quantile regression

Mutual funds are one of the most paramount investment mechanisms in financial markets. By playing a financial intermediary role, they give nonprofessionals access to professionally managed portfolios of securities and provide numerous benefits for both the capital market and investors simultaneously. This study evaluated and investigated the systemic risk of mutual funds in the Iran capital market by adopting a Conditional Value at Risk (CoVaR) approach and employing quantile regression. In the finance literature, systemic risk is the probability of a downfall in the financial system when a segment or an individual component gets in distress. This risk can trigger instability or shock in financial markets and the real part of the economy. The results revealed that stock (equity) mutual funds were systemically more important than other funds, including fixed-income and balanced mutual funds, due to the high volatility in their return, which makes them riskier. To compare systemic risk and market risk among mutual funds, funds classified into five different groups based on their systemic risk. According to this categorization, analysis of variance illuminated that the market risk of mutual funds had a direct relationship with their systemic risk, such that a higher systemic risk of a fund stood for higher market risk.

Financial statements of nineteen mature banks have been patronized to examine the impact of macroeconomic indicators and bank-specific determinants on the NPLs ratio through Quantile and Panel Data regression approaches. The impact of macroeconomic indicators on credit risk is statistically estimated for banking network via two directions. First, different quantiles are econometrically calculated, assessed and compared during 2007-12. Second, the Panel Data estimation is utilized in the same way to verify the outcomes of quanitle regression and to check the robustness. Results indicate that the impact of real money supply on the banks’ NPLs in 25%, 50%, and 75% of data is positive and significant in line with empirical evidence. The coefficients of the other variables (including the ratio of individual banks’ performing loans to total deposits, individual banks’ performing loans to total loans ratio, as well as GDP would be positively significant as well. The real interest rate has negatively-significantly driven NPLs. The banks’ NPLs are generally exacerbated by the impact of higher real money supply over the long run, real interest rate in the money market and upper return in the assets market mainly because of the negative-inflationary transmission effect. JEL Classifications: C16, C23, E51, G21

Shadow banking is a term that came out of the financial crisis of 2007-2009. There is a belief that shadow banking was one of the crisis reasons. Because the excessive expansion of shadow banking endangers the financial stability of countries, this paper examines the impact of shadow banking on financial stability using data from 14 countries of the G20 during 2002-2018. We divided countries into four groups according to the level of shadow banking activity; then, we employed the quantile regression method. The results indicated that shadow banking hurts financial stability (positive impact on financial instability) in countries with a high shadow banking index (fourth group countries). One unit of increase in the shadow banking index increases financial instability in the fourth group countries (high shadow banking) by 1.6 units. But in countries where shadow banking is not very strong (other three groups), shadow banking does not significantly affect financial stability.

The efficient use of electricity in the household sector to ensure maximum welfare of households and supply of electricity required by industry as an engine of economic growth is the important goal of countries. Therefore, reducing the inefficiency of energy consumption by households is of high importance. The present study uses statistical evidence of expenditure-income of Iranian households for the period 2010–2021 to estimate the share of energy inefficiency in the households’ energy consumption differences. The results of Blinder–Oaxaca decomposition show that the share of inefficiency in creating a gap in the share of household electricity costs has decreased from 87.2% in 2010 to 76.5% in 2021. The results of Machado-Mata decomposition show that in the upper quantiles of the share of electricity consumption, the share of the difference in the socio-economic characteristics of households is more than that of the lower quantiles and this share has increased in 2021 as compared to 2010. Therefore, the role of household consumption pattern is more than the rate of access to high-energy appliances, so providing a step-by-step pricing system with an exponential rate for electricity consumption is an effective policy to reduce inefficiency in electricity consumption. Furthermore, quantile regression estimation shows that household income and size have a negative effect, and ownership and size of housing and access to household appliances have a positive significant effect on the share of household electricity costs.