Author:
Silva João,Febra Lígia,Costa Magali
Abstract
Purpose
This study aims to advance knowledge on the direct impact of the investor’s protection level on the stock market volatility, that is, whether investor’s protection is an important stock market volatility determinant.
Design/methodology/approach
A panel data was estimated using a sample of 48 countries, from 2006 to 2018, totalizing 31,808 observations. To measure stock market volatility and the investor protection level, a generalized autoregressive conditional heteroskedasticity model and the World Bank Doing Business investor protection index were used, respectively.
Findings
The results evidence that the protection of investors’ rights reduces the stock market volatility. This result indicates that a high level of investor protection, which is the result of a better quality of laws and policies in place that protect investor’s rights, promotes the country as a “safe haven.”
Practical implications
The relationship that the authors intend to analyze becomes important, given that investor protection will give outsiders guarantees on the materialization of their investments. This study contributes important knowledge for investors and for the establishment of government policies as a way of attracting investment.
Originality/value
Although there have been a few studies addressing this relationship, to the knowledge, none of them directly analyses the influence of investor protection on the stock market volatility.
Subject
General Economics, Econometrics and Finance,Finance,Accounting
Reference65 articles.
1. Quality of governance and stock market performance: the Nigerian experience;Journal of Economics and Development Studies,2014
2. Illiquidity and stock returns: cross-section and time-series effects;Journal of Financial Markets,2002
3. Risk, time-varying second moments and market efficiency;The Review of Economic Studies,1991
4. Stock market volatility and economic growth in Nigeria (1980-2010);International Review of Management and Business Research,2013
5. Clustering financial time series with variance ratio statistics;Quantitative Finance,2012