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Statistical modeling and forecasting are very important for decision-making in any field of life. This paper has two major objectives, namely, statistical modeling and forecasting of real phenomena. For covering the first aim (i.e., statistical modeling), we introduce a new probabilistic model. The new model is introduced by mixing the Dagum distribution with the weighted T X family approach. The proposed model is called the weighted T X Dagum distribution and possesses heavy-tailed characteristics. The new model is illustrated by analyzing real-life data related to Bitcoin prices. To cover the second aim (i.e., forecasting), we take into account six macroeconomic and financial indicators to investigate their impact on Bitcoin prices such as the Adaptive least absolute shrinkage and selection operator (Alasso), elastic net, and minimax concave penalty. After analyzing the data, it is found that Alasso and MCP have retained all the included predictors, except import, while Enet holds all the predictors. The root means square error and mean absolute error associated with MCP are lower than Alasso and Enet, which reveals that MCP fits the data very well as compared to rival methods. [ABSTRACT FROM AUTHOR]

Renewable energy has demonstrated a significantly greater increase in its share of total energy consumption than carbon emissions in recent decades. This rise in renewable energy consumption did not effectively mitigate climate change, as evidenced by the accelerated rate of climate change. This paper aims to determine the cause of this failure by examining the total effect centrality and mediating effects of renewable energy in economic systems in the short run and long run. The total effect centrality and mediating effects of renewable energy illustrate the evolution of its overall impact on the major variables of an economic system and its role in mediating the indirect effects of these variables on carbon emissions, respectively. The study employs a recently developed network analysis that is a pioneer in the estimation of the time-varying total effect centrality and time-varying mediating effects of renewable energy, while also accounting for its cross-associations among all variables in a network. The study derives three conclusions by examining the three countries with the highest carbon emissions in their respective regions: the United States in North America, China in Asia, and Germany in Europe. First, renewable energy has become an essential element of economic systems, having a substantial total effect centrality on macroeconomic systems and, by way of its mediating effects, substantially mitigating the indirect effects of major macroeconomic variables on carbon emissions in the short run. Second, the total effect centrality and mediating effects of renewable energy diminish in the long run, indicating that its capacity to effectively mitigate climate change remains constrained in structural terms. Third, the long-term decline in total effect centrality rises by 0.3, 0.7, and 0.4 for the United States, Germany, and China, respectively. In the same nations, the long-term decline of mediating effects rises by 0.2, 0.4, and 0.2, respectively. The study provides pertinent policy recommendations on how to augment the total effect centrality and mediating effects of renewable energy in economic systems. [Display omitted] • Explains why renewable energy failed in curbing the accelaration of climate change. • Introduces the 'total effect centrality' of renewable energy in economic systems. • Illustrates the time-varying mediation role of renewable energy in economic systems. [ABSTRACT FROM AUTHOR]

Abstract The paper presents the use of the difference GMM and the system GMM for the purpose of determining the critical determinants of non-performing loans. The aim of the paper is to point out the theoretical advantages of GMM in relation to other (often used) panel data estimators in the study of NPLs, and to demonstrate its advantages in the case of the emerging markets in the countries of Latin America. The paper seeks to identify a relevant econometric model in order to demonstrate the impact of crucial macro and microeconomic variables on the NPLs. No evidence was found in support of the significance of the impact of the inflation rate and the microeconomic variables that were the subject of research in this paper. Highlights • To point out the theoretical advantages of GMM in relation to other (often used) panel data estimators. • To demonstrate its advantages in the case of the emerging markets in the countries of Latin America. • To identify a relevant econometric model. • To demonstrate the impact of crucial macro and microeconomic variables on the NPLs. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the impact of four major macroeconomic variables (private investment, public investment, oil production and inflation) on non-oil GDP in the oil-based Saudi Arabia. To this end, we use the nonlinear autoregressive distributed lag (NARDL) and the causality-in-quantiles methods to measure the impact of these variables on non-oil GDP. The results show that past non-oil GDP shocks affect current non-oil GDP strongly in the short term. Moreover, a surge in public investment increases non-oil GDP in both the short- and long-run, while a negative private investment shock reduces non-oil GDP in both the short- and long-run. Furthermore, positive (negative) oil production shocks increase the non-oil GDP also in the short- and long-run. In addition, we find a positive relationship between negative and positive inflation shocks and non-oil GDP in the long run, while negative inflation shocks decrease non-oil GDP. Using the nonparametric causality-in- quantile approach, we find that causality-in-the mean and causality-in-the variance emanating from the four explanatory variables vary across the quantiles. Finally, non-oil GDP does not Granger cause these macroeconomic variables. Those non-standard macroeconomic results for this major oil exporter are different from those for well-diversified developed countries. Regardless, they have important implications for Saudi policy makers involved in the Vision 2030 initiative, international organizations and institutional investors. Highlights • We study the impact of the macroeconomic variables on non-oil GDP in Saudi Arabia. • We use the nonlinear ARDL and the non-parametric causality-in-quantile methods. • A nonlinear relationship between the macroeconomic variables and the non-oil GDP is found. • There is evidence of causality-in-mean and -in-variance emanating for macroeconomic variables. • These relationships depend on the time horizons (short- and long-run) and the quantiles. [ABSTRACT FROM AUTHOR]

