Time-Varying Connectedness Between Global Uncertainties and Economic Activity in a Developing Economy Using a Dynamic Conditional Correlation — GARCH Model

Coupling between global climate policy uncertainty and economic policy uncertainty

Morocco's green shield: ESG stock performance under global climate, economic, geopolitical and oil uncertainties

The world is currently facing unprecedented uncertainty in social, political, and economic aspects, which continuously evolve every year owing to extraordinary events that impact countries worldwide. In contrast, The growth strategy of a country organizes must include Foreign Direct Investment (FDI), especially in Indonesia. The fluctuation in foreign investment in Indonesia indicates that foreign investors also consider growing global uncertainty alongside the development of Indonesia’s financial sector. This study examines the relationship between global uncertainty and financial development on FDI in Indonesia from 1997 to 2020 using the Autoregressive Distributed Lag (ARDL) method. The results indicate that global uncertainty significantly negatively impacts Indonesia’s FDI in the short and long terms. However, global economic policy uncertainty positively influences Indonesia’s FDI in the long term. This indicates that Indonesia has become an attractive alternative for foreign investors during global economic policy uncertainty. Regarding financial development, the amount of credit provided by the domestic banking sector to the private sector in Indonesia positively influences Indonesia’s FDI in both the short and long terms. This demonstrates that Indonesia’s financial condition and infrastructure can determine foreign investors’ decisions to invest in Indonesia.

Purpose This paper aims to study the transmission of shocks from global macroeconomic variables to international trade volumes of an emerging market economy. Design/methodology/approach This paper estimates the return and volatility spillovers from global factors such as crude price, global economic activity and global policy uncertainty to India’s imports and exports. This paper also includes the effects of real exchange rate, credit availability, domestic output gap and domestic policy uncertainty. The paper uses wavelet time-scale decomposition analysis to extract the spillovers received by imports and exports at various frequencies, which are otherwise invisible in the raw data. The conditional volatility of variables is extracted using the ARMA-GARCH model. Findings The results show that India’s imports and exports receive return spillovers from real exchange rate, credit availability, crude price and global economic policy uncertainty at lower frequencies. India’s exports receive volatility spillovers from global economic activity at higher frequencies. Research limitations/implications Because different factors impact trade cycles in the short term and long term, understanding the spillovers at different frequencies is essential for policymakers, businesses and investors to prepare for potential economic fluctuations. Practical implications This paper observes spillovers from REER to trade at lower frequency cycles, which calls for better foreign exchange hedging options in the medium to long term. Social implications Enhancing economic partnerships, reducing trade barriers and attracting foreign capital to foster local production could help manage trade spillovers. Originality/value While existing studies used the wavelet method to estimate spillovers in economic and financial variables, to the best of the authors’ knowledge, there is no study using this method to examine spillover from global and domestic macroeconomic variables to international trade. This paper fills this gap.

A global economic policy uncertainty index from principal component analysis

The impact of economic policy uncertainties on the volatility of European carbon market

This paper explores the effects of global economic policy uncertainty (GEPU) on conditional volatility in the gold futures market using Markov regime‐switching GARCH‐MIDAS models. The in‐sample empirical results suggest that GEPU indeed contains predictive information for the gold futures market, and higher GEPU leads to higher volatility within the gold futures market. Moreover, the novel model, which adds Markov regime switching with time‐varying transition probabilities and the GEPU index, achieves relatively better performance than those of the other competing models from a statistical point of view. Furthermore, we discuss the asymmetric effects of different changes in GEPU on the gold futures market and the models' performances with different horizons, and we find that our new model has better predictive performance under negative changes in GEPU than under positive changes in GEPU. Further discussion also confirms that our previous findings are robust during two special cases, the global financial crisis and European debt crisis, during which the market suffered from fierce fluctuations and was fraught with considerable uncertainty.

