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Journal articles on the topic 'Stock return comovement'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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1

Dajcman, Silvo, Mejra Festic, and Alenka Kavkler. "Comovement Dynamics between Central and Eastern European and Developed European Stock Markets during European Integration and Amid Financial Crises – A Wavelet Analysis." Engineering Economics 23, no. 1 (February 15, 2012): 22–32. http://dx.doi.org/10.5755/j01.ee.23.1.1221.

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Stock market comovements between developed (represented in the article by markets of Austria, France, Germany, and the UK) and developing stock markets (represented here by three Central and Eastern European (CEE) markets of Slovenia, the Czech Republic, and Hungary) are of great importance for the financial decisions of international investors. From the point of view of portfolio diversification, short-term investors are more interested in the comovements of stock returns at higher frequencies (short-term movements), while long-term investors focus on lower frequencies comovements. As such, one has to resort to a time-frequency domain analysis to obtain insight about comovements at the particular time-frequency (scale) level. The empirical literature on the CEE and developed stock markets interdependence predominantly apply simple (Pearsons) correlation analysis, Granger causality tests, cointegration analysis, and GARCH modeling. None of the existent empirical studies examine time-scale comovements between CEE and developed stock market returns. By applying a maximal overlap discrete wavelet transform correlation estimator and a running correlation technique, we investigated the dynamics of stock market return comovements between individual Central and Eastern European countries and developed European stock markets in the period from 1997-2010. By analyzing the time-varying dynamics of stock market comovements on a scale-by-scale basis, we also examined how major events (financial crises in the investigated time period and entrance to the European Union) affected the comovement of CEE stock markets with developed European stock markets. The results of the unconditional correlation analysis show that the developed European stock markets of France, the UK, Germany and Austria were more interdependent in the observed period than the CEEs stock markets. The later group of countries exhibited a lower degree of comovement between themselves as well as with the developed European stock markets during all the observed time period. The Slovenian stock market was the least correlated with other stock markets. By using the rolling wavelet correlation technique, we wanted to answer the question as to how the correlation between CEE and developed stock markets changed over the observed period. In particular, we wanted to examine whether major economic (financial) and political events in the world and European economies (the Russian financial crisis, the dot-com financial crisis, the attack on the WTC, the CEE countries joining the European union, and the recent global financial crisis) have influenced the dynamics of CEE stock market comovements with developed European stock markets. The results show that stock market return comovements between CEE and developed European stock markets varied over time scales and time. At all scales and during the entire observed time period the Hungarian and Czech stock markets were more interconnected to developed European stock markets than the Slovenian stock market was. The highest comovement between the investigated CEE and developed European stock market returns was normally observed at the highest scales (scale 5, corresponding to stock market return dynamics over 32-64 days, and scale 6, corresponding to stock market return dynamics over 32-64 and 64-128 days). At all scales the Hungarian and Czech stock markets were more connected to developed European stock markets than the Slovenian stock market. We found that European integration lead to increased comovement between CEE and developed stock markets, while the financial crises in the observed period led only to short-term increases in stock market return comovements.DOI: http://dx.doi.org/10.5755/j01.ee.23.1.1221
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2

Jiang, Lei, Ke Wu, and Guofu Zhou. "Asymmetry in Stock Comovements: An Entropy Approach." Journal of Financial and Quantitative Analysis 53, no. 4 (August 2018): 1479–507. http://dx.doi.org/10.1017/s0022109018000340.

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We provide an entropy approach for measuring the asymmetric comovement between the return on a single asset and the market return. This approach yields a model-free test for stock return asymmetry, generalizing the correlation-based test proposed by Hong, Tu, and Zhou (2007). Based on this test, we find that asymmetry is much more pervasive than previously thought. Moreover, our approach also provides an entropy-based measure of downside asymmetric comovement. In the cross section of stock returns, we find an asymmetry premium: Higher downside asymmetric comovement with the market indicates higher expected returns.
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Hameed, Allaudeen, Randall Morck, Jianfeng Shen, and Bernard Yeung. "Information, Analysts, and Stock Return Comovement." Review of Financial Studies 28, no. 11 (August 4, 2015): 3153–87. http://dx.doi.org/10.1093/rfs/hhv042.

