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The purpose of the article is to analyze the relevance of earnings fundamentals in predicting extreme price reversals of an emerging stock market. We collect monthly price data on six sector indices from Bombay Stock Exchange (BSE) of India for the period 2004–2019. The research decomposes industry stock returns into Potential Maximum Gains (PMG) and Potential Maximum Losses (PML) with price extremes at first and then tests price reversal behavior using Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) and vector autoregressive (VAR) models. The study finds symmetry between PMG and PML in the banking, realty, and oil sectors, while the asymmetric reversal behavior is noted in the automobiles and capital goods industries. The presence of industry fundamentals in the models estimating the reversal behavior of share prices enhances their predictive power, which suggests the significance of value strategies in making gains from extreme price variations. The price reversal behavior is sector specific and found inconsistent in emerging market. Hence, the investors cannot overlook the relevance of the industry characteristics and earnings fundamentals while predicting the stock price behavior in emerging markets. [ABSTRACT FROM AUTHOR]

This study examined agenda-setting relationships across the media, politics, and the public, while accounting for contextual boundaries from 1978 to 2018 in the United States. Our findings indicate that (1) for the overall model, the media and the public influence the prominence of economic concerns on the political agenda, but the political agenda has no agenda-setting power; (2) under a Republican president, the prominence of economic topics in the media and political agendas influence each other, the media and the public agendas influence each other, and the public influences the political agenda; and (3) under a Democrat president, attention paid toward economic topics in the media and political agendas influence each other, as do the political and public agendas. Of particular note for agenda-setting theorists is our finding that the media significantly sets the agenda for the public under a Republican presidential condition and not under other conditions. Additionally, these relationships differ longitudinally; for example, the media responds to economic changes quicker and for more prolonged periods under a Republican than a Democrat. This study advances the agenda-setting theory by presenting novel findings about agenda-setters in action and by demonstrating the unique complexities of modeling longitudinal nonlinear relationships. [ABSTRACT FROM AUTHOR]

This paper estimates the macroeconomic effects of government spending shocks in New Zealand. Using a structural vector autoregression (SVAR) model, I find small output multipliers for government consumption but large multipliers for government investment. Importantly, the real exchange rate appreciates after positive government spending shocks, consistent with classic theory. Private consumption and private investment decrease after government consumption shocks, but increase after government investment shocks. I compare my results to the previous studies and show that selecting the appropriate series for government investment is important for estimating its effect. [ABSTRACT FROM AUTHOR]

In psychology, the use of portable technology and wearable devices to ease participant burden in data collection is on the rise. This creates increased interest in collecting real-time or near real-time data from individuals within their natural environments. As a result, vast amounts of observational time series data are generated. Often, motivation for collecting this data hinges on understanding within-person processes that underlie psychological phenomena. Motivated by the body of Dr. Peter Molenaar's life work calling for analytical approaches that consider potential heterogeneity and non-ergodicity, the focus of this paper is on using idiographic analyses to generate population inferences for within-person processes. Meta-analysis techniques using one-stage and two-stage random effects meta-analysis as implemented in single-case experimental designs are presented. The case for preferring a two-stage approach for meta-analysis of single-subject observational time series data is made and demonstrated using an empirical example. This provides a novel implementation of the methodology as prior implementations focus on applications to short time series with experimental designs. Inspired by Dr. Molenaar's work, we describe how an approach, two-stage random effects meta-analysis (2SRE-MA), aligns with recent calls to consider idiographic approaches when making population-level inferences regarding within-person processes. [ABSTRACT FROM AUTHOR]

Estimating the long-run variance (LRV) is crucial for several econometric issues. Constructing reliable heteroskedasticity autocorrelation consistent (HAC) variance-covariance matrices and implementing efficient generalized method of moments (GMM) estimation procedures require a consistent LRV estimate. A good VARHAC estimator (HAC matrix with the spectral density at frequency zero constructed using a VAR spectral estimation) requires accurately estimating the sum of autoregressive (AR) coefficients; however, a criterion that minimizes the innovation variance does not necessarily yield the best spectral estimate. This article implements an optimal VARHAC estimator using an alternative information criterion, considering the bias in the sum of the parameters for the AR estimator of the spectral density at frequency zero. [ABSTRACT FROM AUTHOR]

