Visualising the Optimistic, Realistic, and Pessimistic Financial Distress Outlooks for Airport Operations in Malaysia

This paper aims to visualise three financial distress outlooks using computer simulations. The financial distress exposure for airport operations in Malaysia between 1991 and 2021 is given by Altman Z”-score and modelled by the multivariate generalized linear model (MGLM). Seven determinants contributing to the financial distress from literature are examined. The determinant series are fitted individually by using linear model with time series components and autoregressive integrated moving average models to forecast values for the next 10 financial years. Future short- to long-term memory effects following COVID-19 are apparent in time series plots. In the simulations, the MGLM procedure utilised Gaussian, gamma, and Cauchy probability distributions associated with expectations and challenges of doing business as well as uncertainties in the economy. The underlying trends of realistic, optimistic, and pessimistic financial distress outlooks insinuate that the increasing risk of financial distress of airport operations in Malaysia is expected to continue for the next decade.

Oil price shocks, economic policy uncertainty, and green finance: a case of China

This study investigates the long- and short-run effects of crude oil price (COP) and economic policy uncertainty (EPU) on China's green bond index (GBI) using the quantile autoregressive distributed lag model. The empirical results show that COP and EPU produce a significant positive and negative influence on GBI in the long-run across most quantiles, respectively, but their short-run counterparts are opposite direction and only significant in higher quantiles. Thus, major contributions are made accordingly and shown in the following aspects. The findings emphasise the importance of understanding how COP and EPU affect China's green bond market for the first time. In addition, both the long- and short-run effects are captured, but long-run shocks primarily drive the green bond market. Finally, time- and quantile-varying analyses are adopted to explain the nexus between COP and EPU to GBI, which considers not only different states of the bond market but also events that occur in different time periods. Some detailed policies, such as a unified and effective green bond market, an early warning mechanism of oil price fluctuation, and prudent economic policy adjustments, are beneficial for stabilising the green finance market.

Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid‐19 Pandemic

This study presents forecasting methods using time series analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an artificial intelligence method and different statistical methodologies that can help predict the future of Covid‐19 cases. Various forecasting methods in time series analysis such as ARIMA, Holt's trend, naive, simple exponential smoothing, TBATS, and MAPE are extended for the study. It also involved the case fatality rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid‐19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.Alternate :Este estudio presenta métodos de pronóstico que utilizan el análisis de series temporales para los casos confirmados, el número de muertes y casos recuperados, y el estado de vacunación individual en diferentes estados de la India. Su objetivo es pronosticar los casos confirmados y la tasa de mortalidad y desarrollar un método de inteligencia artificial y diferentes metodologías estadísticas que puedan ayudar a predecir el futuro de los casos de Covid‐19. Para el estudio se adaptaron varios métodos de pronóstico para el análisis de series temporales como ARIMA, la tendencia de Holt, el ingenuo, el suavizado exponencial simple, TBATS y MAPE. También se incluyó la tasa de fatalidades para el número de muertes y casos confirmados para los respectivos estados de la India. Este estudio incluye los valores de pronóstico para el número de casos positivos, los pacientes curados, la tasa de mortalidad y la tasa de fatalidades para los casos de Covid‐19. Entre todos los métodos de pronóstico utilizados en este estudio, el método ingenuo y el de suavización exponencial simple muestran un mayor número de casos positivos y de pacientes curados.Alternate :抄録本研究は、インドの州における確定症例、死亡数及び回復例、および個人のワクチン接種状況に関する時系列分析を用いた予測方法を提示する。確定症例と死亡率を予測し、人工知能を用いた方法とCOVID‐19の症例の将来を予測するのに役立ついくつかの統計学的方法論を開発することを目指す。ARIMA、Holtのトレンド、単純法、単純指数平滑化法、TBATS、MAPEなどの時系列解析における各種予測法を拡張した。また、インドの各州の死亡者数と確定症例数の致死率も含んだ。本研究は、COVID‐19症例に対する、陽性症例数、治癒患者数、死亡率、および致死率に対する予測値を含む。この研究に含まれるすべての予測法の中で、単純法と単純指数平滑法は、陽性者数と治癒患者数の増加を予測した。

Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19

Forecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations. To forecast the direction of stock markets, the inclusion of leading indicators to volatility models is highly important;however, such series are generally at different frequencies. The paper proposes the GARCH-MIDAS-LSTM model, a hybrid method that benefits from LSTM deep neural networks for forecast accuracy, and the GARCH-MIDAS model for the integration of effects of low-frequency variables in high-frequency stock market volatility modeling. The models are being tested for a forecast sample including the COVID-19 shut-down after the first official case period and the economic reopening period in in Borsa Istanbul stock market in Türkiye. For this sample, significant uncertainty existed regarding future economic expectations, and the period provided an interesting laboratory to test the forecast effectiveness of the proposed LSTM augmented model in addition to GARCH-MIDAS models, which included geopolitical risk, future economic expectations, trends, and cycle industrial production indices as low-frequency variables. The evidence suggests that stock market volatility is most effectively modeled with geopolitical risk, followed by industrial production, and a relatively lower performance is achieved by future economic expectations. These findings imply that increases in geopolitical risk enhance stock market volatility further, and that industrial production and future economic expectations work in the opposite direction. Most importantly, the forecast results suggest suitability of both the GARCH-MIDAS and GARCH-MIDAS-LSTM models, and with good forecasting capabilities. However, a comparison shows significant root mean squared error reduction with the novel GARCH-MIDAS-LSTM model over GARCH-MIDAS models. Percentage decline in root mean squared errors for forecasts are between 39% to 95% in LSTM augmented models depending on the type of economic indicator used. The proposed approach offers a key tool for investors and policymakers.

Investigating the N-shaped EKC in China: An imperious role of energy use and health expenditures

Since the industrial revolution, the concentration of greenhouse gases (GHGs) has been steadily increasing. Notably, China emitted 27% of the world's GHGs in 2019, making it the world's most significant contributor to climate degradation. The key objectives of this investigation are to ascertain the N-shaped association between CO2 emissions and economic growth in the presence of energy use and domestic government health expenditures. Besides, the research inspected the role of the Belt and Road Initiative through economic globalization in China. The study utilized the Autoregressive Distributed Lag model and found that N-shaped EKC exists in China. Furthermore, the study discovered that economic globalization improves ecological excellence in the short run. Nonetheless, energy consumption and health expenditures considerably amplify the intensity of CO2 emanation in China in the long run. The research suggested that installing green industry through economic globalization can imperatively lessen environmental degradation. Moreover, installing technological firms will be more beneficial in the long run to overcome environmental degradation rather than importing from other countries. The study elaborated momentous causation effects among the study variables through the Granger causality test.

House prices and COVID-19 pandemic shocks in India: a nonlinear ARDL analysis

PurposeIndia is one of those countries that are severely affected by the COVID-19 pandemic. With the upsurge in the cases, the country recorded high unemployment rates, economic uncertainties and slugging growth rates. This adversely affected the real estate sector in India. As the relation of the housing market with the gross domestic product is quite lasting thus, the decline in housing prices has severely impacted the economic growth of the nation. Hence, the purpose of this paper is to gauge the asymmetric impact of COVID-19 shocks on housing prices in India.Design/methodology/approachStudies revealed the symmetric impact of macroeconomic variables, and contingencies on housing prices dominate the literature. However, the assumption of linearity fails to apprehend the asymmetric dynamics of the housing sector. Thus, the author uses a nonlinear autoregressive distributed lag model to address this limitation and test the existence of short- and long-run asymmetry.FindingsThe findings revealed the long- and short-run asymmetric impact of the COVID-19 outbreak and the peak of the COVID-19 on housing prices. The results indicate that the peak of COVID-19 had a greater impact on housing prices in comparison to the outbreak of COVID-19. This can be explained as prices will revert to normal at a speed of 0.978% with the decline in the number of COVID-19 cases. Whereas the housing prices rise at a rate of 0.714 as a result of government intervention to deal with the ill effects of the COVID-19 outbreak. Moreover, it can be inferred that both the outbreak and peak of COVID-19 will lead to a minimal decline in housing prices, while with the decline in the number of cases and reduction in the impact of the outbreak of COVID, the housing prices will rise at an increasing rate.Originality/valueTo the best of the authors' knowledge, this is the first study to understand the impact of the outbreak and peak of COVID-19 on the housing prices separately.

Risk measurement in Bitcoin market by fusing LSTM with the joint-regression-combined forecasting model

PurposeThe purpose of the paper is to better measure the risks and volatility of the Bitcoin market by using the proposed novel risk measurement model.Design/methodology/approachThe joint regression analysis of value at risk (VaR) and expected shortfall (ES) can effectively overcome the non-elicitability problem of ES to better measure the risks and volatility of financial markets. And because of the incomparable advantages of the long- and short-term memory (LSTM) model in processing non-linear time series, the paper embeds LSTM into the joint regression combined forecasting framework of VaR and ES, constructs a joint regression combined forecasting model based on LSTM for jointly measuring VaR and ES, i.e. the LSTM-joint-combined (LSTM-J-C) model, and uses it to investigate the risks of the Bitcoin market.FindingsEmpirical results show that the proposed LSTM-J-C model can improve forecasting performance of VaR and ES in the Bitcoin market more effectively compared with the historical simulation, the GARCH model and the joint regression combined forecasting model.Social implicationsThe proposed LSTM-J-C model can provide theoretical support and practical guidance to cryptocurrency market investors, policy makers and regulatory agencies for measuring and controlling cryptocurrency market risks.Originality/valueA novel risk measurement model, namely LSTM-J-C model, is proposed to jointly estimate VaR and ES of Bitcoin. On the other hand, the proposed LSTM-J-C model provides risk managers more accurate forecasts of volatility in the Bitcoin market.

Green metal price volatility and environmentally sustainable real estate development nexus: A global perspective from post COVID-19 pandemic

This study examined the relationship between green metal price shocks and green real estate development using the Structural Vector Autoregressive (SVAR) model. Real-time daily dataset for the study covers the period from January 4, 2021, to December 30, 2022. The findings are presented using two methods: cumulative impulse responses and variance decompositions. The cumulative impulse responses show how structural shocks affect the volatility of green real estate over time. The variance decompositions show the percentage of the variation in green real estate volatility that is caused by each structural shock. The results showed that green real estate development responded negatively to green metal shocks for at least half of the observed period, and the energy price, specifically oil, had a greater and more persistent negative impact. This suggests that changes in oil prices may continue to have a significant influence on the development of green real estate projects and the broader transition towards environmentally sustainable practices in the construction industry. The explained variable, on the other hand, had a positive response to shocks associated with green finance in the latter part of the observed period. Policy and practical implications of the findings have also been discussed. © 2023

Impact of COVID-19 Pandemic on Stock Market: Evidence from Bombay Stock Exchange

The present study attempts to examine the behavior of the Indian stock market during the COVID 19 pandemic. In the study, a systemic approach is undertaken, where three significant events, namely declaration of COVID 19 as a pandemic, first death in India by COVID 19 and imposition of the nationwide lockdown, have been considered and the market reaction around the events is studied. The study employed event study methodology with daily return series of hundred and one firms, constituting the BSE 100 index. The study observed that the investors did not provide much attention to the announcement of the COVID 19 as a pandemic. However, the investors started panicking after the first death by the pandemic was reported, as witnessed from the negative abnormal returns-the return further declined on the announcement of nationwide lockdown. However, the sectors like FMCG, Health and Technology earned an abnormal positive return on the announcement of the nationwide lockdown. The study further employed GARCH Model and observed high volatility in the return series during the events. Overall, the study concludes that the events have a significant negative impact on the Indian stock market.

Indirect inference estimation of higher-order spatial autoregressive models

This paper proposes estimating parameters in higher-order spatial autoregressive models, where the error term also follows a spatial autoregression and its innovations are heteroskedastic, by matching the simple ordinary least squares estimator with its analytical approximate expectation, following the principle of indirect inference. The resulting estimator is shown to be consistent, asymptotically normal, simulation-free, and robust to unknown heteroskedasticity. Monte Carlo simulations demonstrate its good finite-sample properties in comparison with existing estimators. An empirical study of Airbnb rental prices in the city of Asheville illustrates that the structure of spatial correlation and effects of various factors at the early stage of the COVID-19 pandemic are quite different from those during the second summer. Notably, during the pandemic, safety is valued more and on-line reviews are valued much less. © 2023 Taylor & Francis Group, LLC.

Climate change investing and COVID-19: evidence from return volatility

This study investigates the impact of COVID-19 on the volatility of climate-related investments in India. The study evaluates the certainty of investments related to climate change in India. The GARCH (1, 1) model is employed on the CARBONEX index of India. The author has found evidence of increasing volatility due to COVID-19. Also, a large degree of volatility persistency has been exhibited by S&P BSE CARBONEX due to COVID-19. In conclusion of the study, the author discovered that during COVID-19, a rise of 145.75% in unconditional variance was seen.

