Problem Of Heteroscedasticity Research Articles

Does Intellectual Capital Influence Firm's Financial Performance in Bangladesh?

This paper aimed to find the effects of intellectual capital and its components on firms’ financial performance in Bangladesh. A sample of 100 firms comprised of 48 manufacturing, 21 services, and 31 banking companies was studied for five years from 2017 to 2021, setting 500 firm-year observations. Data was collected purposively from secondary sources, such as annual reports of sampled companies. The robust fixed-effect regression model using STATA 14.2 software was applied to test the hypothesis due to autocorrelation and heteroscedasticity problems. The regression results documented that overall IC, human capital efficiency (HCE), and capital employed efficiency (CEE) positively and significantly enhanced the ROA and ROE of the firms. However, structural capital efficiency (SCE) and relational capital efficiency (RCE) negatively and insignificantly influenced the same. The study contributes to resource-based theory using econometric methods to extend the samples of both financial and non-financial companies. This study helps investors, managers, policy-makers, governments, and accounting regulatory bodies regarding the utilization pattern of invisible and tangible resources in Bangladeshi firms.

PERBANDINGAN METODE EXPONENTIAL GARCH (EGARCH) DAN GLOSTEN-JAGANNATHAN-RUNKLE GARCH (GJR-GARCH) PADA MODEL VOLATILITAS SAHAM TUNGGAL

Financial data, such as stock prices, usually have a tendency to fluctuate rapidly and create a heteroscedastic effect on the variance of residuals. The Covid-19 pandemic that occurred from 2020 to 2022 is one factor that can affect economic movements, especially in Indonesia, and has an impact on the volatility of financial data. The problem of heteroscedasticity can be addressed using the ARCH/GARCH model. However, this model has a weakness in capturing the asymmetry of volatility resulting from good news and bad news. Several models that can overcome the problem of volatility asymmetry are EGARCH and GJR-GARCH. The purpose of this thesis research is to determine the best volatility model using the daily stock price data of PT Bank Rakyat Indonesia (Persero) Tbk. covering the period from February 2020 to February 2023. The result of the asymmetric GARCH model suggests that the ARIMA(2,0,2) EGARCH(1,1) model has an AIC value of -4.8850, and the ARIMA(2,0,2) GJR-GARCH(1,1) model has an AIC value of -4.8907. Therefore, the model with the minimum AIC value, which is the ARIMA(2,0,2) GJR-GARCH(1,1) model, is considered the best model. Furthermore, this model exhibits very good forecast accuracy, as evaluated by the sMAPE value of 5,17%.

PENENTUAN VALUE AT RISK (VAR) PADA PORTOFOLIO BIVARIAT DENGAN PENDEKATAN COPULA GUMBEL

One way to minimize risk in stock investment is stock portfolio. Value at Risk (VaR) is a calculation method that can be used to estimate the risk of a stock portfolio. VaR can be measured by parametric and non-parametric approaches. Calculation of VaR with Monte Carlo simulation assumes the data is normally distributed. Stock return data generally has high volatility so that the residual variance of the model is not constant (heteroscedasticity) and not normally distributed. The ARIMA-GARCH model can be used to solve heteroscedasticity problems. Copula is a tool used to model the combined distribution of residuals from the ARIMA-GARCH model which does not require normality assumptions. Gumbel's copula is copula that has the best sensitivity to high risk. This study uses stock data of PT Bukit Asam Tbk (PTBA) and PT Chandra Asri Petrochemical Tbk (TPIA) for the period April 1 2020 – December 1 2022. The initial step of this research is model stock returns using the ARIMA-GARCH method and then calculate portfolio VaR using the Gumbel’s copula. The results showed that the best model for PTBA is ARIMA(2,0,2) GARCH(1,1) and for TPIA is ARIMA(1,0,0) GARCH (1,1). At the 95% confidence level, the portfolio risk is 2,41%.