Recently, the introduction of Environmental, Social, and Governance (ESG) scores has become crucial for investment decisions and in minimizing portfolio risk. This study aims to understand the relationship between ESG scores and Value-at-Risk (VaR), computed by using a Vine copula–GARCH based approach, chosen for its reliability in detecting interdependencies among multiple stocks. In fact, one of the main challenges in estimating VaR for a stock portfolio is capturing the dependence structure among a large number of assets. The dataset consists of 16 companies listed on the FTSE100 index. The corresponding ESG scores were collected over a comprehensive period of five years, from 2018 to 2022, covering both normal and stressed market conditions. Additionally, a focused analysis was conducted for the period from 2020 to 2022 to isolate the specific effects of the COVID crisis. The results indicate that an increase in assets with the highest ESG scores reduces potential losses in the portfolio. This finding underscores the importance of integrating high-level ESG scores into portfolios to mitigate market risk. Additionally, during periods characterized by stressed market conditions, the impact of ESG scores on VaR is even more pronounced, demonstrating that sustainable assets are more resilient in times of crisis. [ABSTRACT FROM AUTHOR]

Investment risk forecasting is challenging when the stock market is characterized by non-linearity and extremes. Under these conditions, VaR estimation based on the assumption of distribution normality becomes less accurate. Combining extreme value theory (EVT) with machine learning (ML) produces a model that detects and learns heavy tail patterns in data distributions containing extreme values while being effective in non-linear systems. We aimed to develop an investment risk forecasting model in the capital market with non-linear and extreme characteristics using the VaR method of the EVT approach combined with ML (VaRGPD-ML(a)). The combination of methods used is a multivariate time series forecasting model with RNN, LSTM, and GRU algorithms to obtain ML-based returns. The EVT method of the POT approach was used to model extremes. The VaR method was used for investment risk estimation. The backtesting method was used to validate the model. Our results showed that determining the threshold based on the normal distribution will identify extreme values with the ideal number, minimum bias, and distribution of extreme data following GPD. The VaRGPD-ML(a) model was valid in all samples based on backtesting at a = 0.95 and a = 0.99. Generally, this model produces a greater estimated value of investment risk than the VaRGPD(a) model at the 95% confidence level. [ABSTRACT FROM AUTHOR]

Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high‐frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5‐min high‐frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short‐term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short‐term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out‐of‐sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model. [ABSTRACT FROM AUTHOR]

We introduce a novel tourism-specific business expectations sentiment index and explore whether it can operate as a leading indicator for international tourist arrivals in Greece. Using monthly data spanning 2002‐2021 and employing a VAR model, we document that this newly introduced tourism-specific business expectations index serves as a leading indicator, whose higher levels foreshadow increased demand for international travel. We also find that its inclusion in a tourism-oriented model increases forecasting accuracy, which can be utilized by travel agent businesses, local government officials, and policymakers in their efforts to predict tourist arrivals in Greece. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Increasing rearrangement is a rewarding instrument in financial risk management. In practice, risks must be managed from different perspectives. A common example is the portfolio risk, which often can be seen from at least two perspectives: market value and book value. Different perspectives with different distributions can be coupled by increasing rearrangement. One distribution is regarded as underlying, and the other distribution can be expressed as an increasing rearrangement of the underlying distribution. Then, the risk measure for the latter can be expressed in terms of the underlying distribution. Our first objective is to introduce increasing rearrangement for application in financial risk management and to apply increasing rearrangement to the class of distortion risk measures. We derive formulae to compute risk measures in terms of the underlying distribution. Afterwards, we apply our results to a series of special distortion risk measures, namely the value at risk, expected shortfall, range value at risk, conditional value at risk, and Wang's risk measure. Finally, we present the connection of increasing rearrangement with inverse transform sampling, Monte Carlo simulation, and cost-efficient strategies. Butterfly options serve as an illustrative example of the method. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Domestic and Foreign institutional investors are professional investors who aim to profit from price movements in the stock market. These investors not only profit from their investments but also drive the stock market of any country. The study examines the association between Sensex Nifty 50’s movements and investing patterns of FIIs, DIIS and the causal relationship between stock market returns and institutional investments (FIIS and DIIs). The study also analyses the impact of FIIs and DIIs in driving the Indian stock market. The study employed correlation analysis, vector autoregression model, Block exogeneity Wald-test and the Granger causality test for the objectives. Our results indicate the gradual fall in the FIIs-DIIs ratio in the Indian stock market, making domestic institutional investors a prominent counterforce to FIIs and establishing it as an equal driver. DIIs act as positive feedback traders, while FIIs act as neutral to negative traders. The causality test demonstrates that market return has a significant impact on both the players, but neither FIIs nor DIIs have a significant impact on market movement [ABSTRACT FROM AUTHOR]