Maintaining the stability of the housing market in the event of an economic shock

PurposeThis paper aims to identify the economic stimulus measures that ensure stability of the Lithuanian housing market in the event of an economic shock.Design/methodology/approachThe econometric analysis includes stationarity test, Granger causality test, correlation analysis, autoregressive distributed lag models and cointegration analysis using ARDL bounds testing.FindingsThe econometric modelling reveals that the housing price in Lithuania correlates with quarterly changes in the gross domestic product and approves that the cycles of the real estate market are related to the economic cycles. Economic stimulus measures should mainly focus on stabilizing the economics, preserving the cash and deposits of households, as well as consumer spending in the case of economic shock.Originality ValueThis study is beneficial for policy makers to make decisions to maintain stability in the housing market in the event of any economic shock.

VAR, ARIMAX and ARIMA models for nowcasting unemployment rate in Ghana using Google trends

The analysis of the high volume of data spawned by web search engines on a daily basis allows scholars to scrutinize the relation between the user's search preferences and impending facts. This study can be used in a variety of economics contexts. The purpose of this study is to determine whether it is possible to anticipate the unemployment rate by examining behavior. The method uses a cross-correlation technique to combine data from Google Trends with the World Bank's unemployment rate. The Autoregressive Integrated Moving Average (ARIMA), Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) and Vector Autoregression (VAR) models for unemployment rate prediction are fit using the analyzed data. The models were assessed with the various evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute error (MedAE), and maximum error (ME). The average outcome of the various evaluation metrics proved the significant performance of the models. The ARIMA (MSE = 0.26, RMSE = 0.38, MAE = 0.30, MAPE = 7.07, MedAE = 0.25, ME = 0.77), ARIMAX (MSE = 0.22, RMSE = 0.25, MAE = 0.29, MAPE = 6.94, MedAE = 0.25, ME = 0.75), and VAR (MSE = 0.09, RMSE = 0.09, MAE = 0.20, MAPE = 4.65, MedAE = 0.20, ME = 0.42) achieved significant error margins. The outcome demonstrates that Google Trends estimators improved error reduction across the board when compared to model without them.

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh's 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city's high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Performance Of Traffic Accidents Prediction Models

Modeling traffic accident frequency is an important issue to better understand the accident trends and the effectiveness of current traffic policies and practices in different countries. The main objective of this study is to model traffic road accidents, fatalities, and injuries in Jordan, using different modeling techniques including regression, Artificial Neural Network (ANN), and Autoregressive Integrated Moving Average (ARIMA) models, and to evaluate the impact of Covid-19 pandemic on traffic accident statistics for the year of 2020. To accomplish these objectives, traffic accidents, registered vehicles (REGV), population (POP), and economic gross domestic product (GDP) data from 1995 through 2020 were obtained from related sources in Jordan. Results of the analysis revealed that accidents, fatalities, and injuries have an increasing trend in Jordan. Also, it was found that the developed ANN models were more accurate for accidents, injuries, and fatalities prediction than ARIMA, which was also better than regression which comes in the last place in terms of its prediction power. Finally, it was concluded that strategies are undertaken by the government of Jordan to combat Covid-19;including complete and partial banning on travel, had resulted in a considerable reduction of accidents, injuries, and fatalities by about 35, 37, and 50%, respectively.

The Trilogy of the Chinese Apple Futures Market: Price Discovery, Risk-Hedging and Cointegration

The agricultural futures market plays an extremely important role in price discovery, hedging risks, integrating agricultural markets and promoting agricultural economic growth. China is the largest apple producer and consumer in the world. In 2017, Chinese apple futures were listed on the Zhengzhou Commodity Exchange (CZCE) as the first fruit futures contract globally. This paper aims to study the efficiency of the apple futures market by using the Wild Bootstrapping Variance Ratio model to estimate the price discovery function, the ARIMA-GARCH model to estimate the risk-hedging function, and the ARDL-ECM model to estimate the cointegration relationship of the futures and spot market. Experimental results firstly demonstrate that the apple futures market conforms to the weak-form efficiency, which indicates that it is efficient in price discovery. Secondly, the apple futures market is not of semi-strong efficiency because it generated abnormal profit margins amid China–US trade friction, climate disaster, and COVID-19;in terms of the degree of impact, the COVID-19 pandemic had the greatest impact, followed by the rainstorm disaster and trade friction. Thirdly, the results of this study indicate that the cointegration relationships exist between the futures market and the spot markets of the main producing areas. This paper is not only conducive to sustainable development of the global fresh or fruit futures market, but also has potential and practical importance for China in developing the agricultural futures market, strengthening market risk management and promoting market circulation.