Global uncertainties and portfolio flow dynamics of the BRICS countries

Examining the links between economic uncertainty and precious metals allows us to explore the interconnectedness of various economic factors and their potential impact on markets, investments, and global trade. Precious metals such as gold and silver have historically been used as safe havens during times of economic uncertainty. Understanding how these metals interact with fluctuating economic policies can help assess their role as a safe haven and their importance to investors. Analyzing the links between precious metals, economic uncertainty, and agricultural prices can help policymakers design measures to mitigate negative impacts on the economy and foster financial and food stability. The main objective of this paper is to examine the co-movements between global economic policy uncertainty and each commodity such as oil, precious metals and agricultural prices. We employ two empirical approaches such as wavelet coherence and bootstrap rolling window Granger causality. The dynamic causality according to the bivariate framework between variables is analyzed. We used monthly data during the period span from January 2004 to September 2022. Empirical results indicate the evidence of unidirectional, bidirectional and absence of causality between variables. In addition the co-movements between GEPU and each variable are positive and negative.

Can geopolitical risks excite Germany economic policy uncertainty: Rethinking in the context of the Russia-Ukraine conflict

The aim of this research is to investigate the causality between Global Economic Political Uncertainty (GEPU) and Geopolitical Risk (GPRT) and Bitcoin Energy Consumption (BTCE). In order to test the stationarity of the variables, the Lee-Strazich unit root test, which takes into account the structural breaks, was used, and the causality relationship between the variables was analyzed with the Hatemi-J (2012) causality test. Monthly data between May 2011 and February 2022 were used in the research. According to the results obtained from the research, geopolitical risk and global economic policy uncertainity are effective on bitcoin energy consumption. In addition, it has been determined that the negative effects of geopolitical risk and global uncertainties are more dominant. The results show that the demand for bitcoin, which is considered an alternative financial asset class, and accordingly bitcoin energy consumption, increases in case of global risks and economic uncertainties.

This paper collects a total of 541 groups of the RMB exchange rate to build the model of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) for forecasting of RMB exchange rate against US dollar. The empirical results show that the GARCH models can fit the series well and the prediction effects of both the GARCH (1, 1) model and the GARCH-M model are good.

Applying cointegration analysis on the monthly data of Singapore, this research note examines the impacts of global economic policy uncertainty (GEPU) and domestic economic policy uncertainty (DEPU) on the hotel room demand. Although control variables, such as average room rates, industrial production index and international tourist arrivals, are used and the specific-to-general modelling is adopted, the results show that the standard diagnostic tests may be incapable of identifying possible model mis-specification. The main findings show that DEPU has a negative impact, but GEPU has a positive impact on the hotel room demand. DEPU is linked to the political instability of Singapore, which discourages international travellers to visit Singapore. GEPU enhances Singapore’s position as a stable business and financial hub in relation to others and thereby increases business travel.

Volatility plays an important role in financial markets and has held the attention of academics and practitioners. Using Generalized autoregressive conditional heteroscedasticity (GARCH) is one of the effective perspective for forecasting volatility. This study focused on comparing GARCH (P, Q) model with GJR-GARCH (P, Q) model and EGARCH (P, Q) model to make prediction more reliable and accurate. The results suggested that both GARCH (P, Q) model and GJR-GARCH (P, Q) model are good choices for forecasting volatility in financial market, especially for describing heteroscedastic time series. GARCH models are consistent with various forms of efficient market theory. These theories state that asset returns observed in the past cannot improve the forecasts of asset returns in the future.

GARCH (Generalized Autoregressive Conditional Heteroscedasticity) models are new statistical methods that are used especially in economic time series. There is a consensus that speech signals exhibit variance that changes through time. GARCH models are a popular choice to model these changing variances. In this chapter, we propose three methods for VAD, which are based on GARCH models. In the first method, heteroscedasticity will be modeled by GARCH process and hard detection is the result of comparing a Multiple Observation Likelihood Ratio Test (MOLRT) with a threshold function. In the second method, no distinct probability functions are assumed for speech and noise distributions and no LRT is employed. We will show that VAD is related to the parameter constancy test in GARCH process. For testing parameter constancy in GARCH models, the algorithm of the Cramer-von Mises (CVM) test is described. In the last method, the process of outlier detection in GARCH model is presented and the motivation for using it and its relation with VAD are discussed.