Search results

Qatar, a wealthy country with an open economy has limited arable land. To meet its domestic food demand, the country heavily relies on food imports. Additionally, the over three year-long economic embargo enforced by regional neighbors and the covariate shock of the COVID-19 pandemic have demonstrated the country's vulnerability to food insecurity and potential for structural breaks in macroeconomic data. The purpose of this paper is to examine short- and long-run determinants of Qatar's imports of aggregate food, meats, dairy and cereals in the presence of structural breaks.

The authors use 24 years of food imports, gross domestic product (GDP) and consumer price index (CPI) data obtained from Qatar's Planning and Statistics Authority. They use the autoregressive distributed lag (ARDL) cointegration framework and Chambers and Pope's exact nonlinear aggregation approach.

Unit root tests in the presence of structural breaks reveal a mixture of I (1) and I (0) variables for which standard cointegration techniques do not apply. The authors found evidence of a significant long-run relationship between structural changes and food imports in Qatar. Impulse response functions indicate full adjustments within three-quarters of a year in the event of an exogenous shock to imports.

An exogenous shock of one standard deviation on this variable would reduce Qatar's food imports by about 2.5% during the first period but recover after the third period.

The failure of past aggregate food demand studies to go beyond standard unit root testing creates considerable doubt about the accuracy of their elasticity estimates. The authors avoid that to provide more credible findings.

The authors investigate the role played by the political climate and other covariates on the prevalence of undernourishment for 34 low-income countries across a 21-year period.

Political climate is measured in terms of political freedoms and civil liberties. The authors follow a Granger causality approach, which looks at predictive causality (i.e. causality in a temporal sense). For the socio-economic data, the authors rely on annual time series data from the World Bank.

Most of the findings are in keeping with our expectations: (1) Lowering women's fertility rate lowers undernourishment; (2) undernourishment converges to its long-run equilibrium path in response to changes in income, political climate, health expenditure, fertility rate and drinking water access; (3) the effect of an instantaneous shock from income, changes to the political climate, health expenditure, fertility rate and drinking water access on undernourishment are completely adjusted in the long run. One surprising result is that there is a positive and significant relationship between the prevalence of undernourishment and political freedom. The authors offer several possible explanations for this unexpected result.

Given our results, careful attention to the co-curation of policies is desirable. As an example, the authors would advocate a more proactive role by the richer countries in terms of their commitments to foreign aid in addressing the identified problems.

The authors use advanced panel data techniques, considering a long span of time. Unlike other studies which aim to establish correlations, the authors test for Granger causality.

The purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.

Two methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.

The causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.

The obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.

This paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

This paper aims to examine the causation linking financial technology to economic growth in the East African Community states from 1997 to 2019.

Autoregressive distributed lag is used. Gross domestic product per capita proxies economic growth, automated teller machines, point of sale, debit card ownership and mobile banking measure financial technology.

The results unveil a significant relationship between financial technology and economic growth. The findings show bidirectional causality between automated teller machine and economic growth, with unidirectional causation from economic growth to point of sales and internet banking, mobile banking and government effectiveness to economic growth. The error correction term is negatively significant, demonstrating a long-term convergence between Fintech measures and economic growth.

The governments should effectively enact and implement policies that protect investments in financial technologies to boost economic growth in the East African Community countries. The government should reduce taxes on financial technology equipment and related services. The use of automated teller machine, debit card ownership and internet banking should be encouraged through cashless transactions. Financial institutions should adopt cashless operation policies to encourage the use of financial technologies.

Research results on the bond between financial technology and economic growth are not conclusive. These studies demonstrate that technological innovations are double edged-swords, with both positive and negative sides. The results are conflicting; some reveal positive relationships, while others show negative links. Hence, research is required to fill the lacuna.

The worldwide imposition of lockdown measures to control the 2020 coronavirus disease 2019 (COVID-19) outbreak has shifted most executive communications with external stakeholders online, resulting in quick responses from stakeholders. This study aims to understand how presentational styles exhibited in online communication induce immediate audience responses and empirically test the effectiveness of reactive impression management tactics.

The authors analyze presentational styles using MP3 files containing executive utterances during earnings call conferences held by S&P 100-listed firms after June 2020, the quarter after the World Health Organization declared the COVID-19 outbreak a pandemic on March 11, 2020. Using timestamps, the authors link each utterance to a 1-minute interval change in the ask/bid prices of the stocks that occurs a minute after the corresponding utterance begins.

Exhibiting an informational presentation style in earnings calls leads to positive and immediate audience responses. Managers tend to increase their reliance on promotional presentation styles rather than on informational ones when quarterly earnings exceed market forecasts.

Drawing on organizational genre theory, this research identifies the discrepancy between the presentation styles that audiences positively respond to and those that managers tend to exhibit in earnings calls and provides a reactive impression management typology for immediate responses from online audiences.

This study investigates whether a firm that has experienced an environmental accident (EA) is less likely to conduct a product recall. If true, it would indicate that EAs tempt firms to hide operational problems that need to be revealed. The logic is that both events are operational failures that damage a firm's reputation and share price. Following an EA, a firm may avoid a discretionary product recall to avoid providing additional evidence of operational incapability and social irresponsibility and thereby triggering amplified reputational and market penalties.

The dataset is compiled from several public and private sources and includes 4,355 product recalls, 153 EAs and 120 firms from the industries that often recall products, including automotive, pharma, medical device, food and consumer products. The study timeframe is 2002–2013. Empirical models are evaluated using hazard modeling.

Results show that EAs reduce the probability of a product recall by 32%, on average. Effect sizes are larger when accidents are more frequent or more severe and when recalls are less severe. Through post hoc analyses, the study finds support for the proposed mechanism that firms avoid recalls due to reputational concerns, provides evidence that EAs can have a lengthy impact on recall behavior, and shows that firms are more likely to avoid recalls managed by the CPSC and NHTSA than recalls managed by the FDA.

Prior studies in operations management (OM) have not examined the impact of one negative event on another. This study finds that EAs tempt firms to hide operational problems that need to be revealed. While recalling fewer defective products is of concern to consumers and regulators, should EAs influence a broader set of discretionary operational decisions, such as closing/relocating a production facility, outsourcing production or conducting a layoff, study implications increase significantly.

The current body of empirical research regarding the impact of trust in the cybersecurity commitment of institutions on digital payment usage has focused solely on a macro-level analysis, overlooking the intricate dynamics between institutions' cybersecurity commitments and the trust levels of digital payment users. In light of this limitation, this study aims to offer a more comprehensive understanding of this complex relationship.

A case study was conducted on digital payment users in India through the critical realist lens. To gather data, interviews and focus group discussions were conducted with digital payment users from various regions of the country.

The citizen-centric outcomes of the national cybersecurity commitment (performance and responsiveness) are the most prominent and impactful trust indicators. These outcomes play a crucial role in shaping digital payment users' perception and trust in the cybersecurity commitment of public institutions. Individuals' value positions also influence trust judgments, as it is essential to recognize the value tensions that may arise due to security implementation and their congruence with citizens' values.

The findings of this study have significant implications for policymakers. They are potentially an artifact of the security and perception of digital payment users and the cultural uniqueness of digital payment users in India.

The study proposes a holistic understanding of the relationship between institutions' cybersecurity commitments and the trust levels of digital payment users. It offers a qualitative evaluation of how digital payment users perceive and construe efficient information security management implemented by public institutions.