Unusual behavior: Reversed Leverage Effect Bias

Document Type : Journal of Mathematics and Modeling in Finance (JMMF)


1 Postdoc of Finance, Faculty of Economics, University of Tehran, Tehran, Iran

2 Ph.D. in Financial Management, Faculty of Management, University of Tehran, Tehran, Iran

3 Postdoc of Finance, Hankuk University of Foreign Studies, Seoul, South Korea



According to the literature on risk, bad news induces higher volatility than good news. Although parametric procedures used for conditional variance modeling are associated with model risk, this may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. For inferring non-linear financial time series, various parametric and non-parametric models are generally used. Since the leverage effect refers to the generally negative correlation between an asset return and its volatility, models such as GJRGARCH and EGARCH have been designed to model leverage effects. However, in some cases, like the Tehran Stock Exchange, the results are different in comparison with some famous stock exchanges such as the S&P500 index of the New York Stock Exchange and the DAX30 index of the Frankfurt Stock Exchange. The purpose of this study is to show this difference and introduce and model the "reversed leverage effect bias" in the indices and stocks in the Tehran Stock Exchange.