A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach

Financial market interdependencies: A quantile regression analysis of volatility spillover

“…To achieve our objective, we adopt a more appropriate methodology which is generally characterized by its stability and is suitable for non-standard shaped distributions and by a non linear behavior, contrary to the conventional least squares which, in our view, has not been yet used in this context. It's the Quantile Regression (QR) model, which has been previously used in the financial literature to study the value-at-risk (Engle and Manganelli, 2004;Rubia and Sanchis-Marco, 2013), the systemic risk (Adrian and Brunnermeier, 2011) the prediction of failure (Li and Miu, 2010) and also the modeling of dependence between financial variables (Bassett and Chen, 2001;Chuang et al, 2009;Baur et al, 2012;. Lee and Li, 2012;Tsai, 2012;Ciner et al, 2013;Gebka and Wohar, 2013).…”

A quantile regression approach to bank efficiency measurement*

“…This analysis is particularly useful when the conditional distribution does not have a standard shape, such as an asymmetric, fat-tailed, or truncated distribution. Consequently, quantile regression was recently employed in various strands of the finance and banking literature, including banking risk and regulations (Klomp and de Haan, 2012), the herding behavior in stock markets (Chiang et al, 2012), capital structure (Fattouh et al, 2005), bankruptcy prediction (Li and Miu, 2010), ownership and profitability (Li et al, 2009), the relationship between stock price index and exchange rate (Tsai, 2012), and credit risk (Schechtman and Gaglianone, 2012). 1 In the context of our study, quantile analysis provides an ideal tool to examine bank efficiency heterogeneity, departing from conditional-mean models.…”

Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis

“…Hwang and Chu [34] propose a new procedure to estimate the loss given default using logistic regression. Li and Miu [35] establish a prediction model with dynamic loading on accounting ratio-based and market-based information using a regression approach.…”