzes that the climate physical risk escalates social-economic inequality and change fiscal-financial policy functions, illustrates how the climate damage cost and adaptation cost distorts fiscal-finance cyclical and structural equilibrium function. This research uses binary and multinomial logistic regression analysis, dynamic stochastic general equilibrium method (DSGE) and Bayesian estimation model. Based on the climate disaster compensation scenarios, damage cost and adaptation cost, analyzing the increased public expenditure and reduced revenue income, demonstrates how climate physical risk externalities generate binary regression to financial fiscal equilibrium, trigger structural and cyclical public budgetary deficit and fiscal cliff. This research explores counterfactual balancing measures to compensate the fiscal deficit from climate physical risk: effectively allocating resources and conducting the financial fiscal intervention, building greening fiscal financial system for creating climate fiscal space. [ABSTRACT FROM AUTHOR]

The aim of this study is to delve into the intricate the mechanism through which alterations in currency exchange rates give rise to shifts in inflation rates, while taking into careful consideration the country's economic cycle. In order to accomplish this objective, we used a dataset that spanned from 1 January 1999 to 1 July 2023, focusing our analytical lens on three specific geographic areas, namely the Eurozone, the United Kingdom, and Canada. In our pursuit of understanding this complex relationship, we employed the Markov Switching Vector Autoregressive model. Our research outcomes can be succinctly encapsulated as follows: in the initial stages, particularly during phases characterized by robust economic growth, the transmission of exchange rate effects onto inflation levels appeared to exhibit a partial impact across all geographic areas under examination. However, during periods marked by economic downturns, both the United Kingdom and Canada displayed a distinctly more comprehensive transmission of these effects. Moreover, the prevailing projections for the forthcoming time horizon, across all the countries encompassed by our study, strongly indicate the onset of an expansionary phase that is projected to extend over a span of 25 months. Lastly, concerning the implications of unexpected disturbances or shocks, it is noteworthy that the response of exchange rates to inflation induced shocks was neither immediate nor as pronounced as the corresponding reaction of inflation to sudden shifts in exchange rates. [ABSTRACT FROM AUTHOR]

This study examines the relationship between the business sentiment of Japanese companies regarding promising or potential countries for investment and macroeconomic statistics, such as economic or population growth in Thailand, using data from the Survey Report on Overseas Business Operations by Japanese Manufacturing Companies from 1992 to 2022. Although investing in Thailand has been popular among Japanese companies since the late 1980s, it has seemingly become relatively inactive in recent years. The present study's results are summarized as follows: First, the business sentiment of Japanese companies has some relationships with relatively short-term economic growth and the business cycle in the short run. Second, business sentiment depends on long-term trends, and this stance may have changed after 2020. Third, other elements, such as minimum wage or fewer young people, do not necessarily have a relationship with business sentiment. Although more studies including capital accumulation or the global value chain should be conducted, improving the sentiments of Japanese businesspersons is desirable. [ABSTRACT FROM AUTHOR]

As global fish consumption rises, a large amount of waste is generated that is generally neglected. Considering the value embedded in these resources, sustainable methods become more important in extracting valuable ingredients from fish processing residues. Enzymatic hydrolysis is a fast and easily reproducible method for recovering protein ingredients and obtaining valuable by-products. To confirm its advantages, an environmental and economic impact assessment is essential. This study overviewed the sustainability and economic viability of extracting protein compounds and oil from Atlantic mackerel processing residues using enzymatic hydrolysis. Life cycle assessment (LCA) and life cycle cost analysis (LCCA) methods were employed. It was found that the climate change impact of the whole process was 0.073 kg CO2-eq per 1 g of fish protein hydrolysate (FPH). As the process produces FPH as the main product and fish oil as the by-product, economic allocation was used to distribute the impacts of FPH and fish oil. The findings of the LCCA showed that producing 1 g of FPH costs EUR 3.68. The contribution analysis indicated the crucial role of electricity and fish in environmental impacts. To ensure the accuracy of the calculation, the results of an LCA study published previously were recalculated. The sensitivity analysis showed that the results were susceptible to the region and source of electricity production. This research provides valuable insights into the sustainability and economic aspects of using enzymatic hydrolysis for extracting protein ingredients and oils from Atlantic mackerel. This can inform future investigations of environmentally friendly and economically viable solutions for extracting fish ingredients. [ABSTRACT FROM AUTHOR]