Highlights • We present an empirical study of stress testing for portfolios of auto loans. • Loans aged five years or more have significantly higher default probabilities. • This finding raises concerns about the increasing maturity of auto loans in recent years. • Model instability is an inherent challenge in stress testing. Abstract We present an empirical study of stress testing for portfolios of auto loans. We find that loans aged five years or more have significantly higher default probabilities. This finding raises concerns about the increasing maturity of auto loans in recent years. A challenge in stress testing is the instability of the estimated coefficient of macroeconomic variables, which raises questions on the reliability of stress test results. For this reason, it is important for model developers to perform sensitivity analyses and make conservative adjustment to minimize model risk. [ABSTRACT FROM AUTHOR]

This study examines the dynamic conditional correlations between the US and Korean financial markets and identifies the determinants of those correlations using the VAR-DCC-MGARCH model. We find that the Global Financial Crisis (GFC) affects both countries. Although the shocks to the Korean market before the GFC are not shared by the US market, those to the US market after the GFC are shared by the Korean market. We also examine the determinants of the dynamic conditional relations between the US and Korean markets using domestic macroeconomic variables and US/Korean financial variables. The results indicate that the US financial variables are more significant than domestic macroeconomic variables and that they have become increasingly important over time. [ABSTRACT FROM AUTHOR]

This study investigates the impact of bank-level and macroeconomic variables on bank fragility using a dynamic two-step GMM panel estimator on 433 banks in 46 African countries over the period 1997-2012. The study finds that both bank characteristics and macroeconomic variables are key drivers of bank fragility. The past experience of higher levels of non-performing loans (NPLs) significantly and positively determines current levels of NPLs. The growth of gross loan is negative and significant but economic growth leads to higher NPLs. The equity to assets ratio and the log of assets of banks are negatively associated with NPLs suggesting their potential to provide buffers to banks. Equally, total assets reduce bank fragility. These findings have important policy implications. The study shows that credit risk management initiatives, bank operation oversight and regulations should not be restricted in the times of financial crises, even during positive economic growth episodes in the business cycle. [ABSTRACT FROM AUTHOR]

Economic development of a state can be well defined as quantitative alterations in macroeconomic variables, which upsurge the overall production or yield over a certain period, equated to the preceding period. Amongst many some principal macroeconomic aspects that donate towards the progression of economic growth comprise Interest Rate, Forex Rate/Exchange Rate, Consumer Price Index (CPI - Inflation), Current Account Deficit, and Stock Prices. Secondary data were used, USA, Pakistan and rest of the World, from January, 2016 to December, 2022 with monthly frequency was utilized for estimation of coefficients. The empirical results show that equality has a negative impact on economic growth of USA, Pakistan and rest of the World. Results indicate that the pandemic have a significant effect on the interest rate of World, USA and Pakistan which increases in the start and then gradually decline. The exchange rate of USA, Pakistan and rest of the World is devalued significantly along with the rate of unemployment which increase and both affect significantly and negatively impact on the economy. The consumer price Index was decrease because of high uncertainty which have a negative effect on the GDP of USA, Pakistan and rest of the World. This brings the current account deficit to decreased due to less import and it has a significant impact on economy. [ABSTRACT FROM AUTHOR]

