Stock Price Jumps and Cross-Sectional Return Predictability

Jiang, George J.; Yao, Tong

doi:10.1017/s0022109013000513

Cited by 72 publications

(62 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…14 consistent with Jiang and Yao (2013) who show that small stocks tend to have higher jump returns than large stocks. The negative jump-firm size relationship found here and in Jiang and Yao (2013) could be explained by prior literature (see, for example, Bhushan, 1989) showing that small firms tend to have low analyst followings because of frequent credit-constrained situations.…”

Section: Defining Systematic Cojumpssupporting

confidence: 77%

Systematic Cojumps, Market Component Portfolios and Scheduled Macroeconomic Announcements,

Chan

Bowman²,

Neely

2017

View full text Add to dashboard Cite

Abstract:This study provides evidence of common bivariate jumps (i.e., systematic cojumps) between the market index and style-sorted portfolios. Systematic cojumps are prevalent in book-tomarket portfolios and hence, their risk cannot easily be diversified away by investing in growth or value stocks. Nonetheless, large-cap firms have less exposure to systematic cojumps than small-cap firms. Probit regression reveals that systematic cojump occurrences are significantly associated with worse-than-expected scheduled macroeconomic announcements, especially those pertaining to the Federal Funds target rate. Tobit regression shows that Federal Funds news surprises are also significantly related to the magnitude of systematic cojumps. IntroductionEmpirical asset pricing literature typically characterizes the log price process as a combination of continuous and discontinuous sample paths; see, for example, Andersen et al. (2007), Beber and Brandt (2010) and Rangel (2011). Related to these studies, Das and Uppal (2004) define cojumps as infrequent discontinuous sample paths that occur simultaneously across multiple assets (see also Dungey et al., 2009;Lahaye et al., 2011;Dungey and Hvozdyk, 2012). It is important to consider cojumps -in addition to individual jumps -in order to properly diversify portfolios. Das and Uppal (2004) find that cojumps reduce the benefits of portfolio diversification and can expose highly levered portfolios to large losses. Similarly, Cont and Kan (2011) show that cojump is a key determinant in modelling default contagion. In contrast, Pukthuanthong and Roll (2015) find that individual jumps identified across various countries are only weakly correlated, which the authors interpret as suggesting that cross-border diversification could provide reasonable protection against idiosyncratic jumps.The present study characterizes systematic cojumps, which we define as common bivariate jumps in prices between the market index and its component portfolios sorted based on market capitalization (size) and book-to-market (B/M) price ratio. Similarly, Gilder et al. (2014) define simultaneous jumps in the market index and cojumps among the underlying stocks as systematic cojumps. Fundamental portfolio theory suggests that systematic cojumps are non-diversifiable, because common jumps observed across the market portfolio's underlying components are likely due to market-wide news and this news produces jumps in the market portfolio (Bollerslev et al., 2008, p.234; Gilder et al., 2014, p.443). The present study examines whether this really happens by scrutinizing the effect of regularly scheduled macroeconomic announcements on systematic cojumps. In linking cojumps to macroeconomic announcements, this study extends the research by Gilder et al. (2014), who show that systematic cojumps are related to the timing of macroeconomic 2 announcements and Dungey et al. (2009), Evans (2011), Lahaye et al. (2011 and Dungey and Hvozdyk (2012), who provide similar findings for cojumps identified separately across indiv...

show abstract

Section: Defining Systematic Cojumpssupporting

confidence: 77%

Systematic Cojumps, Market Component Portfolios and Scheduled Macroeconomic Announcements,

Chan

Bowman²,

Neely

2017

View full text Add to dashboard Cite

show abstract

“…We include Size, Book-to-Market, Momentum and Illiquidity. These variables have been shown to be priced in the cross-section of stock returns (Jegadeesh & Titman, 1993;Amihud, 2002;Fama & French, 2008;Jiang & Yao, 2013). We further include two jump measures since recent studies have shown that jumps are an important factor in the crosssection of stock returns.…”

Section: B Explaining Long Memory With Firm Characteristicsmentioning

confidence: 99%

“…We further include two jump measures since recent studies have shown that jumps are an important factor in the crosssection of stock returns. Jiang & Yao (2013) analyze the predictability of cross-sectional stock returns and find that once controling for jumps firm characteristics such as size and liquidity are no longer predictive. Kelly & Jiang (2014) and Cremers et al (2015) show that the sensitivity of stocks to market tail and jump risk helps to explain the crosssectional variation in expected returns.…”

Section: B Explaining Long Memory With Firm Characteristicsmentioning

confidence: 99%

The Memory of Stock Return Volatility: Asset Pricing Implications

2017

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.

show abstract

“…For instance, Jiang and Yao (2013) find that illiquid stocks have higher daily jump returns in the cross-section. Bradley et al (2014) find that up to 70% of jumps in equity prices cannot be explained by salient news arrivals, suggesting that trading frictions play an economically important role in causing large stock price changes.…”

Section: Introductionmentioning

confidence: 99%