The benefits of using flight-to-safety (FTS) in volatility forecasting are assessed within a multivariate GARCH framework. In particular, we propose realized semi-covariance between falling equity and rising safe haven returns as a proxy of FTS and we use it to model the conditional distribution...

This paper analyzes conditional threshold effects of stock market volatility on crude oil market volatility. We use the conditional threshold autoregressive (CoTAR) model, a novel extension of TAR from a constant to time-varying threshold. The conditional threshold is specified as an empirical...

We analyze the impact of sentiment and attention variables on volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. Applying a state-of-the-art sentiment classification technique, we investigate the question...

In this paper stock market development as proxied by market capitalisation is examined. The study period is January 2010 to May 2019. The data frequency is monthly. The paper concentrates on the Zimbabwe Stock Market, but briefly walks through Stock Markets in Africa. Examining stock market...

In this paper, we develop new latent risk measures that are designed as a prior synthesis of key forecasting information associated with financial market contagion. These measures are based on the decomposition (using high-frequency financial data) of the quadratic covariation between two assets...

We provide evidence that changes in the equity price and volatility of individual firms (measures that approximate the definition of 'granular shock' given in Gabaix, 2010) are key to improve the predictability of aggregate business cycle fluctuations in a number of countries. Specifically,...

We investigate the relationship between long-term U.S. stock market risks and the macroeconomic environment using a two component GARCH-MIDAS model. Our results show that macroeconomic variables are important determinants of the secular component of stock market volatility. Among the various...

We provide evidence that changes in the equity price and volatility of individual firms (measures that approximate the definition of 'granular shock' given in Gabaix, 2010) are key to improve the predictability of aggregate business cycle fluctuations in a number of countries. Specifically,...

Echo State Neural Networks (ESN) were applied to forecast the realized variance time series of 19 major stock market indices. Symmetric ESN and asymmetric AESN models were constructed and compared with the benchmark realized variance models HAR and AHAR that approximate the long memory of the...