Search results

One of the major goals of sustainable development is creating employment opportunity among all. Despite its largest demographic dividends, the whole world faces challenges in employment generation among youth. The growing number of unemployed youths is one of the important problems faced by developed as well-developing countries. Youth unemployment is the situation of young people who are looking for a job but cannot find a job in the age between 15 and 24. Mismatch between education and employability resulted in high unemployment rates among the youth. A key research question is that how we can bridge the gap and equip the youth for job field. Although eminent economists, newspapers, international statistical bodies continuously put fingers towards this vulnerability, research work in this field is really scant. On this backdrop, this chapter wants to explore the intensity of youth unemployment in India; keeping in mind, India has the largest youth population in the world. Based on data sources from World Development Indicators, the chapter studies the time series since globalisation to COVID periods. This chapter also tries to search the macroeconomic variables related anyway to the youth unemployment rate. As research methodology, we use vector autoregressive (VAR) Granger causality test. Based on our results, the author has concluded that human development index in India and GDP both ways causes each other. And youth unemployment rate in India causes HDI. However, our econometric investigations can be useful to better assessment of youth unemployment in India from liberalisation to pandemic. At the end of this chapter, some final considerations and policy implications on youth labour market dynamics are analysed and discussed.

Given the unique nature of business events tourism, this paper evaluates the forecasting performance of various models using search query data (SQD) to forecast convention attendance.

This research uses monthly and quarterly business event attendance data from both the U.S. (Las Vegas) and China (Macau) markets. Using SQD as the input, we evaluated and compared the cutting-edge forecasting models including Prophet and Long Short-Term Memory (LSTM).

The study reveals that Prophet outperforms complex neural network models in forecasting business event tourism demand. Keywords related to convention facilities, conventions or exhibitions, and transportation are proven to be useful in forecasting business travel demand.

Prophet is an accessible forecasting model for event-tourism practitioners, especially useful in the volatile business event tourism sector. Using verified search keywords in models helps understand traveler motivations and aids event planning.

Our study is among the first to empirically evaluate the performance of forecasting models for business travel demand. In comparison with other mainstream forecasting models, our study extends the scope to examine both the U.S. and Chinese markets.

The volatility of exchange rate has generally been sighted as a primary cause for various shocks and instability in international trade of Ghana as witnessed over the years and most especially in recent times. Hence, owing to the increasing trade levels between Ghana and Ghana's global trading partners, the study aims to investigate if the trade–exchange rate volatility nexus in Ghana supports the positive, negative or ambiguous hypotheses?

The study investigates the effects of Ghana's exchange rate volatility on international trade by designing import and export equations to estimate both short- and long-run specifications of the effect and employing the multivariate generalized autoregressive conditional heteroskedasticity (GARCH) with Baba, Engle, Kraft and Kroner (BEKK) specification developed by Engle and Kroner (1995) as a further check for the robustness of the findings. Monthly data between 1993 and 2017 on the real effective exchange rates of Ghana's trade with 143 trading partners were taken as the series for modeling the volatility using GARCH andexponential generalized autoregressive conditional heteroskedastic (EGARCH) models.

The empirical results show that the volatility of exchange rate negatively impact export performances in the Ghanian economy. On the other hand, there was no sufficient evidence to support the observed positive effect of exchange rate volatility on imports, as the effects were only significant at 10% level in the long run. Thus, it is concluded that the finding cannot confirm a relationship between volatility and import. Thus, the results present differences in the direction of the effect of exchange rate volatility on imports and exports in the context of the Ghanaian economy.

Considering the fragility of the Ghanaian economy and Ghana's macro-economic indicators, the study points at the crucial need for more integration of well-informed trade policies within the country's macro-economic policy framework to contain the impacts of exchange rate volatility on trade performances.

The study contributes to literature by scope and method. More specifically, empirical studies have failed or provided little evidence uniquely on the Ghanaian economy's reaction to exchange rate volatility on the country's imports and exports. Additionally, most of the existing empirical studies measure exchange rate volatility using the standard deviation of the moving averages of the logarithmic transformation of exchange rates. This method is criticized because the method is unsuccessful in capturing the effects of potential booms and bursts of the exchange rate. The authors' study circumvents for these highlighted pitfalls.

The study contributes to literature by scope and method. More specifically, empirical studies have failed or provided little evidence uniquely on the Ghanaian economy's reaction to exchange rate volatility on the country's imports and exports. Thus, the study chat a course for socio-economic dynamic of Ghanaian economy.

The study contributes to literature by its scope and method, as extant empirical studies have provided little evidence specifically on the Ghanaian economy's reaction to exchange rate volatility. Additionally, most of the existing empirical studies measure exchange rate volatility using the standard deviation of the moving averages of the logarithmic transformation of exchange rates. This method is criticized because of the method's inadequacies in capturing the effects of potential booms and bursts of the exchange rate. The study thereby essentially circumvents for these highlighted pitfalls.

This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.

The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.

The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.

These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.

The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.

This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.

We highlight how factors like volatility, futures volume, and open interest vary in their impact.

The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.