This study analyzes the response of economic fundamentals to a misalignment shock of the real effective exchange rate in Korea. The estimation results of the equilibrium exchange rate determination model and time series model show that there is no significant difference in the direction of the deviation from equilibrium and that the won is significantly undervalued during the period before 1988, or during the currency and global financial crises. The cumulative impulse response analysis of the VAR model over the full period shows that an upward shock to the deviation from the equilibrium exchange rate reduces the GDP gap and inflation rate, while the effect on the call rate is not statistically significant. Furthermore, an upward misalignment shock initially worsens the goods and services balance, but the deficit in the goods and services balance shrinks significantly over time. In rolling regressions analysis, the entire sample is divided into two periods to estimate the impulse response function from the first period, and then the same procedure is repeated by moving the sample forward one by one. The cumulative impulse response results show that, as is the case for the full period, a positive exchange rate misalignment shock initially reduces the GDP gap, inflation, and worsens the goods and services balance, but the impact of this upward shock on these variables becomes increasingly weaker in the more recent sample. It also shows that the negative impact of upward shocks on the current account is smoothed out more recently during periods of undervaluation than during periods of overvaluation.

This study investigated the influence of digital payment systems on banks’ stability by exploring their effect on the Z-score of the Jordanian banking sector during the period from 2004 until 2022. It specifically focused on liquidity risks generated from e-payment transactions and how sufficient capital adequacy ratios enhance banking sector stability over both short-term and long-term periods by standing against sudden volatilities yielded from large amounts of transactions executed through digital payment systems. To achieve this objective, the study utilizes time series dual regression analyses of vector autoregression and vector error correction models on E-views 12 to cover the time variation influences of digital payment on the banking sector Z-score. The regression results indicate varied effects between the benefits and risks of digital payment systems on a bank’s Z-score that influence the immediate sector’s stability, indicating that while digital payment systems can initially hold liquidity risks, leading to short-term instability; the strategic implementation of robust capital adequacy ratio stands as a protective buffer by fostering long-term banking sector resilience. The results also suggest future predictions and insights for financial sector legislators and regulators emphasizing the need for monitoring strategies that stimulate continuous innovations in the digital payment infrastructure while constantly ensuring the stability and resilience of the banking sector. Thus, prudent liquidity management and the reinforcement of capital buffers are encouraged to pilot the dual challenges and opportunities that appeared at the stages of the digital payment process, ultimately guiding the sector toward continuous growth and sustainability. AcknowledgmentThe author is grateful to the Middle East University, Amman, Jordan for the financial support granted to cover the publication fee of this research.

As interest in Bitcoin as an investment asset continues to rise, this study investigates how social media influencers' statements impact Bitcoin price through sentiment and time series analysis. The research employs data from Telegram and compares it with X (formerly Twitter) to examine their effects on Bitcoin price and trading volume fluctuations using a Vector Autoregressive model. In the short-term, focusing on the five days following a shock, negative sentiment affects future price changes negatively for the first two days, while positive sentiment has a positive impact; the direction of the shock reverses after the third day. These findings align with the noise trader theory, supporting the interaction between noise traders and rational traders in the Bitcoin market. Further analysis using the NRC Word-Emotion Association Lexicon (EmoLex) to measure the occurrence of eight basic emotion words (joy, trust, fear, surprise, sadness, disgust, anger, anticipation) revealed that messages from Bitcoin channels on Telegram often convey strong trust and anticipation, suggesting a potential stimulation of noise traders' investment activities. In contrast, posts by Bitcoin influencers on X frequently employ emotionally charged words, likely contributing to the spread and amplification of sentiment among noise traders. These results suggest that messages from social media influencers function as a valuable information source for noise traders, potentially impacting their short-term behavior. This study contributes to understanding the dynamics of the Bitcoin market and offers insights into the intersection of social media and Bitcoin market movements, providing valuable information for researchers and investors. [ABSTRACT FROM AUTHOR]