The primary objectives of this research are to compare the economic performance of the Member States (MS) in the Euro Area 12 (EA-12) of the European Union (EU) that served as net contributors and net recipients of its budget during the period 1998–2022. The comparison focuses on aspects related to economic growth and the business cycle, exploring the presence of hysteresis. To achieve these objectives, a novel approach is employed, which integrates the analysis of both growth and cycles. This approach involves using the Seemingly Unrelated Regression Equations (SURE) model to estimate a translog production frontier and assess the impact of lagging output gaps on the potential output or capacity for each of the MS considered. The data used to estimate each frontier and analyze growth decomposition consist of quarterly data filtered using the Kalman filter. The results reveal that lagging output gaps, occurring one or two quarters prior, significantly affect the potential output or capacity of the EA-12 MS, thereby supporting the existence of hysteresis. However, the impacts appear to be minimal. Furthermore, the research concludes that changes in total factor productivity (TFP), mainly due to technical change, play a more crucial role in the growth of MS that are net contributors to the EU budget. Conversely, total factor accumulation (TFA), mainly physical capital accumulation, has a more pronounced impact on the growth of MS net recipients from the EU budget. Interestingly, the heterogeneity observed among the EA-12 MS extends beyond the simple division between net contributors and net recipients of the EU budget. This heterogeneity raises questions about the European Central Bank's (ECB) ability to stabilize the economies of these various MS solely through conventional monetary policy. [ABSTRACT FROM AUTHOR]

The auxiliary regulation capacity of pumped-storage power stations can be utilized as an effective method to regulate the output of a hydro-photovoltaic complementary system, further mitigating the power fluctuations of the system and enhancing the photovoltaic absorption. This study aims to minimize power fluctuations and maximize the economic benefits of electricity generation in a hydropower-photovoltaic-pumped-storage complementary system (HPPCS), which are treated as the objective functions. It explores the participation of the HPPCS in grid active power balance auxiliary services. By modulating the participation ratio of the HPPCS in the grid's active balance service, the system output is aligned to fluctuate proportionally with the daily load curve trend. Consequently, a short-term economic dispatch model for the integrated HPPCS is developed. The case study focuses on the considerable impact of weather conditions on photovoltaic (PV) power generation. In this model, the outputs of cascading hydro-power stations and pumped-storage power stations are considered as the decision variables. A decomposition-based multi-objective evolutionary algorithm is applied to derive an optimized intra-day dispatch Pareto solution set for the cascading HPPCS in each of these scenarios. Additionally, this study compares the Pareto solution sets for the HPPCS in various extents of its participation in grid auxiliary services. The results of the case study suggest that the system is capable of timely adjustments during the peak and trough periods of load demand. Considering the economic benefits, it enables the pumped-storage station to generate electricity for the grid during periods of high electricity prices and to store energy by pumping water when prices are low. [ABSTRACT FROM AUTHOR]

The European Union (EU), which has led international discussions on global warming, officially announced its plan for the Carbon Border Adjustment Mechanism (CBAM) in July 2021. Many existing studies have indicated the CBAM will curtail greenhouse gases, and will subsequently be positive in terms of reducing global warming. However, serious legal issues and trade disputes are expected in terms of the compatibility of the CBAM with the trade rules of the General Agreement on Tariffs and Trade (GATT). Contrary to the EU's explanation, the international community has a strong view of CBAM as a new trade barrier under the guise of preventing global warming. Above all, this is because it is an arbitrary measure by the EU and not the one that has been internationally agreed upon. Therefore, this paper tries to identify the pitfalls and estimate the global cost of CBAM, arguing that the mechanism is not in line with international trade rules, and that many countries will not sit back and suffer from it. The world economy will inevitably face a vicious cycle of trade retaliation. The CBAM will drive up trade costs and cause another trade distortion. While the goal of preventing climate change is good, the CBAM scheme is too costly for the world economy. [ABSTRACT FROM AUTHOR]