This study presents and empirically tests a simple framework that examines the effects of market liquidity (the ease with which stocks are traded) and funding liquidity (the ease with which market participants can obtain funding) on stock market bubbles. Three key findings emerge from this...

We examine the predictive power of market-based indicators over the positive and negative stock market bubbles via an application of the LPPLS Confidence TM Multi-scale Indicators to the S&P 500 index. We find that the LPPLS framework is able to successfully capture, ex-ante, some of the...

We investigate investor's correlated attention as a determinant of excess stock market comovement. We propose a novel proxy, "co-attention", that measures the correlation in demand for market-wide information across stock markets approximated by the Google Search Volume Index (SVI). Our results...

We consider the problem of ex-ante forecasting conditional correlation patterns using ultra high frequency data. Flexible semiparametric predictors referring to the class of dynamic panel and dynamic factor models are adopted for daily forecasts. The parsimonious set up of our approach allows to...

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how...

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how...

Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the...

Using daily data of the S&P 500 index from 1950 to 2015, we investigate the relation between return and transaction volume in the statistical distribution tails associated with booms and crashes in the US stock market. We use extreme value theory (peaks-over-threshold method) to study the...