The depletion of conventional energy sources has led to an increase in interest in renewable energy across the globe. The usage of renewable energy has lowered economic risk in the electricity markets. This study presents an approach to utilize solar photovoltaic as a renewable energy source, fuel cells as the energy storage system, and Flexible AC Transmission networks (FACTS) to reduce system risk in deregulated networks. The difference between real and expected renewable energy data is the primary cause of disequilibrium pricing (DP) in the renewable energy-integrated system. Integration of the FCs with a Unified Power Flow Controller (UPFC) can play an important role in coping with the disequilibrium pricing, emphasizing optimizing profitability and societal welfare in a deregulated environment. The paper also evaluates the system voltage outline and LBMP (location-based marginal pricing) scenarios, both with and without the integration of solar power. Two distinct factors, i.e., Bus Sensitivity Index (BSI) and Line Congestion Factor (LCF), have been proposed to identify the key buses and lines for solar power and Unified Power Flow Controller installation in the system. The study also employs conditional-value-at-risk (CVaR) and value-at-risk (VaR) to assess the system's risk. Using a real-time IEEE 39-bus New England system, multiple optimization algorithms including Sequential Quadratic Programming and the Slime Mould Algorithm (SMA) are employed to estimate the financial risk of the considered system. This analysis demonstrates that the risk coefficient values improve with the placement of UPFC and fuel cells in the renewable incorporated system. [ABSTRACT FROM AUTHOR]

The research discusses management strategies for addressing production risks in the polyculture cultivation of milkfish and vaname shrimp in Bulukumba Regency. Effective management is necessary to minimize these risks and optimize profits for business operators. Production risk management is carried out using Value at Risk (VaR) analysis, based on observations and meetings with business operators and fisheries services. This approach helps identify and address sources of production risk in the polyculture of milkfish and vaname shrimp. The findings show that both the probability and impact of risk sources are higher for vaname shrimp than for milkfish. Feed is the largest source of risk for milkfish, while disease poses the greatest risk for vaname shrimp. Conversely, feed is a minor risk factor for vaname shrimp, and disease is a smaller concern for milkfish. Risk mitigation strategies include: (1) carefully monitoring the dosage of saponin poison used to eradicate pests; (2) ensuring the correct dosage and application of urea fertilizer, TSP, and feed; (3) controlling water quality; and (4) selecting high-quality seeds. [ABSTRACT FROM AUTHOR]

The term "Dutch disease" is commonly used to refer to economic problems that arise in resource-rich countries, but there are other factors that can lead to the outcomes of Dutch disease. In particular, foreign capital inflows, by focusing on profitable sectors, can increase the competitive advantage in certain sectors of the economy, but can also bring about problems such as the appreciation of the local currency and changes in the balance of foreign trade. In clearer terms, foreign capital inflows can also lead to Dutch Disease. The Turkish economy experienced a period of high foreign capital inflows as a result of local policies and the global economic context after the 2001 economic crisis. This study investigates econometrically whether there is a hypothesis of Dutch Disease arising from foreign capital inflows in the Turkish economy. The analyses using the VAR method cover the period from 2003:1 to 2020:4. In this context, an econometric model that takes foreign trade channels into account has been created. The results of the foreign trade model show that foreign capital inflows resulted in a depreciation of the exchange rate, leading to a decrease in exports and an increase in imports, thus causing a contraction in tradable sectors. In conclusion, empirical findings regarding the validity of the Dutch Disease hypothesis for the relevant period have been obtained. [ABSTRACT FROM AUTHOR]

Based on daily data from 2013 to 2022, this study examines the spillover effects of volatility between cloud stocks and other asset classes (global stocks, treasury bonds, gold and crude oil) using the VAR connectedness approach. The results show that there is a significant spillover effect from global stocks and crude oil markets to the cloud stock market. The spillover effects become stronger whenever there are shocks such as economic crisis, turbulence in the international financial markets, COVID‐19 and global inflation. However, nearly 91% of the variations of cloud stocks come from within, suggesting that the diversification/hedging value of cloud stocks is potentially high. [ABSTRACT FROM AUTHOR]

Nowadays, online public opinions (OPOs) significantly impact corporate brand value (CBV). To prevent corporate brand crises caused mainly by OPOs, it is essential to detect anomalies in OPOs related to corporate reputation in a timely manner. This study explores how dramatic changes in OPOs affect market capital value (MCV), the primary indicator of CBV, and aims to construct a CBV early warning evaluation model. First, a set of OPO indicators dedicated to CBV are selected based on correlation analysis between various popular OPO and CBV indicators collected through a literature review. The method of Criteria Importance Through Intercriteria Correlation (CRITIC) is then employed to determine the indicator weights using data collected from popular social media platforms. Finally, the vector auto-regression (VAR) model is applied to validate the effectiveness of the proposed evaluation model. A case study involving several Chinese enterprises shows that abnormal changes in their MCVs consistently follow abnormal fluctuations observed in their OPOs, with a significant delay. This finding enables managers to promptly detect potential crises from the internet and take actions to avoid unexpected shocks. [ABSTRACT FROM AUTHOR]

Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading volumes in Thailand through the VAR(2) model. Trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures are considered over the sample period from 31 October 2022 to 12 January 2024. The empirical results provide no evidence that the trading volume of EUR/USD futures is dependent on the past trading volumes of USD/JPY futures and USD/THB futures. The Granger causality test results show the existence of bidirectional causality between the trading volumes of USD/JPY futures and USD/THB futures. The results of the impulse response function are consistent with the sign results of the VAR(2) model, showing that the USD/JPY futures trading volume has a negative impact on the USD/THB futures trading volume, and vice versa. The analysis of variance decomposition shows that the variability of the USD/JPY futures trading volume and USD/THB futures trading volume, apart from its own shock, is explained by other FX futures trading volume shocks. Therefore, traders should pay more attention to new FX futures trading activity due to its negative impact on the USD/THB futures trading volume and its contribution to the variance in the USD/THB futures trading volume. Understanding the futures trading volume relationship also helps Thailand Futures Exchange develop new products and services that can foster market liquidity and stability. [ABSTRACT FROM AUTHOR]

This paper explores the cointegration between agricultural commodity prices, crude oil prices and exchange rates. Vector autoregressions (VAR) or vector error correction models (VECMs) are applied to price data for French maize and milling wheat, U.S. corn prices and spot and future oil prices. The series are split between 2000-2007 and 2009–2019 omitting the "Great Recession" allowing for analysis of structural market changes. While oil prices influence futures pricing, the link is neither stable nor widespread. Modeling of time series in search of cointegration must consider structural market changes. Results from a "one-model-fits-all" approach are unlikely to be satisfactory. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between the international oil market and the West Texas Intermediate (WTI)—Western Canadian Select (WCS) price differential. Monthly data were collected for January 2005 to September 2023 from the U.S. Energy Information Administration, the Federal Reserve Bank of Dallas, the Canada Energy Regulator, and the Government of Alberta. The paper finds that a negative oil supply shock increases the differential with a delay. On the other hand, a positive oil-specific demand shock increases the WTI-WCS differential with a delay, while a positive aggregate demand shock increases the differential persistently. Overall, the three shocks in the international oil market account for 61% of the variability in the price differential in the long run. The aggregate demand shock is the most substantial factor in explaining the variation in the WTI-WCS differential in the long run. This indicates that the global oil market demand has an asymmetric effect on WTI and WCS prices. [ABSTRACT FROM AUTHOR]