The use of the (Tobit) Model in Studying the Variables affecting the Increase in the Number of People with Systolic Hypertension, in the Presence of the problem of Heterogeneity of Random Error Variance

The process of regression analysis is based on a number of basic assumptions, and if one of these assumptions is not available, this will lead to obtaining inaccurate results, and the most of these assumptions is homoscedasticity, violating this assumption causes a problem of heteroscedasticity of the random error variance the, which may come from different variances This cause to misleading and inaccurate decisions. Recently Econometric models received attention, especially limited regression models, which contain specific response variables and several repeated observations within a certain range, including Tobit models and also considered imitations of the Censored Regression Model. Problem arise when it appears in the data heteroscedasticity, which makes methods of estimating the parameters of the linear regression model giving incorrect and inaccurate results, hence we get Biased parameters that do not have minimalist variance, as well as getting an unreliable p-value. The objective of this research is to study the estimation of the multiple standard (TOBIT) model which features that the variable (Y) specific variant. In heteroscedasticity, which both likelihood function method weighted and Genetic Algorithm was used in simulation. To sum up, the results showed, that Genetic algorithm better outcomes than weighted likelihood function method therefore, it was used in the practical application.

IMPACT OF CLIMATE CHANGE ON RICE PRODUCTIVITY IN KHYBAR PAKHTUNKHWA, PAKISTAN

Climate change is currently the most significant concern on the planet. This study examined the impacts of climate change on rice yield across agro-ecological zones of Khybar Pakhtunkhwa, Pakistan. This study focused only on four districts across four agroecological zones in Khybar Pakhtunkhwa based on the availability of data on areas under rice, rice production, and meteorological variables. Districts Swat from Zone A, Mansehra from Zone B, Mardan from Zone C, and D.I. Khan from Zone D were selected. This study covered 33 years (1986-2018) of data across these four districts. Panel unit root tests of LLC and IPS suggested that all variables included in the model were stationary. Durbin Wu Hausman's test favored the fixed effect model compared to the random effect model. Diagnosis of fixed effect model results showed that it was plagued with problems of cross-sectional dependency, heteroscedasticity, and autocorrelation. Final findings were estimated using the Feasible Generalized Least Squares (FGLS) model to overcome the problems in the fixed effect model. Results revealed that area under rice crop has a positive and significant effect on rice yield. The estimated critical temperature for the maximum yield of rice was 34.48 ºC in the sowing stages, 35.85 ºC in the vegetative stages, and 29.43 ºC in the maturity stages, and Rainfall in the vegetative stages was 152 mm. It is concluded that rice yield showed a decline when temperature and rainfall exceeded the critical levels. The government needs to use afforestation and other appropriate measures to keep the temperature from rising in these three zones (zones A, C and D). The extension department needs to inform rice growers about adaptation strategies to climate change.

ANALISIS HUBUNGAN HARGA EMAS DAN PASAR SAHAM MENGGUNAKAN MIXED-COPULAS

Gold is considered as a reliable investment tool for long-term savings and/or investment portfolios. Investors who do not like high risks trust gold to be a safe haven commodity that can mitigate the impact of any financial crisis. Gold and stocks are often used as substitutes for each other, where the two have an inverse relationship. Copula is used to capture the dependence relationship between world gold prices and the stock indexes. The data used are the stock index data for the JKSE Indonesia), PSE (Phillipines), Nikkei 225 (Japan), HSI (Hong Kong), and world gold prices (XAU) from January 1, 2014 to December 31, 2019. From the data, the ARMA-GARCH model is made to solve the problem of autocorrelation and heteroscedasticity. Then, the correlation between assets is calculated using the rank correlation. Furthermore, four pairs of data are made from each stock index with the price of gold. Next, the best copula and the estimated Value-at-Risk (VaR) are sought for each portfolio. From the results of the selecting of the best copula for each pair of data, it is found that gold can be a safe haven asset in the Hong Kong's stock market. The VaR results show that the biggest loss is in the Japanese market.