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Cho, Chan Ho, and Tim Mooney. "Stock return comovement and Korean business groups." Review of Development Finance 5, no. 2 (December 2015): 71–81. http://dx.doi.org/10.1016/j.rdf.2015.09.001.

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Luo, Ting, and Wenjuan Xie. "Industry information uncertainty and stock return comovement." Asia-Pacific Journal of Accounting & Economics 19, no. 3 (December 2012): 330–51. http://dx.doi.org/10.1080/16081625.2012.667477.

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6

Hobbes, Garry, Frewen Lam, and Geoffrey F. Loudon. "Regime Shifts in the Stock–Bond Relation in Australia." Review of Pacific Basin Financial Markets and Policies 10, no. 01 (March 2007): 81–99. http://dx.doi.org/10.1142/s0219091507000969.

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Previous evidence suggests that the implied volatility from equity index options, as a measure of stock market uncertainty, can provide "forward-looking information" about the stock–bond return correlation. This paper uses an alternative regime-switching autoregressive model to characterize state-dependent stock–bond return comovement and to evaluate the contribution of implied volatility in understanding transition dynamics. We confirm that implied volatility provides information about transition dynamics which is not inherent in the stock and bond returns, notwithstanding several different features of our data set and methodological approach.
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7

Kahraman, Bige, and Heather Tookes. "Margin Trading and Comovement During Crises*." Review of Finance 24, no. 4 (September 27, 2019): 813–46. http://dx.doi.org/10.1093/rof/rfz019.

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Abstract We exploit threshold rules governing margin trading eligibility in India to identify a causal link between margin trading and increased comovement during crises. Margin trading explains more than one-quarter of the increase return comovement that we observe during crises. To understand the mechanisms driving this result, we evaluate the relative importance of stock connections through common brokers (who provide margin financing) versus common margin traders. We find that common brokers are most important. Margin-eligible stocks that are more connected through common brokers experience larger crisis-period increases in pairwise return comovement, especially when those brokers’ clients have experienced recent portfolio losses, when their clients have outstanding margin loans in more volatile stocks, and when the brokers are large. These findings are consistent with Brunnermeier and Pedersen (2009), in which initial shocks propagate due to the tightening of margin constraints imposed by financial intermediaries.
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8

Faias, José A., and Miguel A. Ferreira. "Does institutional ownership matter for international stock return comovement?" Journal of International Money and Finance 78 (November 2017): 64–83. http://dx.doi.org/10.1016/j.jimonfin.2017.08.004.

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9

Marcet, Francisco. "Analyst coverage network and stock return comovement in emerging markets." Emerging Markets Review 32 (September 2017): 1–27. http://dx.doi.org/10.1016/j.ememar.2017.05.002.

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10

Chen, Zhuo, and Andrea Lu. "A Market-Based Funding Liquidity Measure." Review of Asset Pricing Studies 9, no. 2 (September 10, 2018): 356–93. http://dx.doi.org/10.1093/rapstu/ray007.

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AbstractWe construct a traded funding liquidity measure from stock returns. Guided by a model, we extract the measure as the return spread between two beta-neutral portfolios constructed using stocks with high and low margins, to control for their sensitivity to the aggregate funding shocks. Our measure of funding liquidity is correlated with other funding liquidity proxies. It delivers a positive risk premium that cannot be explained by existing risk factors. A model augmented by our funding liquidity measure has superior pricing performance for various portfolios. Despite evident comovement, this measure contains additional information that is not subsumed by market liquidity.Received March 29, 2017; accepted August 8, 2018 by Editor Wayne Ferson.
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11

Engsted, Tom, and Carsten Tanggaard. "The Danish stock and bond markets: comovement, return predictability and variance decomposition." Journal of Empirical Finance 8, no. 3 (July 2001): 243–71. http://dx.doi.org/10.1016/s0927-5398(01)00029-9.

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12

Hu, Yitong, Xiao Li, and Dehua Shen. "Attention allocation and international stock return comovement: Evidence from the Bitcoin market." Research in International Business and Finance 54 (December 2020): 101286. http://dx.doi.org/10.1016/j.ribaf.2020.101286.

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13

Mazouz, Khelifa, Abdulkadir Mohamed, and Brahim Saadouni. "Stock return comovement around the Dow Jones Islamic Market World Index revisions." Journal of Economic Behavior & Organization 132 (December 2016): 50–62. http://dx.doi.org/10.1016/j.jebo.2016.05.011.

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14

Liow, Kim Hiang, Xiaoxia Zhou, Qiang Li, and Yuting Huang. "Comovement of Greater China Real Estate Markets: Some Time Scale Evidence." Journal of Real Estate Research 41, no. 3 (July 2019): 473–512. http://dx.doi.org/10.22300/0896-5803.41.3.473.

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The novelty of this study is the use of wavelets, which make it possible to assess simultaneously how the Greater China (GC) and international securitized real estate markets comove at various frequencies. From the wavelet analysis, investors can extract the time scale that most interests them. We apply both continuous wavelet coherency modeling and discrete decompositions to unveil the multi-horizon nature of the co-movement relationship. We find that the examined real estate market co-movement is a “multi-scale” phenomenon. The strength of the return linkage increases with scales. The co-movement within and across the three GC markets is unstable and the pattern of the relationship is non-uniform across various time scales. The strongest degree of cross-market connection occurs during the global financial crisis period and at the longest investment horizon of 256–512 days. Moreover, the real estate-stock returns of the three GC economies are less correlated in the long run, implying potential opportunities for both time and scale in GC real estate-stock portfolio diversification activities.
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15

Deng, Kaihua. "Another Look at Large-Cap Stock Return Comovement: A Semi-Markov-Switching Approach." Computational Economics 51, no. 2 (June 13, 2016): 227–62. http://dx.doi.org/10.1007/s10614-016-9596-x.

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16

Korley, Maud, and Evangelos Giouvris. "The Regime-Switching Behaviour of Exchange Rates and Frontier Stock Market Prices in Sub-Saharan Africa." Journal of Risk and Financial Management 14, no. 3 (March 15, 2021): 122. http://dx.doi.org/10.3390/jrfm14030122.

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Frontier markets have become increasingly investible, providing diversification opportunities; however, there is very little research (with conflicting results) on the relationship between Foreign Exchange (FX) and frontier stock markets. Understanding this relationship is important for both international investor and policymakers. The Markov-switching Vector Auto Regressive (VAR) model is used to examine the relationship between FX and frontier stock markets. There are two distinct regimes in both the frontier stock market and the FX market: a low-volatility and a high-volatility regime. In contrast with emerging markets characterised by “high volatility/low return”, frontier stock markets provide high (positive) returns in the high-volatility regime. The high-volatility regime is less persistent than the low-volatility regime, contrary to conventional wisdom. The Markov Switching VAR model indicates that the relationship between the FX market and the stock market is regime-dependent. Changes in the stock market have a significant impact on the FX market during both normal (calm) and crisis (turbulent) periods. However, the reverse effect is weak or nonexistent. The stock-oriented model is the prevalent model for Sub-Saharan African (SSA) countries. Irrespective of the regime, there is no relationship between the stock market and the FX market in Cote d’Ivoire. Our results are robust in model selection and degree of comovement.
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17

Trichilli, Yousra, Mouna Boujelbène Abbes, and Sabrine Zouari. "The impact of political instability driven by the Tunisian revolution on the relationship between Google search queries index and financial market dynamics." Journal of Capital Markets Studies 4, no. 1 (July 13, 2020): 61–76. http://dx.doi.org/10.1108/jcms-04-2020-0005.

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PurposeThis paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.Design/methodology/approachFirst, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.FindingsUsing the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.Research limitations/implicationsThis study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.Originality/valueThe important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.
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18

Li, Lu, Yang Li, Xueding Wang, and Tusheng Xiao. "Structural holes and hedge fund return comovement: evidence from network‐connected stock hedge funds in China." Accounting & Finance 60, no. 3 (September 16, 2019): 2811–41. http://dx.doi.org/10.1111/acfi.12537.

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19

Griffin, Paul A., David H. Lont, and Kurt Purdon. "Stock and Bond Return Comovement as a Different Way to Assess Information Content: The Case of Debt Covenant Violation Disclosures." Abacus 57, no. 1 (March 2021): 101–25. http://dx.doi.org/10.1111/abac.12217.

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20

BEKAERT, GEERT, ROBERT J. HODRICK, and XIAOYAN ZHANG. "International Stock Return Comovements." Journal of Finance 64, no. 6 (November 25, 2009): 2591–626. http://dx.doi.org/10.1111/j.1540-6261.2009.01512.x.

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21

Gagnon, Louis, and G. Andrew Karolyi. "Information, Trading Volume, and International Stock Return Comovements: Evidence from Cross-Listed Stocks." Journal of Financial and Quantitative Analysis 44, no. 4 (August 2009): 953–86. http://dx.doi.org/10.1017/s0022109009990196.

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AbstractWe investigate the joint dynamics of returns and trading volume of 556 foreign stocks cross-listed on U.S. markets. Heterogeneous-agent trading models rationalize how trading volume reflects the quality of traders’ information signals and how it helps to disentangle whether returns are associated with portfolio-rebalancing trades or information-motivated trades. Based on these models, we hypothesize that returns in the home (U.S.) market on high-volume days are more likely to continue to spill over into the U.S. (home) market for those cross-listed stocks subject to the risk of greater informed trading. Our empirical evidence provides support for these predictions, which confirms the link between information, trading volume, and international stock return comovements that has eluded previous empirical investigations.
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22

Firth, Michael, Shihe Fu, and Liwei Shan. "Do agglomeration economies affect the local comovement of stock returns? Evidence from China." Urban Studies 54, no. 5 (September 29, 2016): 1142–61. http://dx.doi.org/10.1177/0042098016633101.

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Prior studies in finance have examined the comovement of stock returns of firms headquartered in the same location. One interpretation of the results is that local investors have a ‘local bias’ due to an information advantage on local firms. We propose that localised agglomeration economies affect the fundamentals of local firms, resulting in the local comovement of stock returns. Using data for China A-share listed firms from 1997 to 2007, we find evidence of the comovement of stock returns of Chinese firms headquartered in the same city. We find inconsistent evidence for the local bias theory. The comovement of the stock returns of firms headquartered in the same city is stronger when the agglomeration economies in the city are stronger, suggesting that localised agglomeration economies provide an alternative explanation of the comovement of stock returns.
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23

Ye, Pengfei. "The Value of Active Investing: Can Active Institutional Investors Remove Excess Comovement of Stock Returns?" Journal of Financial and Quantitative Analysis 47, no. 3 (January 30, 2012): 667–88. http://dx.doi.org/10.1017/s0022109012000099.

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AbstractThis study uses Cremers and Petajisto’s (2009) method to separate active institutional investors from passive ones and shows that active investors can alleviate the anomalous comovement of stock returns. Focusing on 2 events linked to the excess comovement anomaly, Standard & Poor’s 500 Index additions and stock splits, I find that if an event stock has more active institutional investors trading in the post-event period, the anomalous comovement effect disappears. In contrast, if an event stock experiences a massive exit of active investors, this anomaly persists. The exit of active institutional investors also results in a strong price synchronicity effect.
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Kumar, Alok, Jeremy K. Page, and Oliver G. Spalt. "Gambling and Comovement." Journal of Financial and Quantitative Analysis 51, no. 1 (February 2016): 85–111. http://dx.doi.org/10.1017/s0022109016000089.

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AbstractThis study shows that correlated trading by gambling-motivated investors generates excess return comovement among stocks with lottery features. Lottery-like stocks comove strongly with one another, and this return comovement is strongest among lottery stocks located in regions where investors exhibit stronger gambling propensity. Looking directly at investor trades, we find that investors with a greater propensity to gamble trade lottery-like stocks more actively and that those trades are more strongly correlated. Finally, we demonstrate that time variation in general gambling enthusiasm and income shocks from fluctuating economic conditions induce a systematic component in investors’ demand for lottery-like stocks.
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25

Inaba, Kei-Ichiro. "Information-driven stock return comovements across countries." Research in International Business and Finance 51 (January 2020): 101093. http://dx.doi.org/10.1016/j.ribaf.2019.101093.

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Connolly, Robert, Chris Stivers, and Licheng Sun. "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis 40, no. 1 (March 2005): 161–94. http://dx.doi.org/10.1017/s0022109000001782.

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AbstractWe examine whether time variation in the comovements of daily stock and Treasury bond returns can be linked to measures of stock market uncertainty, specifically the implied volatility from equity index options and detrended stock turnover. From a forward-looking perspective, we find a negative relation between the uncertainty measures and the future correlation of stock and bond returns. Contemporaneously, we find that bond returns tend to be high (low) relative to stock returns during days when implied volatility increases (decreases) substantially and during days when stock turnover is unexpectedly high (low). Our findings suggest that stock market uncertainty has important cross-market pricing in-fluences and that stock-bond diversification benefits increase with stock market uncertainty.
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Baele, Lieven, Geert Bekaert, and Koen Inghelbrecht. "The Determinants of Stock and Bond Return Comovements." Review of Financial Studies 23, no. 6 (March 28, 2010): 2374–428. http://dx.doi.org/10.1093/rfs/hhq014.

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28

Greenwood, Robin M., and Nathan Sosner. "Trading Patterns and Excess Comovement of Stock Returns." Financial Analysts Journal 63, no. 5 (September 2007): 69–81. http://dx.doi.org/10.2469/faj.v63.n5.4841.

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29

Uysal, Vahap, and Seth Hoelscher. "Local clientele: geography and comovement of stock returns." Review of Behavioral Finance 10, no. 3 (August 13, 2018): 231–51. http://dx.doi.org/10.1108/rbf-07-2017-0071.

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Purpose Local investors have the ability to impact the stock prices and returns of local firms. However, the impact of news made by a firm on local investors and neighboring companies is absent from the academic literature. The purpose of this paper is to fill that void and examine how a local investor clientele affects the stock market reactions of firms located within the same geographic proximity as a news-generating firm. Design/methodology/approach After accounting for firm, industry, and geographic characteristics, this study examines how a firm’s dividend initiation announcement (positive news) influences stock prices of seemingly unrelated firms within the same metropolitan statistical area (MSA). Findings Dividend-paying firms located in areas with a higher percentage of dividend clientele experience a positive comovement reaction when a seemingly unrelated firm within the same MSA announces a dividend initiation. The positive reactions are specifically for dividend-paying firms, while non-dividend payers exhibit no significant response. These results are robust to numerous regression methods and alternative explanations. Practical implications These findings are consistent with the positive-investor-attention hypothesis, suggesting positive spillover effects from news announcements for other local firms in the presence of individual investor clientele. Originality/value This is the first study to link how news generated by one firm can influence other geographically local firms, providing evidence on the impact of individual investor clientele on stock returns of local non-news firms.
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Tavares, José. "Economic integration and the comovement of stock returns." Economics Letters 103, no. 2 (May 2009): 65–67. http://dx.doi.org/10.1016/j.econlet.2009.01.016.

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31

Harford, Jarrad, and Aditya Kaul. "Correlated Order Flow: Pervasiveness, Sources, and Pricing Effects." Journal of Financial and Quantitative Analysis 40, no. 1 (March 2005): 29–55. http://dx.doi.org/10.1017/s0022109000001733.

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AbstractWe examine the importance of indexing, industry, and broad market forces in driving common effects in order flow, returns, and trading costs. Common effects are strong for order flow and returns in a sample of S&P 500 stocks, but are weak in a sample of non-index stocks and for trading costs in both samples. Industry and broad market effects exist in order flow for both samples, but indexing effects are dominant. Correlated order flow drives common effects in returns and, to a lesser extent, those in trading costs. An event study of the effect of index addition on order flow and return comovement reinforces these conclusions. Our results show that common effects are not pervasive and have implications for diversification strategies and price formation models.
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Connolly, Robert A., Chris Stivers, and Licheng Sun. "Commonality in the time-variation of stock–stock and stock–bond return comovements." Journal of Financial Markets 10, no. 2 (May 2007): 192–218. http://dx.doi.org/10.1016/j.finmar.2006.09.005.

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Rua, António, and Luís C. Nunes. "International comovement of stock market returns: A wavelet analysis." Journal of Empirical Finance 16, no. 4 (September 2009): 632–39. http://dx.doi.org/10.1016/j.jempfin.2009.02.002.

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Li, F. "Identifying Asymmetric Comovements of International Stock Market Returns." Journal of Financial Econometrics 12, no. 3 (June 7, 2013): 507–43. http://dx.doi.org/10.1093/jjfinec/nbt006.

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Faseli, Omid. "Screening for light crude oil and market comovements." International Journal of Research in Business and Social Science (2147- 4478) 9, no. 7 (December 12, 2020): 123–29. http://dx.doi.org/10.20525/ijrbs.v9i7.949.

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This study aimed to perform a screening for economic interrelationships among market participants from the stock market, global stock indices, and commodities from fossil energy, agricultural, and the metals sector. Particular focus was put on the comovements of the light crude oil benchmarks West Texas Intermediate (WTI) and Brent crude oil. In finance research and the crude oil markets, identifying novel groupings and interactions is a fundamental requirement due to the extended impact of crude oil price fluctuations on economic growth and inflation. Thus, it is of high interest for investors to identify market players and interactions that appear sensitive to crude oil price volatility triggers. The price development of 14 stocks, 25 leading global indices, and 13 commodity prices, including WTI and Brent, were analyzed via data mining applying the hierarchical correlation cluster mapping technique. All price data comprised the period from January 2012 – December 2018 and were based on daily returns. The technique identifies and visualizes existing hierarchical clusters and correlation patterns emphasizing comovements that indicate positively correlated processes. The method successfully identified clustering patterns and a series of relevant and partly unexpected novel comovements in all investigated economic sectors. Although additional research is required to reveal the causative factors, the study offers an insight into in-depth market interrelationships.
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Frijns, Bart, Willem F. C. Verschoor, and Remco C. J. Zwinkels. "Excess stock return comovements and the role of investor sentiment." Journal of International Financial Markets, Institutions and Money 49 (July 2017): 74–87. http://dx.doi.org/10.1016/j.intfin.2017.02.005.

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Deng, Kaihua. "A test of asymmetric comovement for state-dependent stock returns." Journal of Empirical Finance 36 (March 2016): 68–85. http://dx.doi.org/10.1016/j.jempfin.2016.01.009.

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Li, Mingyi, Xiangkang Yin, and Jing Zhao. "Does program trading contribute to excess comovement of stock returns?" Journal of Empirical Finance 59 (December 2020): 257–77. http://dx.doi.org/10.1016/j.jempfin.2020.11.001.

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Brenner, Menachem, Paolo Pasquariello, and Marti Subrahmanyam. "On the Volatility and Comovement of U.S. Financial Markets around Macroeconomic News Announcements." Journal of Financial and Quantitative Analysis 44, no. 6 (October 8, 2009): 1265–89. http://dx.doi.org/10.1017/s002210900999038x.

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AbstractThe objective of this paper is to provide a deeper insight into the links between financial markets and the real economy. To that end, we study the short-term anticipation and response of U.S. stock, Treasury, and corporate bond markets to the first release of surprise U.S. macroeconomic information. Specifically, we focus on the impact of these announcements not only on the level, but also on the volatility and comovement of those assets’ returns. We do so by estimating several extensions of the parsimonious multivariate GARCH-DCC model of Engle (2002) for the excess holding-period returns on seven portfolios of these asset classes. We find that both the process of price formation in each of those financial markets and their interaction appear to be driven by fundamentals. Yet our analysis reveals a statistically and economically significant dichotomy between the reaction of the stock and bond markets to the arrival of unexpected fundamental information. We also show that the conditional mean, volatility, and comovement among stock, Treasury, and corporate bond returns react asymmetrically to the information content of these surprise announcements. Overall, the above results shed new light on the mechanisms by which new information is incorporated into prices within and across U.S. financial markets.
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Ben Ameur, Hachmi, Fredj Jawadi, Wael Louhichi, and Abdoulkarim Idi Cheffou. "MODELING INTERNATIONAL STOCK PRICE COMOVEMENTS WITH HIGH-FREQUENCY DATA." Macroeconomic Dynamics 22, no. 7 (November 21, 2017): 1875–903. http://dx.doi.org/10.1017/s1365100516000924.

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This paper studies stock price comovements in two key regions [the United States and Europe, which is represented by three major European developed countries (France, Germany, and the United Kingdom)]. Our paper uses recent high-frequency data (HFD) and investigates price comovements in the context of “normal times” and crisis periods. To this end, we applied a non-Gaussian Asymmetrical Dynamic Conditional Correlation (ADCC)-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model and the Marginal Expected Shortfall (MES) approach. This choice has three advantages: (i) With the development of high-frequency trading (HFT), it is more appropriate to use HFD to test price linkages for overlapping and nonoverlapping data. (ii) The ADCC-GARCH model captures further asymmetry in price comovements. (iii) The use of the MES enables to measure systemic risk contributions around the distribution tails. Accordingly, we offer two interesting findings. First, while the hypothesis of asymmetrical and time-varying stock return linkages is not rejected, the MES approach indicates that both European and US indices make a considerable contribution to each other's systemic risk, with significant input from Frankfurt to the French and US markets, especially following the collapse of Lehman Brothers. Second, we show that the propagation of systemic risk is higher during the crisis period and overlapping trading hours than during nonoverlapping hours. Thus, the MES test is recommended as an indicator to help monitor market exposure to systemic risk and to gauge expected losses for other markets.
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41

Farhi, Emmanuel, and Xavier Gabaix. "Rare Disasters and Exchange Rates *." Quarterly Journal of Economics 131, no. 1 (October 29, 2015): 1–52. http://dx.doi.org/10.1093/qje/qjv040.

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Abstract We propose a new model of exchange rates, based on the hypothesis that the possibility of rare but extreme disasters is an important determinant of risk premia in asset markets. The probability of world disasters as well as each country’s exposure to these events is time-varying. This creates joint fluctuations in exchange rates, interest rates, options, and stock markets. The model accounts for a series of major puzzles in exchange rates: excess volatility and exchange rate disconnect, forward premium puzzle and large excess returns of the carry trade, and comovements between stocks and exchange rates. It also makes empirically successful signature predictions regarding the link between exchange rates and telltale signs of disaster risk in currency options.
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42

Kumar, Alok, Jeremy K. Page, and Oliver G. Spalt. "Investor Sentiment and Return Comovements: Evidence from Stock Splits and Headquarters Changes*." Review of Finance 17, no. 3 (April 25, 2012): 921–53. http://dx.doi.org/10.1093/rof/rfs010.

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43

Didier, Tatiana, Inessa Love, and María Soledad Martínez Pería. "What explains comovement in stock market returns during the 2007-2008 crisis?" International Journal of Finance & Economics 17, no. 2 (January 24, 2011): 182–202. http://dx.doi.org/10.1002/ijfe.442.

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44

Kaplanis, Evi C. "Stability and forecasting of the comovement measures of international stock market returns." Journal of International Money and Finance 7, no. 1 (March 1988): 63–75. http://dx.doi.org/10.1016/0261-5606(88)90006-x.

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45

Kizys, Renatas, and Christian Pierdzioch. "Changes in the international comovement of stock returns and asymmetric macroeconomic shocks." