We propose the use of a risk measure built on flight-to-safety (FTS) episodes into a volatility forecasting model. We assign to each day in the sample a probability of being a FTS day after observing (ab)normal movements in the US equity, US bond and gold markets. By allowing each FTS day to be...

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...

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...

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...

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 propose a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional...

Testing for constant expected returns and forecasting future returns necessitate the information beyond a single predictor. We consider the predictive regression model with multiple predictors which are potentially strongly persistent and cointegrated. Instrumental variables based tests for...