This paper employs the Partial Least Squares Structural Equation Modelling approach to analyse the impact of inflation, monetary policy rate, current account balance, money and quasi money supply per GDP, annual GDP growth rate and the total external debt on the Cedi to the US dollar exchange rate in Ghana with data spanning 1975–2014. The results show that the macroeconomic variables explain 82% of the adjusted variance in the cedi-dollar exchange rate with monetary policy rate, inflation and current account balance contributing 8.2%, 10.5% and 19.2% respectively. Money and quasi money supply per GDP, total external debt and annual GDP growth rate contribute 15%, 14.9% and 15.1% respectively to the explained variance in cedi-dollar exchange rate. Whereas monetary policy rate, inflation and current account balance have negative relationships with the cedi-dollar exchange rate, money and quasi money supply per GDP, total external debt and annual GDP growth rate have positive relationships with the cedi-dollar exchange rate. These results underpin the conclusion that inflation, monetary policy rate, current account balance, money and quasi money supply per GDP, annual GDP growth rate and the total external debt are significant predictors of the cedi-dollar exchange rate in Ghana. [ABSTRACT FROM AUTHOR]

Using the dynamic conditional correlation multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model, we empirically examine the dynamic relationship between stock market returns (KOSPI200 returns) and implied volatility (VKOSPI), as well as their statistical mechanics, in the Korean market, a representative and leading emerging market. We consider four macroeconomic variables (exchange rates, risk-free rates, term spreads, and credit spreads) as potential determinants of the dynamic conditional correlation between returns and volatility. Of these macroeconomic variables, the change in exchange rates has a significant impact on the dynamic correlation between KOSPI200 returns and the VKOSPI, especially during the recent financial crisis. We also find that the risk-free rate has a marginal effect on this dynamic conditional relationship. [ABSTRACT FROM AUTHOR]

• We examine how solar activity affects the oil volatility index. • We employ Granger- and step-by-step causality. • We estimate a relevant ARDL model. • Solar wind Granger- and step-by-step causes the oil volatility price. • Solar activity does indeed influence the market for oil prices. This study examines how solar activity affects the oil volatility index. To test whether solar phenomena affect the oil volatility price index, the Granger and Step-by-Step causality techniques are applied. Furthermore, we employ the autoregressive distributed lag order model (ARDL), which introduces as exogenous variables the Dow Jones Equity REIT index, the 3-month U.S. Government bond, the Oil Volatility Index, and the spread. As part of the model, we also include a solar variable, namely, the solar wind velocity, which is one of the most significant characteristics of the solar wind plasma. According to the results, the solar wind velocity "Granger causes" and "step-by-step causes" the oil volatility price index. All variables analyzed provide useful information for the modeling of the oil volatility index, which shows that solar activity does indeed influence the market for oil prices. [ABSTRACT FROM AUTHOR]

• This study investigates the impacts of the macroeconomic variables and GEPU on gold futures return predictability. • Few macroeconomic variables (e.g., inflation) can significantly have impact on gold futures excess returns. • Inflation can increase the forecast accuracy compared to other strategies. This study investigates the impacts of the macroeconomic variables and global economic policy uncertainty (GEPU) on gold futures return predictability using a simple regression. We find that few macroeconomic variables (e.g., inflation) can significantly have impact on gold futures excess returns. Out-of-sample results indicate that inflation can increase the forecast accuracy compared to other strategies, even during the COVID-19, two- and three-month ahead forecasts. However, the GEPU index is useless to predict the gold futures returns in various conditions and formulations. [ABSTRACT FROM AUTHOR]

This paper examines whether an extreme good air quality index (GAQI) is the superior predictor of stock market returns in China based on ordinary least squares method. This GAQI index is constructed based on data series from China Stock Market & Accounting Research Database. The results demonstrate that good air quality can increase stock market returns' forecasting accuracy more than most popular variables, thereby confirming the prediction validity of the GAQI. The GAQI further exhibits superior portfolio performance when considering different risk appetites and transaction costs, thereby revealing that risk-seeking investors use GAQI information to obtain better portfolio performance over risk-averse investors. The findings offer new insights for stock market returns' prediction based on air quality on the condition that air quality is undeniably a sharp focus in society. [Display omitted] • This paper investigates whether the extreme air quality index (GAQI) can predictor stock market returns in China. • Considering different types of risk appetite, we further check whether the GAQI is efficient in forecasting stock returns. • This paper finds that GAQI can obtain better portfolio performances under the existence of transaction costs. • This paper provides special evidence for predicting the stock market returns based on air quality. [ABSTRACT FROM AUTHOR]

