Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence
By Tim Bollerslev, J. Marrone, Lai Xu, Hao Zhou. Journal of Financial and Quantitative Analysis, 2011
300 citations recorded by the source corpus.
Abstract Recent empirical evidence suggests that the variance risk premium predicts aggregate stock market returns. We demonstrate that statistical finite sample biases cannot “explain” this apparent predictability. Further corroborating the existing evidence of the United States, we show that country-specific regressions for France, Germany, Japan, Switzerland, the Netherlands, Belgium, and the United Kingdom result in quite similar patterns. Defining a “global” variance risk premium, we uncover even stronger predictability and almost identical cross-country patterns through the use of panel regressions.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/a134862c9ab1a1d8f5a60a5248d0e5ea42242cc4
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Citations in the corpus
- Stock return predictability from a mixed model perspective (2020, methodology): “9 , 2 , 1 , 1 , 1, i M i M i M i M r x (3)”
- Tail risk premia and return predictability (2014, methodology): “Geman, Madan, and Yor (2003), along with the nonparametric estimation procedure employed in Bollerslev and Todorov (2011b). 23 This approach is exemplified by the jump-diffusion models estimated in Pan (2002) and Eraker (2004).”
- DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “Although opinions differ regarding the efficiency of markets, many widely accepted studies show that financial markets are to some extent predictable [13]–[16].”