The VPIN, or Volume-synchronized Probability of INformed trading, metric is introduced by Easley, Lopez de Prado and O'Hara (ELO) as a real-time indicator of order flow toxicity. They find the measure useful in predicting return volatility and conclude it may help signal impending market...

In Andersen and Bondarenko (2014), using tick data for S&P 500 futures, we establish that the VPIN metric of Easley, Lopez de Prado, and O'Hara (ELO), by construction, will be correlated with trading volume and return volatility (innovations). Whether VPIN is more strongly correlated with volume...

Easley, Lopez de Prado and O'Hara introduce VPIN as a real-time indicator of order flow toxicity. They find it useful for monitoring order fl ow imbalances and signaling impending market turmoil, exemplified by the ash crash. They also deem VPIN a good forecaster of short-term volatility. In...

We study the evolution of the behavioral component of the financial market by estimating a Bayesian mixture model in which two types of investors coexist: one rational, with standard subjective expected utility theory (SEUT) preferences, and one behavioral, endowed with an S-shaped utility...

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the...

Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models,...

Yes. We use intraday data to compute weekly realized variance, skewness and kurtosis for individual equities and assess whether this week?s realized moments predict next week?s stock returns in the cross-section. We sort stocks each week according to their past realized moments, form decile...

This chapter surveys the methods available for extracting forward-looking information from option prices. We consider volatility, skewness, kurtosis, and density forecasting. More generally, we discuss how any forecasting object which is a twice differentiable function of the future realization...

We introduce a multivariate GARCH model that utilizes and models realized measures of volatility and covolatility. The realized measures extract information contained in high-frequency data that is particularly beneficial during periods with variation in volatility and covolatility. Applying the...

Out-of-sample tests of forecast performance depend on how a given data set is split into estimation and evaluation periods, yet no guidance exists on how to choose the split point. Empirical forecast evaluation results can therefore be difficult to interpret, particularly when several values of...