Search results

This study seeks to build a financial uncertainty index for Saudi Arabia. This index serves as a leading indicator of Saudi economic activity and helps to describe economic fluctuations and forecast economic trends.

This study adopts an extension of the Jurado et al. (2015) procedure by combining financial uncertainty factors with their net spillover effects on GDP and inflation to construct an aggregate financial uncertainty index. The authors consider 13 monthly financial variables for Saudi Arabia from January 2010 to June 2021.

The empirical results show that the constructed financial uncertainty estimates are good leading indicators of economic activity. The robustness analysis suggests that the authors’ proposed financial uncertainty estimators outperform the alternative estimates used by other existing approaches to estimate the financial conditions index.

To the best of the authors’ knowledge, this is the first attempt at constructing a financial uncertainty index for Saudi Arabia. This study extends the empirical literature, from which the authors propose a novel conceptual framework for building a financial uncertainty index by combining the approach of Jurado et al. (2015) and the time-varying connectedness network approach proposed by Antonakakis et al. (2020)

This paper aims to assess the asymmetric impact of corruption on financial development in BRICS economies context.

The authors have adopted the novel panel non-linear autoregressive distributed lag (PNARDL) model of Shin et al. (2014), covering the period 1991–2018.

The findings confirm that corruption asymmetrically impacts financial development in BRICS economies. More precisely, long-run negative shocks of the control of corruption index have significant negative impacts on financial development. However, long-run positive shocks of the control of corruption index are insignificant. Moreover, both positive and negative shocks of corruption in short-run results are insignificant. Generally, the findings are robust having carried out several robustness checks and in favor of “sand in the wheels” hypothesis.

This study makes a novel contribution by developing insight on how corruption asymmetrically impacts financial development. To the best of the authors’ knowledge, this is the first attempt to use the PNARDL, which decompose the main independent variable (corruption) into positive and negative shocks. The PNARDL approach is a dynamic robust estimate that controls for the problem of endogeneity, which is a common phenomenon in such studies. Additionally, it is believed that the findings are important for policy makers, scholars and practitioners. Finally, the authors used the most recent available dataset covering the BRICS context.