To improve the dynamic assessment of risks of speculative assets, we apply a Markov switching MGARCH approach to portfolio risk forecasting. More specifically, we take advantage of the flexible Markov switching copula multivariate GARCH (MS‐C‐MGARCH) model of Fülle and Herwartz (2022). As an empirical illustration, we take the perspective of a risk‐averse agent and employ the suggested model for assessments of future risks of portfolios composed of a high‐yield equity index (S&P 500) and two safe‐haven investment instruments (i.e., Gold and US Treasury Bond Futures). We follow recent suggestions to employ the expected shortfall as a prime assessment of tail risks. To accurately evaluate the merits of the new model, we back‐test the risk forecasting for daily returns over 10 years for heterogeneous market environments including, for example, the COVID‐19 pandemic. We find that the MS‐C‐MGARCH model outperforms benchmark volatility models (MGARCH, C‐MGARCH) in predicting both value‐at‐risk and expected shortfall. The superiority of the MS‐C‐MGARCH model becomes stronger, when the share of comparably risky assets in the portfolio is relatively large. [ABSTRACT FROM AUTHOR]

In this study, we propose the application of the GARCH‐EVT‐Copula model in estimating liquidity‐adjusted value‐at‐risk (L‐VaR) of energy stocks while modeling nonlinear dependence between return and bid‐ask spread. Using the L‐VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non‐zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student‐t. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH‐EVT‐Clayton copula is superior and most consistent in forecasting L‐VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation. [ABSTRACT FROM AUTHOR]

Industrialization is a key element for economic growth in both developed and undeveloped countries. Due to globalization, the cause-effect between industrialization, economic growth and urbanization are changing. This study investigates the relationship between economic expansion, urbanization, and industrialization in Kenya using a quantitative methodology. Using World Bank economic indicator parameters, it focuses on understanding the complex interrelationships between these factors. Vector Autoregression (VAR) modeling is used to analyze the relationships between GDP, urbanization, and industrialization between 1990 and 2018. The Granger causality test is used to determine the causal linkages between GDP, urbanization, and industrialization. Results show that urbanization is increasing steadily across Kenya, with a high population in central, western, Rift valley, and coastal regions. The availability of agricultural land influences urbanization, with a significant positive relationship between the three parameters. The Granger causality test shows a unidirectional relationship between GDP, urbanization, and industrialization. A minimum of 20% is considered as the benchmark for urbanization to reduce both GDP and industrialization. Unplanned and rapid urbanization, accompanied by ecologically unfavorable industrialization schemes, threatens Kenya's biodiversity. The study provides insight into the interconnection between the three pillars of the economy and the statistical models developed are useful in forecasting future trends. [ABSTRACT FROM AUTHOR]

In the new era, coordinating the relationship between population flow and forestry ecological security has become an important challenge. In this study, we take Heilongjiang Province in China as an example, and through the combination of macro data and micro in-depth interviews, we explore whether population mobility really affects the intrinsic mechanism of forestry ecological security from the perspective of population exodus from forest areas. Based on the DPSIR model, we constructed a forestry ecological security evaluation index system, used the TOPSIS multi-objective decision analysis method to quantify the forestry ecological security status from 2000 to 2022, and utilized the impulse response function of the VAR model to explore the dynamic response relationship between population outflow and forestry ecological security. The results of this study show that, firstly, the comprehensive index of forestry ecological security level in Heilongjiang Province exhibits a fluctuating upward trend from 2000 to 2022. Second, forestry ecological security has a lagged effect on population outflow, and population outflow has almost no effect on forestry ecological security at present. Third, while the population outflow of Luobei County reduces the interference of human activities on the natural environment, it also brings about the pressures of insufficient forestry ecological resource management and forestry personnel. The Dongfanghong Forestry Bureau has effectively improved the efficiency of ecological construction and ecological security through the introduction of digitalized and intelligent equipment, which has effectively compensated for the negative impact of population outflow on the reduction in ecological management personnel. These findings will help realize the coordinated development of population, economy and society, and ecology. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of machine learning models, specifically gradient boosting machine (GBM) and random forest (RF), against the traditional vector autoregressive (VAR) model for forecasting economic growth in Algeria. By utilizing a dataset comprising key macroeconomic indicators—Gross Domestic Product (GDP), money supply (M), and inflation (I)—we aim to evaluate the predictive accuracy and robustness of these models. Our findings indicate that the RF model outperforms both GBM and VAR in terms of accuracy and reliability, providing a valuable understanding of the economic dynamics of Algeria. These results highlight the potential of advanced machine learning techniques in improving economic forecasting and informing policy decisions in emerging economies. [ABSTRACT FROM AUTHOR]