Indonesia Gold Price Forecasting Using ARIMA Model (0,1,1) - GARCH (1,0)

A frequently employed time series model is the Autoregressive Integrated Moving Average (ARIMA) model. In highly volatile data, ARIMA models sometimes produce residual variances that are heteroscedasticity. One method that can overcome the problem of residual variance heteroscedasticity is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. The purpose of this study is to obtain the ARIMA-GARCH model for daily gold price data in Indonesia for the period 1 January 2022 to 31 December 2022, and to obtain daily gold price forecasting results in Indonesia. The daily gold price forecasting model obtained for Indonesia is ARIMA (0,1,1) - GARCH (1,0) with a MAPE value of 0.5745% which shows that the model is very good because the MAPE value is less than 10%. The results of Indonesia's daily gold price forecast from January 1st, 2023 to January 3rd, 2023 remain stable.

Does Fiscal Decentralization Reduce Poverty? An insight from provincial level analysis in Pakistan

The present study has been designed to check the effect of fiscal decentralization (FD) on the poverty levels in Pakistan. Three measures of FD, namely intergovernmental transfers, provincial government revenues, and expenditure, are used in this study. Furthermore, the effect of FD is checked on the poverty levels separately in four provinces of Pakistan. Poverty levels are measured with the help of the Multidimensional Poverty Index (MPI) and the percentage of poor households with a value under 35 according to the International Wealth Index (IWI). This study has used panel data from 1990 to 2020 for the model in which poverty is measured through IWI value. Data from 2004 to 2020 has been used for the model in which poverty is measured through MPI. Feasible Generalized Least Square (FGLS) has been used to obtain the regression results by avoiding the problem of serial correlation and heteroscedasticity. The study's results highlighted that two measures, namely intergovernmental transfers and provincial government expenditures, have a nonlinear relationship with poverty. An increase in intergovernmental transfers and provincial government expenditures reduces poverty at a decreasing rate at first. Then, an increase in both variables results in the form of a higher level of poverty. Moreover, revenue decentralization at the provincial level increases poverty initially and reduces poverty after a critical point.

Macroeconomics Determinants of Fiscal Sustainability in the Asian Countries

The present study is designed to investigate how macroeconomic determinants of the South Asian region have affected the fiscal sustainability from 1980 to 2021. This study has used Newey and West (1987) robust standard errors approach to overcome the problem of autocorrelation and heteroscedasticity in panel data. The results of the statistical model confirmed the existence of fiscal sustainability in selective Asian countries. The significant contribution of the study is to obtain robust standard errors that rectify the issues of violation of simple regression analysis, which is the solution to all problems relevant to OLS regression. Furthermore, the results also confirmed that using macroeconomic determinants significantly improves fiscal sustainability. The results confirmed that macroeconomic factors contribute to fiscal sustainability. The study concluded that South Asian countries should use macro indicators as sources to achieve a higher fiscal sustainability rate.

Estimation Parameters in the fuzzy class Poisson mixture regression model

In the Poisson mixture regression for the fuzzy class model, observations originate from distinct sub-sources or classes due to the problem of heteroscedasticity. The underlying assumption posits that the observed data stem from a finite mixture of fuzzy classes. The primary challenge lies in achieving the optimal assignment of observations to their respective categories, necessitating the utilization of sophisticated methods for parameter estimation within the model. This research paper explores the estimation of parameters in a Poisson mixture regression model designed for the fuzzy class. Leveraging the FCML (Fuzzy Classification Maximum Likelihood algorithm) and genetic algorithm methodologies, we conducted simulations to assess the accuracy and efficacy of parameter estimation. Our findings reveal a clear superiority of the genetic algorithm over the FCML algorithm, as evidenced by the Mean Square Error (MSE) criterion. The genetic algorithm consistently produced lower MSE values, indicating more precise and reliable parameter estimates compared to the FCML algorithm. This research underscores the potential of the genetic algorithm as a robust and effective tool for parameter estimation in complex statistical models, offering researchers an alternative approach for tackling challenges in the estimation of parameters within the context of the fuzzy class

