By using the DEA method, the paper measures market efficiency of the banks in Bosnia and Herzegovina in the period 2017-2020, in the context of challenges caused by the COVID-19. The aims of the research are: a) to measure market efficiency of the banks in Bosnia and Herzegovina and rank them using the DEA method (applying three models CCR-O, BCC-O, and Window-I-C), b) establish the effect of the COVID-19 pandemic on market efficiency of the observed banks, and c) established the link between the volume of digital banking services usage and market efficiency of the observed banks. The research results show that in 2020, when the COVID-19 appeared, CCR-O and BCC-O models revealed a decrease in market efficiency for 73.9% of the observed banks while Window-I-C model revealed lower market efficiency for all the observed banks. The regression analysis applied showed a significant link between the volume of digital banking services usage market efficiency of the observed banks. The regression model was established, pointing to a significant importance of independent variables for the prediction of the dependent variable.
{"title":"APPLICATION OF DEA METHOD IN MEASURING OF MARKET EFFICIENCY OF BANKS IN BOSNIA AND HERZEGOVINA AND REFLECTION OF THE COVID-19","authors":"Beriz Čivić","doi":"10.33818/ier.844228","DOIUrl":"https://doi.org/10.33818/ier.844228","url":null,"abstract":"By using the DEA method, the paper measures market efficiency of the banks in Bosnia and Herzegovina in the period 2017-2020, in the context of challenges caused by the COVID-19. The aims of the research are: a) to measure market efficiency of the banks in Bosnia and Herzegovina and rank them using the DEA method (applying three models CCR-O, BCC-O, and Window-I-C), b) establish the effect of the COVID-19 pandemic on market efficiency of the observed banks, and c) established the link between the volume of digital banking services usage and market efficiency of the observed banks. The research results show that in 2020, when the COVID-19 appeared, CCR-O and BCC-O models revealed a decrease in market efficiency for 73.9% of the observed banks while Window-I-C model revealed lower market efficiency for all the observed banks. The regression analysis applied showed a significant link between the volume of digital banking services usage market efficiency of the observed banks. The regression model was established, pointing to a significant importance of independent variables for the prediction of the dependent variable.","PeriodicalId":32692,"journal":{"name":"International Econometric Review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49038908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper a new consumption function is derived based on savings motive hypothesis (SMH). The major theory behind the SMH is that households save part of their income in period 1 and transfer it to period 2. Implying that consumption in period 1 is the addition of autonomous consumption and variable consumption in period 2. The validity of the SMH is tested by using data from India, Kenya, South Africa, Saudi Arabia, UK and USA for the period 1970 to 2018. The data analyses are performed by using World Bank Data and generalized least squares (GLS) method. The paper demonstrates that estimation of the consumption function could be done more accurately by using SMH of the consumption function. The MSH is based on the psychological savings motive theory. Some results in the paper can be used in making both household and national welfare decisions e.g. making use of the short run global marginal propensity to consume that is found to be 0.43.
{"title":"Estimation of Consumption Functions Using Savings Motive Hypothesis (SMH)","authors":"Jimmy Alani","doi":"10.33818/ier.1023428","DOIUrl":"https://doi.org/10.33818/ier.1023428","url":null,"abstract":"In this paper a new consumption function is derived based on savings motive hypothesis (SMH). The major theory behind the SMH is that households save part of their income in period 1 and transfer it to period 2. Implying that consumption in period 1 is the addition of autonomous consumption and variable consumption in period 2. The validity of the SMH is tested by using data from India, Kenya, South Africa, Saudi Arabia, UK and USA for the period 1970 to 2018. The data analyses are performed by using World Bank Data and generalized least squares (GLS) method. The paper demonstrates that estimation of the consumption function could be done more accurately by using SMH of the consumption function. The MSH is based on the psychological savings motive theory. Some results in the paper can be used in making both household and national welfare decisions e.g. making use of the short run global marginal propensity to consume that is found to be 0.43.","PeriodicalId":32692,"journal":{"name":"International Econometric Review","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43615405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Volatility is a key concept for understanding the dual relationships between the economic variables since it is inversely related to the stability of economies. Many models such as GARCH models have been constructed through time to understand which determinants and conditions can affect the volatility. These models mostly show the significant relationships between the volatilities generated by the low frequency macroeconomic activities and the high frequency financial variables in a stochastic way. However, it is required to check whether there exist deterministic effects of volatilities on high frequency economic variables. In order to reveal these deterministic effects, we developed a new component-wise model, namely GARCH-M MIDAS model. We formulate this model on stock prices and exchange rates, in which long run volatility is driven by consumer price and industrial production indexes in a separate way. Hence, our empirical analysis supports that both types of the volatilities have statistically significant deterministic effects on the asset pricing of high frequency financial variables. We also find that macroeconomic activities have a significant role on the asset pricing in long horizons.
{"title":"Deterministic Effects of Volatility on Mixed Frequency GARCH in Means MIDAS Model: Evidence from Turkey","authors":"Fehmi Özsoy, N. Dogan","doi":"10.33818/ier.1053547","DOIUrl":"https://doi.org/10.33818/ier.1053547","url":null,"abstract":"Volatility is a key concept for understanding the dual relationships between the economic variables since it is inversely related to the stability of economies. Many models such as GARCH models have been constructed through time to understand which determinants and conditions can affect the volatility. These models mostly show the significant relationships between the volatilities generated by the low frequency macroeconomic activities and the high frequency financial variables in a stochastic way. However, it is required to check whether there exist deterministic effects of volatilities on high frequency economic variables. In order to reveal these deterministic effects, we developed a new component-wise model, namely GARCH-M MIDAS model. We formulate this model on stock prices and exchange rates, in which long run volatility is driven by consumer price and industrial production indexes in a separate way. Hence, our empirical analysis supports that both types of the volatilities have statistically significant deterministic effects on the asset pricing of high frequency financial variables. We also find that macroeconomic activities have a significant role on the asset pricing in long horizons.","PeriodicalId":32692,"journal":{"name":"International Econometric Review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44394108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the most optimal hedging portfolio for some selected emerging and developed markets by employing dynamic conditional variances and dynamic conditional covariances. Throughout the study, we used the daily index values of some selected investment instruments. The data contains the period from 02/01/2006 to 01/11/2018. In this essay, to obtain the most efficient hedging portfolio for each emerging country, firstly, we used Dcc-Figarch specifications to measure volatility. Secondly, we checked the robustness of the model by observing its forecast performance. As out-of-sample forecast performance has an ability to assist empirical evidence to outliers and data mining in a detailed way as well as it reflects better the information available to the forecaster in “real-time” out-of-sample forecasting is more appropriate to be used in this regard. Then, we calculated the mean absolute error (MAE) to detect the most fitted model. Thirdly, we mentioned two methods: Optimal hedge ratio and optimal portfolio weight. These methods are two hedging portfolio implications. Lastly, we will propose an economic rationale behind the results.
{"title":"Optimal Dynamic Hedging in Selected Markets","authors":"Tunahan Yilmaz","doi":"10.33818/ier.839349","DOIUrl":"https://doi.org/10.33818/ier.839349","url":null,"abstract":"This study examines the most optimal hedging portfolio for some selected emerging and developed markets by employing dynamic conditional variances and dynamic conditional covariances. Throughout the study, we used the daily index values of some selected investment instruments. The data contains the period from 02/01/2006 to 01/11/2018. In this essay, to obtain the most efficient hedging portfolio for each emerging country, firstly, we used Dcc-Figarch specifications to measure volatility. Secondly, we checked the robustness of the model by observing its forecast performance. As out-of-sample forecast performance has an ability to assist empirical evidence to outliers and data mining in a detailed way as well as it reflects better the information available to the forecaster in “real-time” out-of-sample forecasting is more appropriate to be used in this regard. Then, we calculated the mean absolute error (MAE) to detect the most fitted model. Thirdly, we mentioned two methods: Optimal hedge ratio and optimal portfolio weight. These methods are two hedging portfolio implications. Lastly, we will propose an economic rationale behind the results.","PeriodicalId":32692,"journal":{"name":"International Econometric Review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43979000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Smooth Threshold Autoregressive models and Markov process: An application to the Lebanese GDP growth rate","authors":"Jean-françois Verne","doi":"10.33818/ier.791543","DOIUrl":"https://doi.org/10.33818/ier.791543","url":null,"abstract":"","PeriodicalId":32692,"journal":{"name":"International Econometric Review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42305207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Do Green and Energy Indices Outperform BSESENSEX in India? Some evidence on investors’ commitment towards climate change","authors":"D. Mukhopadhyay, Nityananda Sarkar","doi":"10.33818/ier.787620","DOIUrl":"https://doi.org/10.33818/ier.787620","url":null,"abstract":"","PeriodicalId":32692,"journal":{"name":"International Econometric Review","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41734487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inflation and Inflation Uncertainty in Growth Model of Barro: An Application of Random Forest Model","authors":"Houcine Senoussi","doi":"10.33818/ier.854697","DOIUrl":"https://doi.org/10.33818/ier.854697","url":null,"abstract":"","PeriodicalId":32692,"journal":{"name":"International Econometric Review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46033934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, the price and income elasticities of Turkey's imported crude oil demand are analysed. In this context, annual time series data covering the period 1970-2018 are preferred for imported crude oil, real price for crude oil and real GDP. As known, Turkey is an energy dependent country especially in fossil fuels. Therefore, estimating Turkey's crude oil demand equations is very significant to analyse the consumption trend and the future expectations in terms of this energy resource. This study employs Harvey's Structural Time Series Modelling Method (STSM) with the underlying energy demand trend (UEDT) concept for determining the relations among income, price and crude oil import demand. The empirical results show that the income and price elasticities of crude oil demand in Turkey are 0.66 and -0.11, respectively. The estimated elasticities suggest that income and price elasticities for the imported crude oil demand are inelastic.
{"title":"Estimating the Price and Income Elasticities of Crude Oil Import Demand for Turkey","authors":"İsmail Kavaz","doi":"10.33818/ier.754989","DOIUrl":"https://doi.org/10.33818/ier.754989","url":null,"abstract":"In this study, the price and income elasticities of Turkey's imported crude oil demand are analysed. In this context, annual time series data covering the period 1970-2018 are preferred for imported crude oil, real price for crude oil and real GDP. As known, Turkey is an energy dependent country especially in fossil fuels. Therefore, estimating Turkey's crude oil demand equations is very significant to analyse the consumption trend and the future expectations in terms of this energy resource. This study employs Harvey's Structural Time Series Modelling Method (STSM) with the underlying energy demand trend (UEDT) concept for determining the relations among income, price and crude oil import demand. The empirical results show that the income and price elasticities of crude oil demand in Turkey are 0.66 and -0.11, respectively. The estimated elasticities suggest that income and price elasticities for the imported crude oil demand are inelastic.","PeriodicalId":32692,"journal":{"name":"International Econometric Review","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41928537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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