In this paper, we aim to empirically test the relationship between current account deficit and different macroeconomic variables by using panel logit model. For this research, we selected 16 OECD member countries over the years of 2005-2009. Our empirical results indicate that there is a significant positive relationship between the current account deficit and public expenditure. On the other hand, significant negative relationships have been obtained between consumer price index (CPI), unemployment rate and the current account deficit. These three variables not only have a direct effect on the crisis indicator, but also have an indirect effect on the economies of 16 OECD member countries by increasing the probability of a volatile regime. [ABSTRACT FROM AUTHOR]

The use of non-parametric statistical methods, the development of models geared towards the homogeneous characteristics of corporate sub-populations, and the introduction of non-financial variables, are three main issues analysed in this paper. This study compares the predictive performance of a non-parametric methodology, namelyClassification/Regression Trees (CART), against traditional logistic regression (LR) by employing a vast set of matched-pair accounts of the smallest enterprises, known as micro-entities,from the United Kingdom for the period 1999 to 2008 that includes financial, non-financial, and macroeconomic variables. Our findings show that CART outperforms the standard approach in the literature, LR. [ABSTRACT FROM AUTHOR]

This paper investigates the predictability of severe simultaneous bear stock markets in 10 industrialized countries. Based on a set of US macroeconomic variables, all of the in-sample and out-of-sample results from probit models with a single macroeconomic variable and with more than one macroeconomic variables confirm that severe simultaneous bear stock markets are indeed predictable. In particular, while the inflation rate is the strongest predictor at longer forecast horizons, the relative long-term government bond yield and the stock return perform best at shorter forecast horizons. [ABSTRACT FROM AUTHOR]

Complex networks is an interdisciplinary field of study, effective in modeling various phenomena of strategic and/or market interest. Complex correlation networks between financial assets are mathematical abstractions that represent the relations between the financial returns of certain assets in a given period. The present work analyzed the Brazilian stock market, as well as the macroeconomic variables and indicators correlated to it in the context of complex networks. Based on the concept moving networks , 43 monthly complex networks were developed with relationships based on Pearson correlations between the logarithms of individual asset returns. To evaluate the impact of the oscillations of macroeconomic indicators on the topological structure of the network of assets, autoregressive vector models were used, as well as variance decomposition and Granger causality. The results of the Granger causality tests suggest that Gross Domestic Product, Risk Brazil, Ibovespa points and Interest rate influence the metrics density and number of components. The macroeconomic variables Gross Domestic Product, Risk Brazil and Ibovespa points presented, in general, higher explanatory power in relation to the variances of the density, transitivity and components number metrics. Among the positive and practical aspects related to this work, it is possible to highlight the use of global metrics of complex networks of assets as a support tool for investors and financial analysts in the detection of risk and volatility through oscillations in macroeconomic variables and policies. • Oscillations in the global network metrics characterized periods of financial crisis. • Macroeconomic variables presented explanatory power related to the global metrics. • The degree centrality distribution of the stock networks showed a negative skewness. • Network assortativity related to sectors and asset indices was not verified. • In almost all periods, the networks consisted of a single component. [ABSTRACT FROM AUTHOR]

• We applied a newly developed time-varying granger causality approach of Shi et al. (2018 , 2020) to explore the bidirectional causal relationship between S&P500 and macroeconomic variables. This new approach allows unknown change points in the causal relationships and also accommodates potential heteroscedasticity. It is also possible to determine the origination and termination dates for any episode of Granger causality. In complement, this methodology does not require detrending or differencing the data. • We investigated the bidirectional causality between macroeconomic factors and the stock market. Indeed, most studies focus on a unidirectional relationship. However, we believe that analyzing a double causality to assess the connection/disconnection and lead-lag effects is more relevant, especially during periods of recession or uncertainty, such as the Covid crisis. • This study incorporates the most recent data in order to judge the singularity (or not) of the health crisis concerning the interactions between stock market activity and macroeconomic indicators. • We integrated the "unemployment" variable in the VAR model. This variable is very little used in articles studying the interrelationships between macroeconomic variables and the stock market. However, we believe that the integration of this variable is interesting in several respects: it reflects the economic situation of a country, particularly during phases of recession. This study investigates the connection/disconnection between the stock market and macroeconomic fundamentals in the United States from January 1960 to December 2021. Using a recent time-varying Granger causality framework, tests revealed asymmetric bidirectionnal causality. The lead-lag relationships between stock prices and key macroeconomic indicators are more prevalent during recession phases. However, the significance and intensity of the causal relationships during the Covid crisis varied greatly; which could indicate a possible disconnection. [ABSTRACT FROM AUTHOR]