We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

This study aims to explore the asymmetric effects of macroeconomic factors on the profitability of large-cap companies in an emerging country like Colombia, using the Morgan Stanley Capital International (MSCI) Colombia index as the basis.

We employ a combination of singular spectrum analysis (SSA) and principal component analysis (PCA) to identify and estimate four key macroeconomic factors that account for approximately 47.8% of Colombia's macroeconomy. These factors encompass indicators related to inflation and cost of living, foreign trade and exchange rate, employment and labor force and trade and production in Colombia. We utilize the distributed lag nonlinear model (DLNM) to analyze the asymmetric relationships between these factors and corporate profitability, considering different scenarios and lags.

Our analysis reveals that there are indeed asymmetric relationships between the identified macroeconomic factors and corporate profitability. These relationships exhibit variability over time and lags, indicating the nuanced nature of their impact on corporate performance.

This study contributes to the existing literature by applying a novel methodology that combines SSA and PCA to identify macroeconomic factors within the Colombian context. Additionally, our focus on asymmetric relationships and their dynamic nature in relation to corporate profitability, using DLNM, adds original insights to the research on this subject.

This study analyses the impact of the Covid-19 on stock market performance of BRICS nations together. BRICS countries comprise almost 30% of the global GDP and around 50% of the world’s economic growth. As BRICS nations have gained the attraction as financial investment destinations, their financial markets have apparently been as potential opportunities for foreign portfolio investors. While there is extensive research on the impact of the Covid-19 pandemic on individual economies and global financial markets, this paper is among the first to systematically investigate the dynamic connectedness of these emerging economies during the pandemic using the Time-Varying Parameter Vector Autoregressions (TVP-VAR) approach.

We categorise our data into two distinct periods: the pre-Covid period spanning from January 1, 2018, to March 10, 2020, and the Covid crisis period extending from March 11, 2020, to June 4, 2021. To achieve our research objectives, we employ the Time-Varying Parameter Vector Autoregressions (TVP-VAR) approach to assess dynamic connectedness.

Our findings reveal that among the BRICS nations, Brazil and South Africa serve as net transmitters of shocks, while China and India act as net receivers of shocks during the Covid crisis. However, the total connectedness index (TCI) has exhibited a notable increase throughout this crisis period. This paper makes several notable contributions to the academic literature by offering a unique focus on BRICS economies during the Covid-19 pandemic, providing practical insights for stakeholders, emphasising the importance of risk management and investment strategy, exploring diversification implications and introducing advanced methodology for analysing interconnected financial markets.

The results have important implications for the investors, the hedge funds, portfolio managers and the policymakers in BRICS stock markets. The investors, investment houses, portfolio managers and policymakers can develop investment strategies and policies in the light of the findings of this study to cope up the future pandemic crisis.

This study is one of its kind that examines the dynamic connectedness of BRICS with recently developed TVP-VAR approach across pandemic crisis.

This study aims to assess volatility spillovers and directional connectedness between electricity (EPs) and natural gas prices (GPs) in the Canadian electricity market, based on a hydrothermal power generation market strongly dependent on exogenous variables such as fossil fuel prices and climatology factors.

The methodology is divided into two stages. First, a quantile vector autoregression model is used to evaluate the direction and magnitude of the influence between natural gas and electricity prices through different quantiles of their distributions. Second, a cross-quantilogram is estimated to measure the directional predictability between these prices. The data set consists of daily electricity and natural gas prices between January 2015 and December 2023.

The main finding shows that electricity prices are pure shock receivers of volatility from natural gas prices for the different quantiles. In this way, natural gas price fluctuations explain 0.20%, 0.98% and 22.72% of electricity price volatility for the 10th, 50th and 90th quantiles, respectively. On the other hand, a significant and positive correlation is observed in the high quantiles of the electricity prices for any natural gas price value.

The study described the risk to the electricity market caused by nonrenewable source price fluctuations and provided evidence for designing regulatory policies to reduce its exposure in Alberta, Canada. It also allows us to understand the importance of natural gas in the energy transition process and define it as the fundamental determinant of the electricity market dynamic.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

The purpose of this study is to investigate the interplay between fiscal dominance and monetary policy in South Africa from 1960 to 2023.

The study employs a structural vector autoregression (SVAR) medel to analyze the relationship between fiscal dominance and monetary policy. Short-term and long-term shocks of government borrowing and deficits are examined to understand their impact on inflation dynamics.

Fiscal dominance has a significant effect both in the short and long run. There is evidence that government debt and deficits increase inflation, overriding the effects of monetary policy aimed at maintaining price stability. On the other hand, the study reveals that money supply shocks have a greater effect in reducing fiscal dominance compared to interest rate shocks. The variance movement on inflation is significantly explained by government debt and deficits. This emphasizes the persistence of inflationary pressures associated with fiscal dominance, highlighting the importance of effective policy interventions to mitigate inflationary risks.

This study contributes to the existing literature by providing insights into the dynamics of fiscal dominance in South Africa. Moreover, this study extends the theoretical framework of the fiscal theory of the price level (FTPL) and the government budget constraint. This study contributes valuable insights into the dynamics of fiscal dominance in South Africa and offers guidance for policymakers in formulating strategies to safeguard economic stability.