The carbon market was established to reduce carbon dioxide emissions. The traditional fossil energy market, new energy market, and carbon market have interrelated effects such as substitution, demand, and production inhibition, which can potentially lead to risk transmission. This study examines the nonlinear volatility correlation between China's carbon market, China's new energy market, and the international crude oil futures market. Seven submarkets within these three markets are selected for analysis. By measuring volatility risk through the conditional heteroscedasticity of returns, the analysis of nonlinear Granger causality networks reveals that, from a nonlinear perspective, risk primarily spills over through the paths of "International crude oil futures market → China's carbon market" and "International crude oil futures market → China's new energy market → China's carbon market." China's carbon market serves as a recipient of risk, with minimal spillover effects. Therefore, further optimization is needed for the framework of China's carbon market to enhance its asset allocation function and promote its spillover influence. Investors in China's carbon market should consider both linear and nonlinear risks from China's new energy market and the international crude oil futures market, and take appropriate measures to facilitate the sustainable growth of Chinese enterprises. [ABSTRACT FROM AUTHOR]

This study investigates the VAR time series data of the overall expenditures and income in the Kurdistan Region of Iraq. It applies multivariate wavelet shrinkage within the VAR model, comparing it to traditional methods to identify the most appropriate model. The chosen model will then be used to predict general expenditures and revenues for the years 2022-2026. The analysis involved assessing the stationarity of the expenditure and revenue time series, which are interrelated variables during the interval 1997-2021, and identifying the overall trend through differencing to achieve stationarity. The proposed method incorporated multivariate wavelet shrinkage in the VAR model to address data contamination in expenditures and revenue using various wavelets like Coiflets, Daubechies, Symlets, and Fejér-Korovkin at different orders. Threshold levels were estimated using the SURE method and soft thresholding rules to denoise the data for the following analysis within the VAR model. Model selection was based on Akaike and Bayes information criteria. The analysis, conducted using MATLAB, indicated the superiority of the proposed method over traditional methods, forecasting a continued rise in expenditures and revenues for the Iraqi Kurdistan region from 2022 to 2026. The findings suggest that advanced techniques can offer more accurate economic forecasts, benefiting regional planning and policy-making. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate determinants of banks profitability in CBE by using time series data of CBE from year2003 to 2015. Design/methodology/approach: The research has econometric research design. The study used quantitative research approach and secondary financial data are analyzed by using Vetcor Auto Regressive Model (VAR), for the bank profitability was measureed by Return on Asset (ROA). OLS method was applied to investigate the impact of bank size, managerial efficiency, liquidity, credit risk, real GDP growth rate, and annual inflation rate. Findings: The empirical results shows that bank specific factors; bank size, managerial efficiency, credit risk and macro-economic factors; level of GDP and annual inflation rate have a strong influence on the profitability of banks. The empirical study shows that, all explanatory variables in this study have negative effect on the profitability measure except GDP and inflation rate. In addition to this, without liquidity all explanatory variables are statistically significant. Practical implications: Management bodies of CBE should strive to strengthen the bank specific factors like capital position, managerial efficiency, credit risk, asset quality, and bank size. Because, The competition become tough since increase in new entrant to the market, banks are increasingly being substituted by the general public as a source of funds by new share companies being established in a variety of sectors, and the micro-finance industry continues to show rapid growth. Originality/value: The banking industry is crucial source of financing different business segments that is operated in a given country. [ABSTRACT FROM AUTHOR]

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility. [ABSTRACT FROM AUTHOR]

