Average Correlation and Stock Market Returns

Pollet, Joshua Matthew; Wilson, Mungo Ivor

doi:10.2139/ssrn.965354

Cited by 61 publications

(80 citation statements)

References 39 publications

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“…Consequently, we document an increase in the informational content of such strategies when combined with the IV-sentiment strategy, especially for cross-sectional momentum. In line with this outcome, we also report that returns from a IVof measures: Ang and Liu (2007) for realized variance, Bliss and Panigirtzoglou (2004) for risk-aversion implied by risk-neutral probability distribution function embedded in cross-sections of options, Bollerslev et al (2009) for variance risk premium, Driessen et al (2013) for option-implied correlations, Pollet and Wilson (2008) for historical correlations, and Vilkov and Xiao (2013) for the risk-neutral tail loss measure. Most of these studies document a short-term negative relation between risk measures and equity market movements.…”

Section: Introductionsupporting

confidence: 73%

“…Our results, thus, offer additional findings to the literature that explores the link between variance-measures and forward returns (Ang and Liu, 2007;Bliss and Panigirtzoglou, 2004;Doran et al, 2007;Pollet and Wilson, 2008). Most of these studies recognize a negative and short-term relation between risk measures and returns, where a high variance links to subsequent negative to low returns.…”

Section: Iv-sentiment Pair Trading Strategysupporting

confidence: 64%

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Implied Volatility Sentiment: A Tale of Two Tails

Félix¹,

Kräussl

Stork

2018

SSRN Journal

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Low probability events are overweighted in the pricing of out-of-the-money index puts and single stock calls. We show that such a behavioral bias is strongly time-varying and is linked to equity market sentiment and higher moments of the risk-neutral density. We find that our implied volatility (IV) sentiment measure, jointly derived from index and single stock options, explains investors' overweight of tail events well. Our IV-sentiment measure adds value over and above traditional factors in predicting the equity risk premium out-of-sample. When employed as a mean-reversion strategy, our IV-sentiment measure delivers economically significant results, which are more consistent than the ones produced by the conventional sentiment factor. We find that our contrarian investment strategy shows limited exposure to a set of cross-sectional equity factors, including Fama and French's five factors, the momentum factor and the low-volatility factor, and seems valuable in avoiding momentum crashes.

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Section: Introductionsupporting

confidence: 73%

Section: Iv-sentiment Pair Trading Strategysupporting

confidence: 64%

Implied Volatility Sentiment: A Tale of Two Tails

Félix¹,

Kräussl

Stork

2018

SSRN Journal

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“…Our consideration of industry effects is also motivated by the growing literature investigating the time-varying correlation between asset returns and the role of correlation risk in asset pricing (e.g., Moskowitz [2003]; Driessen et al [2009];and Pollet and Wilson [2010]). The correlation between the returns of different stocks is stronger during market downturns (Longin and Solnik [2001]), especially for stocks in the same sector.…”

Section: Yuanyuan Zhangmentioning

confidence: 99%

Option-Implied Volatilities and Stock Returns: Evidence from Industry-Neutral Portfolios

Liu

Pong²,

Shackleton

et al. 2014

JPM

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“…Much attention has been given to understanding correlations in financial markets and their dynamics, for both daily (Mantegna 1999, Gopikrishnan et al 2000, Cizeau et al 2001, Forbes and Rigobon 2002, Campbell et al 2008, Podobnik and Stanley 2008, Carbone 2009, Aste et al 2010, Pollet and Wilson 2010, Kenett et al 2012a and intra-day time scales (Bonanno et al 2001, Borghesi et al 2007, Tumminello et al 2007b, Munnix et al 2010. More recently, other measures of similarity have been introduced, such as Granger-causality analysis (Billio et al 2012) and partial correlation analysis (Kenett et al 2010), both of which aim to quantify how the behavior of one financial asset provides information about the behavior of a second asset.…”

Section: Introductionmentioning

confidence: 99%

Emergence of Statistically Validated Financial Intraday Lead-Lag Relationships

Curme

Tumminello

Mantegna³

et al. 2014

SSRN Journal

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According to the leading models in modern finance, the presence of intraday lead-lag relationships between financial assets is negligible in efficient markets. With the advance of technology, however, markets have become more sophisticated. To determine whether this has resulted in an improved market efficiency, we investigate whether statistically significant lagged correlation relationships exist in financial markets. We introduce a numerical method to statistically validate links in correlationbased networks, and employ our method to study lagged correlation networks of equity returns in financial markets. Crucially, our statistical validation of lead-lag relationships accounts for multiple hypothesis testing over all stock pairs. In an analysis of intraday transaction data from the periods 2002-2003 and 2011-2012, we find a striking growth in the networks as we increase the frequency with which we sample returns. We compute how the number of validated links and the magnitude of correlations change with increasing sampling frequency, and compare the results between the two data sets. Finally, we compare topological properties of the directed correlation-based networks from the two periods using the in-degree and out-degree distributions and an analysis of three-node motifs. Our analysis suggests a growth in both the efficiency and instability of financial markets over the past decade.

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Average Correlation and Stock Market Returns

Cited by 61 publications

References 39 publications

Implied Volatility Sentiment: A Tale of Two Tails

Implied Volatility Sentiment: A Tale of Two Tails

Option-Implied Volatilities and Stock Returns: Evidence from Industry-Neutral Portfolios

Emergence of Statistically Validated Financial Intraday Lead-Lag Relationships

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