For many assets, trading is fragmented across multiple exchanges. Price discovery measures summarize the informativeness of trading on each venue for discovering the assetś true underlying value. We explore intraday variation in price discovery using a structural model with time-varying...

To investigate how economies, financial markets or institutions can deal with stress, we nowadays often analyze the effects of shocks conditional on a recession or a bear market. MSVAR models are ideally suited for such analyses because they combine gradual movement with sudden switches. In this...

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week...

We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative...

This paper analyzes the empirical performance of two alternative ways in which multi-factor models with time-varying risk exposures and premia may be estimated. The first method echoes the seminal two-pass approach advocated by Fama and MacBeth (1973). The second approach is based on a Bayesian...

This paper investigates how investor sentiment affects stock market returns and evaluates the predictability power of sentiment indices on U.S. and EU stock market returns. As regards the American example, evidence shows that investor sentiment indices have an economic and statistical...