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1 – 10 of over 7000Karan Raj and Devashish Sharma
The purpose of this study is to construct a new index to assess the impact of an energy price shock on macroeconomic indicators of India. This paper also shows a comparative…
Abstract
Purpose
The purpose of this study is to construct a new index to assess the impact of an energy price shock on macroeconomic indicators of India. This paper also shows a comparative analysis of the constructed index along with pre-existing World Bank and International Monetary Fund indices on energy.
Design/methodology/approach
This paper uses three vector autoregressions and compute the long-term impact of the indices on the considered macroeconomic variables through impulse response functions.
Findings
This paper finds that an energy price shock has a detrimental impact on the macroeconomic indicators of India in the long run. This study also finds that the constructed index acts as a relatively more sensitive index in comparison to the International Monetary Fund and World Bank indices, which is bespoke to a developing economy case. This sensitivity is ascribed to dynamic weighting for a different basket of energy components, which are more pertinent to an Indian context.
Originality/value
The novelty of this research lies in the construction of a new index and its comparison to the existing ones. This study justifies why a developing economy would require a different measure of energy as opposed to the existing indices.
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Zengli Mao and Chong Wu
Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the…
Abstract
Purpose
Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.
Design/methodology/approach
The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.
Findings
Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.
Practical implications
The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.
Social implications
If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.
Originality/value
Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.
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Malihe Ashena, Hamid Laal Khezri and Ghazal Shahpari
This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials…
Abstract
Purpose
This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.
Design/methodology/approach
The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.
Findings
The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.
Originality/value
This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.
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Emmanuel Joel Aikins Abakah, Aviral Kumar Tiwari, Johnson Ayobami Oliyide and Kingsley Opoku Appiah
This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from…
Abstract
Purpose
This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.
Design/methodology/approach
This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.
Findings
Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.
Practical implications
The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.
Originality/value
The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.
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Xiaojie Xu and Yun Zhang
The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention…
Abstract
Purpose
The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.
Design/methodology/approach
The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.
Findings
The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.
Originality/value
Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.
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Xiaojie Xu and Yun Zhang
The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important…
Abstract
Purpose
The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.
Design/methodology/approach
The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.
Findings
The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.
Originality/value
The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.
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Mustafa Tevfik Kartal, Serpil Kılıç Depren and Özer Depren
By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI)…
Abstract
Purpose
By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks.
Design/methodology/approach
A quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness.
Findings
Predictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables; the pandemic and rent have the highest effect on the indices; The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles; the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles; the results for RPPI, NRPPI and ORPPI are consistent and robust.
Research limitations/implications
The results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study.
Practical implications
The COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables.
Social implications
Emerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets.
Originality/value
The QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens.
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Abhishek Poddar, Sangita Choudhary, Aviral Kumar Tiwari and Arun Kumar Misra
The current study aims to analyze the linkage among bank competition, liquidity and loan price in an interconnected bank network system.
Abstract
Purpose
The current study aims to analyze the linkage among bank competition, liquidity and loan price in an interconnected bank network system.
Design/methodology/approach
The study employs the Lerner index to estimate bank power; Granger non-causality for estimating competition, liquidity and loan price network structure; principal component for developing competition network index, liquidity network index and price network index; and panel VAR and LASSO-VAR for analyzing the dynamics of interactive network effect. Current work considers 33 Indian banks, and the duration of the study is from 2010 to 2020.
Findings
Network structures are concentrated during the economic upcycle and dispersed during the economic downcycle. A significant interaction among bank competition, liquidity and loan price networks exists in the Indian banking system.
Practical implications
The study meaningfully contributes to the existing literature by adding new insights concerning the interrelationship between bank competition, loan price and bank liquidity networks. While enhancing competition in the banking system, the regulator should also pay attention toward making liquidity provisions. The interactive network framework provides direction to the regulator to formulate appropriate policies for managing competition and liquidity while ensuring the solvency and stability of the banking system.
Originality/value
The study contributes to the limited literature concerning interactive relationship among bank competition, liquidity and loan price in the Indian banks.
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Yitian Xiao, Jiawu Dai and J. Alexander Nuetah
The purpose of this paper is to test the overshooting effects of monetary expansion on prices of agricultural products at farm production, processing and circulation stages in…
Abstract
Purpose
The purpose of this paper is to test the overshooting effects of monetary expansion on prices of agricultural products at farm production, processing and circulation stages in China, and to investigate the heterogeneity of the overshooting mechanisms in these three links.
Design/methodology/approach
Empirical results are obtained through the vector error correction model and the overshooting framework proposed by Saghaian et al. (2002b). Specifically, we first apply the Dickey–Fuller generalized least squares (DF-GLS) method to test the stationarity of the key variables, and then use the Johansen’s (1991) method to conduct the cointegration test. Finally, the vector error correction model is employed to examine the overshooting hypotheses in the three stages of China’s agricultural sector.
Findings
Empirical results indicate that overshooting of prices relative to monetary expansion in China’s agricultural sector is a common phenomenon, but with significant heterogeneity. Firstly, at the stage of agricultural production, the overshooting degree and restoration rate of material price are greater than those of agricultural products price. Secondly, at the processing stage of agricultural products, both the purchase price of agricultural products and industrial producer price have an overshooting effect, but the overshooting effect of the former is more significant than the latter. Thirdly, at the circulation stage of agricultural products, the overshooting coefficient of the wholesale price index of agricultural products is the most significant, while that of the retail and purchase price of agricultural products is not significant.
Originality/value
The paper contributes to proposing a comprehensive framework on testing the overshooting effects for three main stages of agricultural sector in China and empirically investigating the heterogeneity of the overshooting mechanisms in different stages with time series methods.
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Hanan Naser, Fatima Al-aali, Yomna Abdulla and Rabab Ebrahim
Over the last decade, investments in green energy companies have witnessed noticeable growth rates. However, the glacial pace of the world economic restoration due to COVID-19…
Abstract
Purpose
Over the last decade, investments in green energy companies have witnessed noticeable growth rates. However, the glacial pace of the world economic restoration due to COVID-19 pandemic placed a high degree of uncertainty over this market. Therefore, this study investigates the short- and long-term relationships between COVID-19 new cases and WilderHill New Energy Global Innovation Index (NEX) using daily data over the period from January 23, 2020 to February 1, 2023.
Design/methodology/approach
The authors utilize an autoregressive distributed lag bounds testing estimation technique.
Findings
The results show a significant positive impact of COVID-19 new cases on the returns of NEX index in the short run, whereas it has a significant negative impact in the long run. It is also found that the S&P Global Clean Energy Index has a significant positive impact on the returns of NEX index. Although oil has an influential effect on stock returns, the results show insignificant impact.
Practical implications
Governments have the chance to flip this trend by including investment in green energy in their economic growth stimulation policies. Governments should highlight the fundamental advantages of investing in this type of energy such as creating job vacancies while reducing emissions and promoting innovation.
Originality/value
First, as far as the authors are aware, the authors are the first to examine the effect of oil prices on clean energy stocks during COVID-19. Second, the authors contribute to studies on the relationship between oil prices and renewable energy. Third, the authors add to the emerging strand of literature on the impact of COVID-19 on various sectors of the economy. Fourth, the findings of the paper can add to the growing literature on sustainable development goals, in specific the papers related to energy sustainability.
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