This paper examines the linkages between the development of telecommunications infrastructure (DTI), economic growth, and four key indicators of operation of a modern economy: gross capital formation, foreign direct investment inflows, urbanization rates, and trade openness. By studying the G-20 countries over the period 1991-2012 and employing a panel vector auto-regressive model for detecting Granger causality, we find a network of long-run causal connections between these variables, including bidirectional causality between DTI and economic growth. [ABSTRACT FROM AUTHOR]

Abstract: Artificial intelligence technologies have been used in a number of fields such as engineering, education, medicine and industry successfully. These technologies, likewise, can be applied to finance and economies to solve complex problems for example; the problems about the predicting of the domestic debt stock. The correct prediction of borrowing for the determination of macroeconomic targets concerning the future economic plans is of great importance, because, the fact that the correct prediction (or forecast) will bring about successful decisions and will provide benefit maximizing has increased the interest in Forecast Modeling. In this study, using 10 years’ monthly values of Currency Issued (CI), Total Money Supply (TMS), Consumer Price Index (CPI) and Interest Rate (IR), by means of Adaptive Neuro-Fuzzy Inference System, we have created a forecast model MFDD (Model of Forecasting the Domestic Debt) in order to predict domestic debt. It can be said that our MFDD model is very good with a strong estimation capability according to the findings in this study. [Copyright &y& Elsevier]

Abstract: Currency risk management decision involves deciding on when, how much and what hedging instrument (i.e., currency futures or options) should be used to hedge its risk exposure with the base currency. Intuitively the accuracy in forecasting the direction and magnitude of future exchange rate movements is central to currency risk management decision-making process. This research investigates the predictive performance of a hybrid multivariate model, using multiple macroeconomic and microstructure of foreign exchange market variables. Conceptually, the proposed system combines and exploits the merit of adaptive learning artificial neural network (ANN) and intuitive reasoning (fuzzy-logic inference) tools. An ANN is employed to forecast a foreign exchange rate movement which is followed by the intuitive reasoning of multi-period foreign currency returns using multi-value fuzzy logic for foreign currency risk management decision-making. Empirical tests with statistical and machine learning criteria reveal plausible performance of its predictive capability. [Copyright &y& Elsevier]

Abstract: Unlike most empirical studies on the administered protection measures that dealt with the developed countries, the present study reveals the relationship between macroeconomic variables and the number of investigations of antidumping duties in Korea. The unit root tests show that it is not unrealistic to assume that all concerned variables are integrated of order one. Johansen''s cointegration test shows that there is a long run equilibrium relationship between antidumping duties and real GDP growth rate. The error correction model reveals that protection measures such as antidumping duties lead to slowdown in the overall economic activities in Korea. [Copyright &y& Elsevier]

• We develop a tail index regression-based model to capture financial contagion • It can incorporate information in macroeconomic variables via the MIDAS technique • Implement the model using data of the US and five other developed markets • Structural breaks exist between the US and all tested markets around the financial crisis • Contagion exists between US as the originating market and Germany and HK In this paper, we develop a new approach for modeling financial contagion. It is combines the tail index regression, which specifically describes fat tails in asset returns, with the information contained in macroeconomic variables via the mixed data sampling technique in order to identify contagion in international equity markets. Empirically, our model successfully detects structural breaks in the tails of equity return distributions between the US and five developed economies during the recent Great Recession, and identifies the existence of contagion for two of them. The findings underscore our method as a flexible and reliable alternative for examining contagion. [ABSTRACT FROM AUTHOR]

This study examines the relationship between the returns and the volatilities of the stock market and macroeconomic fundamentals by using monthly data ranging from 1995:M7 to 2015: M6. For this purpose, we employ the Diebold and Yilmaz (2012) spillover index approach under the generalized VAR framework. The empirical results of total spillover index indicate no significant differences in the return and volatility connectedness between stock market and macroeconomic variables for China. The directional return and volatility spillover impact is comparatively stronger from stock market to the macroeconomic variables. The return and volatility spillovers in either direction, changed significantly after the global financial crisis of 2008. The findings of this study provide useful insights for investors and policy makers concerned with the return and volatility nexus between stock market and macroeconomic variables for China. • We check the relationship between stock market and macroeconomic fundamentals. • Diebold and Yilmaz (2012; 2014) spillover index approach is employed. • Total spillover show no significant differences across return and volatility nexus. • Directional spillover is comparatively stronger from stock market to macro variables. [ABSTRACT FROM AUTHOR]