The impact of interest rate volatility on financial market inclusion: evidence from emerging markets

Hajilee, Massomeh; Niroomand, Farhang

doi:10.1007/s12197-017-9422-x

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…We employ a maximum of four lags on each first differenced variable and select the optimum number of lags by Akaike's Information Criterion (AIC; Bahmani‐Oskooee and Hegerty, 2009). All short‐run effects are presented in the estimation of first differenced variables coefficients, and the long‐run effect is presented in the estimation of λ 2 − λ 2 which is being normalised on

λ_{1}

(Hajilee and Niroomand, 2018b). Meanwhile, the standard errors of these normalised coefficients are calculated to show the level of normalised coefficient's significance.…”

Section: Model and Estimation Methodsmentioning

confidence: 99%

The relationship between interest rate volatility and the shadow economy in OECD countries: An asymmetric analysis

Hajilee

Niroomand

Hayes

2023

Australian Economic Papers

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Interest rate changes by central banks are a strong monetary policy tool that has a significant impact on the performance of the real economy via various channels. Despite extensive theoretical and empirical studies in this area, the current literature lacks a comprehensive assessment of the relationship between interest rate volatility and the shadow economy. This study explores the link between interest rate volatility and the shadow economy for 38 Organisation for Economic Co‐operation and Development (OECD) member countries over the period 1991–2021 using both linear and non‐linear ARDL models. The use of the non‐linear ARDL specification will allow for the possibility of an asymmetric effect of interest rate volatility on the shadow economy. In addition to the examination of the potential asymmetric effects, we also discuss the ramifications for policymakers with respect to monetary and financial policies while considering each country's specific economic structure.

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λ_{1}

(Hajilee and Niroomand, 2018b). Meanwhile, the standard errors of these normalised coefficients are calculated to show the level of normalised coefficient's significance.…”

Section: Model and Estimation Methodsmentioning

confidence: 99%

The relationship between interest rate volatility and the shadow economy in OECD countries: An asymmetric analysis

Hajilee

Niroomand

Hayes

2023

Australian Economic Papers

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“…They also showed that real interest rate volatility has similar deleterious effect on finance‐growth nexus. Hajilee and Niroomand (2018) also reported that interest rate uncertainty has adverse effect on financial market performance and economic growth.…”

Section: Macroeconomic Stability and Finance‐growth Nexusmentioning

confidence: 99%

What matters for finance‐growth nexus? A critical survey of macroeconomic stability, institutions, financial and economic development

Ehigiamusoe

Samsurijan

2020

Int. J Fin Econ

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This paper analyses the variables that moderate the impact of financial development on economic growth based on theoretical and empirical evidences. We show that financial and economic development, institutions and macroeconomic stability are the fundamental variables that moderate the finance‐growth nexus. Specifically, higher levels of financial and economic development, institutional quality and macroeconomic stability promote the impact of financial development on economic growth, while lower levels of these variables could have the opposite effect. We also show that too much finance is deleterious to economic growth, suggesting the existence of a threshold level of financial development beyond which further finance inhibits, rather than enhances growth. The economic implication of this study is that a stable macroeconomic environment, higher level of institutional quality, optimum financial and economic development are necessary conditions for finance to accelerate growth. Hence, countries that want to promote economic growth through the financial sector should give adequate priority to these variables.

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“…Commonly, the higher the volatility, the riskier the security. Volatility is influenced by many factors like liquidity, interest rate [5], real estate [6], opinions [7], and a firm's share. The authors in [8] explain how liquidity providers of a market impact the volatility and stock returns, while the study in the paper [6] re-examine the relationship between a firm's market share and volatility.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data

Tigani

Chaibi

Saadane

2019

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This paper carried out a hybrid clustering model for foreign exchange market volatility clustering. The proposed model is built using a Gaussian Mixture Model and the inference is done using an Expectation Maximization algorithm. A mono-dimensional kernel density estimator is used in order to build a probability density based on all historical observations. That allows us to evaluate the behavior’s probability of each symbol of interest. The computation result shows that the approach is able to pinpoint risky and safe hours to trade a given currency pair.

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The impact of interest rate volatility on financial market inclusion: evidence from emerging markets

Cited by 7 publications

References 25 publications

The relationship between interest rate volatility and the shadow economy in OECD countries: An asymmetric analysis

The relationship between interest rate volatility and the shadow economy in OECD countries: An asymmetric analysis

What matters for finance‐growth nexus? A critical survey of macroeconomic stability, institutions, financial and economic development

Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data

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