This study quantifies the presence of financial distress in the cross section of stock returns. Systemic risk is defined as the occurrence of simultaneous tail events for a large fraction of firms. A tail event is interpreted as evidence of downside risk in the tail distribution of financial...

We analyze link between mortgage-related regulatory penalties levied on banks and the level of systemic risk in the U.S. banking industry. We employ a frequency decomposition of volatility spillovers (connectedness) to assess system-wide risk transmission with short-, medium-, and long-term...

We analyze link between mortgage-related regulatory penalties levied on banks and the level of systemic risk in the U.S. banking industry. We employ a frequency decomposition of volatility spillovers to draw conclusions about system-wide risk transmission with short-, medium-, and long-term...

This paper examines the effect of foreign exchange news announcements on the volatility of stock returns in Nigeria, using the daily closing All-Share Index from The Nigerian Stock Exchange from 2000 to 2015. We extended existing literature by augmenting the EGARCH econometric model with...

This study examines the impact of volatility shifts on volatility persistence for three major sector indices of Istanbul Stock Exchange (ISE) and ISE National 100 index over the period beginning from 1997 and ending in 2009. The exponential generalized autoregressive conditional...

The energy sector is one of the most important in the world, so that time series fluctuations in leading energy sources have been analysed widely. As the leading energy commodities are traded on international stock exchanges, the analysis of the fluctuations in stock and financial derivatives...

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual and non-linear relationships,...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

This paper provides robustness checks and analytical derivations to supplement the material presented in the paper Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory.The paper to which these Appendices apply is available at the following URL:...

We document that the first and third cross-sectional moments of the distribution of GDP growth rates made by professional forecasters can predict equity excess returns, a finding which is robust to controlling for a large set of well established predictive factors. We show that introducing...