MCDONALD'S APPLICATION ANALYSIS USING TECHNOLOGY ACCEPTANCE MODEL (TAM)

McDonald's application is an application developed and published by McDonald's Apps. This application aims to provide ordering services to consumers through online without having to come to the outlet. This study aims to explain and analyze user acceptance of McDonald's applications. In this study, the acceptance theory used is the modification and development of the Technology Acceptance Model theory which focuses on five factors, namely usability perception factors, ease perception, usage attitude, usage interest and actual use. The method in this study is quantitative with data analysis techniques using descriptive analysis techniques and using multiple linear regression analysis with the help of IBM SPSS Version 25 software. The sample taken amounted to 100 respondents with characteristics who used or who had used the McDonald's application at least once. The sampling technique uses the Non-probability Sampling method with the Purposive Sampling technique. The results obtained from the analysis using IBM SPSS are (1) usability perception positively affects usage attitude, (2) convenience perception positively affects usage attitude, (3) usability perception positively affects usage interest, (4) usage attitude positively affects usage interest, (5) usability perception positively affects actual use and (6) use interest positively affects actual use. In conclusion, all hypotheses that have been formulated are accepted. The residual data of the variable are independent of the normally distributed dependent variable and the regression model satisfies the normality assumption. Independent and dependent variables do not occur heteroscedasticity problems. Independent and dependent variables do not occur multicollinearity. Independent and dependent variables have no autocorrelation.

Financial Development of the Turkish Pharmaceutical Sector During and After the Pandemic: Sector Panel Data Analysis for the Period 2018-2022

This study examines the effects of the COVID-19 pandemic on the financial performance of the Turkish pharmaceutical industry in the period between 2018 and 2022. Financial indicators in the sector were examined and analyzed through the data set covering the periods before, during and after the pandemic. The hypotheses on which the research focuses predict the impact of the pandemic process on the pharmaceutical industry. The ADF unit root tests focused on four different financial indicators in the sector and it was determined that these indicators were stationary at normal levels. Hausman test results showed that a fixed-effect model is the most suitable option for the panel data model. However, upon detecting autocorrelation and heteroscedasticity problems in the model, the Panel GLS model was applied. Structural break analysis revealed that there were unexpected changes in the periods determined as the second quarter of 2020 and the first quarter of 2022. These periods mark time periods when the effects of the COVID-19 pandemic are particularly evident. Additionally, new recorded case data of COVID-19 shows how the pandemic's impact on the industry has changed over time. It was observed that pandemic conditions became evident in April and May 2020, and February 2022 marked the highest number of cases. This study aims to evaluate the future potential of the sector by examining in detail the changes in the financial performance of the Turkish pharmaceutical industry before, during and after the pandemic.

Determinants of high-tech export: evidence from a cross-country analysis

Relevance. The adoption of new technologies and the rapid emergence of innovation spur high-tech production and export-led economic growth. We aim to provide fresh evidence on the determinants of high-tech exports, considering different macroeconomic factors within the framework of the gravity model. Research Objective. The aim of the research is to empirically assess the impact of macroeconomic instability, tax policies, natural resources endowment, human capital, and institutional environment on the promotion of high-tech exports. Data and Methods. In considering the institutional indicator, six distinct indices from the World Bank are examined, and a common indicator is computed using principal component analysis. The econometric modeling uses a panel dataset covering the world's 80 largest economies from 1996 to 2019. To test the assumptions of the gravity model and tackle the heteroscedasticity problem, the Poisson Pseudo Maximum Likelihood methodology is employed. Results. Higher inflation and unemployment rates are found to significantly decrease high-tech exports, while government external debt contributes to their enhancement. Tight tax policy and an increase in tax contribution are counterproductive in spurring high-tech exports. A negative and significant result is found for resource endowment, indicating that an increase in resource exports is counterproductive for technological advances and high-tech production. In most cases, the institutional environment and human capital significantly promote high-tech exports. Conclusions. Based on the presented empirical findings, we offer recommendations for the government to stimulate high-tech exports.

Effect of exchange rate fluctuation on external reserve in Nigeria

This e study examines the effect of Exchange rate fluctuation on the external reserve of Nigeria using time series data from 2001-2021. The data used are secondary which were obtained from the Central Bank of Nigeria Statistical Bulletin and the data gathered were analysed using e-view12. The study employed Ordinary Least Squares (OLS) to analyze the data. The study was given that the reported p-value of 0.9319 was greater than the critical level of significance at 0.05. This indicates that the model is free from the problem of heteroscedasticity. The results show that there is a negative relationship between external reserves and inflation rate, interest rate, and public debt. Findings however revealed that all independent variables possess negative relationships with external reserves, the relationship is significant in relation to external reserves except public debt which is insignificant. The research therefore recommends that strict foreign exchange control policies should be put in place to help in the proper determination of the value of the exchange rate. This will thus; in the long run, help to strengthen the value of the Naira and the government's need to induce the foreign exchange rate by encouraging positive economic reforms that will reduce the unfavorable effect of exchange rate fluctuation on external reserve.

THE EFFECT OF CEO’S ATTRIBUTES AND ZAKAT ON THE PERFORMANCE OF ISLAMIC BANKS

This paper aims to examine the impact of CEO attributes and Zakat on the performance of Islamic banks in the MENA region and Malaysia. In this paper, we test the effect of CEO tenure, foreign CEO, CEO duality, CEO education level and Zakat on the Islamic bank performance measured by ROA. The sample comprises 126 firm-year observations of 42 Islamic banks from MENA countries and Malaysia that registered over the period of 2017-2019. To overcome the problems of heteroscedasticity and autocorrelation, we apply the generalized least square (GLS) panel regression. The findings of the regression show the great impact of CEO attributes (Tenure, nationality, duality, education level) and Zakat on Islamic banks performance. We control the robustness of our main results by testing the impact of our independent and control variables on bank performance measured by ROE. The results of this test confirm in general our main results. To our knowledge, this paper is the first to provide empirical evidence regarding the effect of CEO attributes (CEO nationality, CEO tenure, CEO duality and CEO education level) and Zakat all together on the performance of Islamic banks. To our knowledge, the analysis of the effect of CEO nationality on Islamic banks performance is also new.

How Does the Inflow of Tourism Affect the Agricultural Exports of India?

The empirical relationship between tourism inflow and international trade has been explored during recent years, supporting the argument that international tourism inflow promotes international trade between countries. However, the impact of international tourism inflow on agricultural exports has been neglected within standard agricultural trade models such as agricultural exports function and gravity model. The main aim of the article is to provide theoretical and empirical evidence that international tourism inflow matters for agricultural trade. The study uses an agricultural export demand function and augmented gravity model to examine the impact of tourism inflow on agricultural exports of India from the top 10 importing countries for the period 2000–2019. The agricultural trade model is estimated using random effect and fixed-effect model. To overcome the problem of panel heteroscedasticity and autocorrelation, it uses the panel corrected standard error model. Further, potential endogeneity is treated by using a 2SLS model. The empirical evidence confirms the significant and positive impact of tourism inflow on agricultural exports of India. JEL Codes: F1, Q17, Z30

