We investigate the impact of China's economic policy uncertainty (EPU) on the time series variation of Chinese stock market expected returns. Using the news based measure in Baker, Bloom, and Davis (2016), we find that EPU predicts negatively future stock market return at various horizons. This...

The CCC-GARCH model, and its dynamic correlation extensions, form the most important model class for multivariate asset returns. For multivariate density and portfolio risk forecasting, a drawback of these models is the underlying assumption of Gaussianity. This paper considers the so-called...

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock-bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of...

Uncertainty may affect economic behavior of individuals and firms in a wide variety of ways, with typically negative consequences for economic growth. It is due to this fact, combined with rising political uncertainty observed lately in many countries, that uncertainty has gained increasing...

We investigate various statistical methods for forecasting risky choices and identify important decision predictors. Subjects (n=44) are presented a series of 50/50 gambles that each involves a potential gain and a potential loss, and subjects can choose to either accept or reject a displayed...

We assess the contribution of macroeconomic uncertainty -- approximated by the dispersion of the real GDP survey forecasts -- to the ex post and ex ante prediction of stock price bubbles. For a panel of six OECD economies covering 24 years, two alternative binary chronologies of bubble periods...

We assess the contribution of macroeconomic uncertainty - approximated by the dispersion of the real GDP survey forecasts - to the ex post and ex ante prediction of stock price bubbles. For a panel of six OECD economies covering 24 years, two alternative binary chronologies of bubble periods are...

Risk estimation or volatility estimation at financial markets, particularly stock exchange markets, is complex issue of great importance to theorists and practitioners. Models used to estimate volatility forecasts are translated into better pricing of stocks and better risk management. The aim...

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory...