This study aims first at improving volatility prediction using a machine learning model called support vector regression GARCH (SVR- GARCH) using selected 30 stocks listed on the S&P 500. The authors compare the prediction results of the SVR-GARCH model with the GARCH family models and find that...

A divergence measure is a tool that applied to many fields. In particular, with the advancement of data-dependent modeling, it becomes one of the main tools in distributional analysis. In the financial context, it is applicable to see whether there is a shift in the estimated default...

Especially after the recent financial crisis that started in mortgage markets and spread all over international markets, in order to better monitor financial risks, the importance and use of stress testing has increased. In this study, top-down macro stress testing from a supervisory perspective...

This study presents evidence about relations between national cultural dimensions, socioeconomic development and governance quality. Relations between Hofstede's dimensions, Schwartz cultural values, Worldwide Governance Indicators (WGI) and UNPD Human Development Index (HDI) were analysed by...

In the narrow sense, financial stability is defined as price stability and the soundness of financial institutions. Although this definition can be extended to cover the functioning of financial markets, asset price volatility, risk management practices of institutions, etc., financial soundness...

Volatility in the financial markets is commonplace and it comes with a cost. One of these costs is abrupt and huge drop in stock price that is known as stock price crash. To model this, we propose a new machine-learning based stock crash risk measure using minimum covariance determinant (MCD) to...