MODEL VOLATILITAS SAHAM LQ45 DENGAN PENDEKATAN MARKOV-SWITCHING GARCH

Financial markets have an important role in the economy of a country including Indonesia. One of the activities chosen by investors in the financial market is investing. In the world of investment, especially in stocks, there is a phenomenon of volatility, which is a situation where a stock price value increases and decreases. Volatility in this financial market is something that is very interesting for investors because of its impact on the existence of global financial markets. The purpose of this study is to model the LQ45 index data using a model that can overcome the problem of heteroscedasticity and changes in data structure. The commonly used model for heteroscedasticity problem is ARCH/GARCH. Furthermore, a model that can account for structural changes is the Markov Switching model. The model that can overcome the problem of heteroscedasticity as well as structural changes is the MS GARCH model. The financial data used in this study are daily data for the LQ45 Index from 10 June 2019 to 28 May 2020. Based on the results of data analysis conducted using the MS GARCH model is the best model in modelling the volatility of the LQ45 index. The best model selection uses the criteria for the AIC and BIC values with the smallest value

PEMODELAN ARIMA-GARCH UNTUK VOLATILIAS DAN VALUE AT RISK PADA SAHAM PT. GUDANG GARAM TBK

Investment is one of the development factors in economic activity, there are two basic things that investor must know before making investment decisions, namely: returns and risk. One of the statistical methods to calculate the maximum loss in investment is Value at Risk (VaR). this study aims to calculate VaR on the closing stock price data of Pt. Gudang Garam TBK for the daily period starting from 4 January 2021 to 30 December 2021. Log return data is model by ARIMA. The ARIMA model contains a heteroscedasticity effect so it is inadequate for modelling, one of the models that can overcome the heteroscedasticity problem is the ARCH-GARCH model. Forecasting the volatility of the data is done using the ARCH-GARCH model. The results show that the GARCH (1,1) is the best model for predicting volatility. Volatility is predicted for the next 30 days, after the volatility forecasting results are obtained, the VaR calculation can be continued. Based on the results, it shows that volatility increases over time, which means that the risk that investor will accept will be higher and the returns will also be greater

Relationship Between Economic Growth and Environmental Degradation in Peru, Period 1990-2019

Objective: The objective of this research is to analyze the relationship between economic growth and environmental degradation through a regression model for the Peruvian case, during the period 1990-2019, based on Georgescu Roegen's Bioeconomy; who explains that, in environmental terms, economic growth seems to be more of a problem than a solution. Method: The quantitative approach was considered, of a non-experimental type, with a descriptive and correlational design; where secondary source information from the World Bank database was used. Results: According to the results obtained, it is concluded that the variable with the most relationship or that best explains economic growth is energy consumption, followed by CO2 emissions; indicators of environmental degradation explain 24% of economic growth, being a model without problems of heteroscedasticity or autocorrelation; the level of individual significance in all cases is less than 5% and according to the proposed model it is necessary to; if CO2 emissions increase, PBI per capita would increase by 19% and if energy consumption increases, PIB per capita would increase by 20%. Conclusions: Finally, it was possible to contrast the hypothesis that there is a direct and significant relationship between economic growth and environmental degradation.

Factors Affecting the Risk Non-Performing Sharia Commercial Bank Financing

This study aims to examine and analyze the influence of internal and external factors on the risk of problem financing in Islamic commercial banks in Indonesia. This research uses a quantitative type. Data comes from the Financial Services Authority (OJK) and Bank Indonesia for the 2015-2022. The data were first tested using the classical assumption test, which included the normality test, multicollinearity test, and autocorrelation test and then analyzed using multiple linear regression analysis. The Robust Least Squares method is used in multiple linear regression analysis to overcome the problem of heteroscedasticity. The results show that the internal factors that are taken into account are the efficiency ratio (BOPO), capital adequacy ratio (CAR) and financing-to-savings ratio (FDR). At the same time, the external factor that is taken into account is the inflation rate. Based on the results of testing the hypothesis using the z test, it is concluded that only CAR and FDR significantly contribute to problem financing. In contrast, efficiency and inflation do not significantly affect problem financing in Islamic Commercial Banks in Indonesia.