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...

Liquidity is extremely important not only within the context of financial markets but also in every scale of economic transactions. In this study, within the realm of financial markets, we configure liquidity as an independent stochastic process moderating the fluidity of all transactions and...

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...

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...

In parallel to the increased complexity and depth of the financial markets, microstructures of the markets have become an attracting field. Price impact stands out as one of most important aspects of market microstructure as it is closely related price discovery process. Understanding price...

Various empirical studies highlight the importance of productivity and institutions along with the traditional economic growth determinants in the growth process. Comparative role of productivity and institutions in convergence, however, remains ambiguous. This paper aims to find out the...