information-based model demonstrates that the correlation of beliefs implied by analyst forecasts leads to return correlations … broadly in line with the data, both in levels and across countries - the correlation between predicted and actual is 0.63. Our … findings have implications for market-wide volatility - the model-implied correlations alone can explain 44% of the cross …

We extend range-based volatility estimation to the multivariate case. In particular, we propose a range …-based covariance estimator motivated by a key financial economic consideration, the absence of arbitrage, in addition to statistical …

-of-sample volatility of optimized portfolios from each model. A few factors capture the general covariance structure but adding more …We evaluate the performance of different models for the covariance structure of stock returns, focusing on their use … factors does not improve forecast power. Portfolio optimization helps for risk control, but the different covariance models …

We introduce a new, hybrid measure of stock return tail covariance risk, motivated by the under-diversified portfolio … covariance is measured across the left tail states of the individual stock return distribution, not across those of the market … covariance risk (H-TCR) and expected stock returns, with an annualized premium of 9%, in contrast to the insignificant or …

with the characteristic, but also unpriced risk. We develop a procedure to remove this unpriced risk using covariance …

This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix …

almost all these papers ignore the bias in the estimated standard errors that serial correlation introduce4s. This is … allowing for an arbitrary covariance structure between time periods. We also suggest a third technique, based on randomization …