This study examines the nexus between trade, financial development, remittances and economic growth in Zimbabwe using time-series data collected from the World Bank Worldwide Development Indicators database from 1980 to 2022. Vector autoregression models were estimated using Stata/SE 14.2 to analyse “short-run” relationships among the four variables. The results obtained showed the non-existence of cointegration equations in the series. The significant findings are the positive “short-run” causal effects of first lag GDP regressor on GDP at 0.05%, first lag remittances regressor on GDP, first lag financial development regressor on financial development, and first lag trade and remittances regressors on remittances, at 0.01% level of significance. Moreover, the Granger causality test showed that the remittances Granger causes GDP and trade Granger causes remittances in the “short-run”, while the opposite is true for other variables. This study is essential to researchers and policymakers. It is recommended that the government formulate policies that promote the inflow of remittances, trade, and financial development for meaningful economic growth. [ABSTRACT FROM AUTHOR]

A procedure is outlined aiming at testing the bias due to omitted variables in vector autoregressions. The procedure consists first of filtering a vector of omitted variables and then testing the bias. The test does not rely on the availability of the omitted variables, and is based on a comparison between maximum-likelihood with Kalman filter vector autoregression and linear vector autoregression estimates. The empirical part considers two illustrative examples: a univariate regression analysis, based on the rational expectation-augmented Phillips curve; and a VAR with output, inflation and interest rates where a "price puzzle" arises. [ABSTRACT FROM AUTHOR]

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We estimate the short run effects of Brexit border disruption on the UK economy. We estimate a structural VAR for the UK, where Brexit effects are identified by the dates of Brexit events, the referendum, and the exit from the single market. We find evidence of short run effects of Brexit: temporary effects on GDP, exports and imports (slightly negative), and on inflation and interest rates (slightly positive). These effects are consistent with modest disruption from introducing a border with the EU--a border due to be made barrier-free and seamless by the UK-EU Trade and Cooperation Agreement. Previous work using other countries as comparators is vulnerable to identification difficulties. We also survey earlier modeling work on the long run effects of evolving policies of free trade, UK-sourced regulation, and liberalized immigration. Models of long run trade suggest the emergence of substantial gains. [ABSTRACT FROM AUTHOR]

The article extends the empirical literature on overconfidence bias in G7 stock markets during pre- and post-COVID-19 and provides additional evidence. Using vector autoregression and impulse response functions (IRFs), we analyze the overconfidence bias for the daily data from January 2015 to December 2021. Because the pertinent coefficients are positive and highly significant for only a few lags, there is a strong contemporaneity between market volume and market return in the pre-COVID-19 period of the Canadian and Italian stock markets. The study shows compelling evidence of overconfident behavior in the Italian market during the COVID-19 crisis. Along with trading volume, market liquidity influences overconfidence bias, which tracks market return but not vice versa. For investors, decision-makers, and market regulators, the study has significant ramifications in the current market turbulence caused by the COVID-19 pandemic. Furthermore, overconfidence contributes to the reported extra unpredictability due to the high level of sensitive data.

Purpose This paper aims to deepen understanding and knowledge regarding the impact of foreign direct investment (FDI) on the industrialization process. Many developing countries aspire to shift from agriculture-centric economies to achieve sustainable development through industrialization. Realizing this goal, however, has been challenging, prompting an examination of the sixty-year trends and effects of FDI on Tanzania’s industrialization trajectory.Methodology This study employs a comprehensive approach utilizing time series models, specifically the Vector Autoregressive (VAR) model and the Error Correction Model (ECM), to analyze the dynamic influence of FDI on industrialization. By forecasting the five-year trajectory of industrial growth and FDI inflows using data from the Bank of Tanzania and the National Bureau of Statistics spanning 1960 to 2020, this methodological framework aims to provide a nuanced understanding of the FDI-industrialization relationship, contributing valuable insights to the economic development discourse.Findings The study’s results highlight the significant role of FDI in shaping both short- and long-term industrial progress, which is critical to advancing Tanzania’s industrialization goals. Conversely, factors like exchange rates predominantly impact the short-term industrial landscape. Forecasts from the analysis indicate a projected decline in both FDI and industrialization from 2020 to 2022, followed by a notable upturn from 2022 to 2025. This underscores FDI as a key driver for integrating agriculture-based economies into global value chains, facilitating economic upgrading through capital accumulation—a fundamental catalyst for sustained industrialization.