Non-Renewable and Renewable Energies, and COVID-19 Pandemic: Do They Matter for China’s Environmental Sustainability?

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people’s everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China’s carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper.

How Energy Sector Reacted to COVID-19 Pandemic? Empirical Evidence from an Emerging Market Economy

The European Union is facing the highest natural gas prices in 15 years, owing largely to an upward trend in electricity prices, which is also on an uphill curve. However, the rise in electricity and natural gas prices is a widespread phenomenon that is being felt not only in Europe but also globally, as economic activity resumes and energy consumption returns to prepandemic levels. Consequently, this paper investigates how COVID-19 influenced the Romanian energy market. To accomplish our goal, we used daily data for variables and market indices that characterize COVID-19 and the energy market from July 1 to December 21, 2021. The results of the GARCH (1, 1) model estimation show that the major performer in Romania’s energy allocation and supply market had the highest conditional variance. In addition, the ARDL model was chosen because of the variable integration mix (order 0 and 1), as well as the VAR and the Granger causality framework. The empirical results of ARDL models provide the first conclusion of the analysis, indicating that the number of short-term connections was greater than long-term connections, which is also explained by the presence of short episodes of high volatility recorded in the investigated time interval. Another conclusion drawn from this study is that COVID-19 cases registered in Europe and around the world have made a significant contribution to explaining the evolution of the energy market, owing to the large number of cases registered in these regions and the level of contagion transmitted from these markets to the energy market. Furthermore, based on the Granger causality test results, only one-way causal relationships were identified from the variables that capture the evolution of the COVID-9 pandemic to the yields of Romanian energy companies. The novelty of this article is the examination of the impact of COVID-19 on the energy market throughout the fourth wave of coronavirus using the GARCH framework, the ARDL model, which allows for the capture of both short- and long-term reactions, the variance decomposition, and the Granger causality test. Because of the ongoing changes in the pandemic’s evolution, additional research on this topic is undoubtedly on the horizon in the near future.

The Validity of Autoregressive Integrated Moving Average Approach to Forecast the Spread of COVID-19 Pandemic in Africa

Africa’s first COVID-19 case was recorded in Egypt on February 14, 2020. Although it is not as expected by the World Health Organization (WHO) and other international organizations, currently a large number of Africans are getting infected by the virus. In this work, we studied the trend of the COVID-19 outbreak generally in Africa as a continent and in the five African regions separately. The study also investigated the validity of the ARIMA approach to forecast the spread of COVID-19 in Africa. The data of daily confirmed new COVID-19 cases from February 15 to October 16, 2020, were collected from the official website of Our World in Data to construct the autoregressive integrated moving average (ARIMA) model and to predict the trend of the daily confirmed cases through STATA 13 and EViews 9 software. The model used for our ARIMA estimation and prediction was (3, 1, 4) for Africa as a continent, ARIMA (3, 1, 3) for East Africa, ARIMA (2, 1, 3) for West Africa, ARIMA (2, 1, 3) for Central Africa, ARIMA (1, 1, 4) for North Africa, and ARIMA (4, 1, 5) for Southern Africa. Finally, the forecasted values were compared with the actual number of COVID-19 cases in the region. At the African level, the ARIMA model forecasted values and the actual data have similar signs with slightly different sizes, and there were some deviations at the subregional level. However, given the uncertain nature of the current COVID-19 pandemic, it is helpful to forecast the future trend of such pandemics by employing the ARIMA model.

Investment certainty in ESG investing due to COVID-19: evidence from India

This article analyses the impact of COVID-19 on the volatility of ESG investing in India. Furthermore, it assesses the investment certainty in ESG related activities in India after detecting the first case of disease. A generalised autoregressive conditional heteroscedasticity model has been applied to the S&P BSE 100 ESG Index returns. The results show that after COVID-19, the risk related to the market price of the S&P BSE 100 ESG Index has increased, and the certainty of investment decreased. Further, the result of the GARCH (1, 1) model estimation indicates the presence of a large degree of persistency in the S&P BSE 100 ESG index. In addition, after reporting the first case of COVID-19, unconditional variance has been increased by 211.98%.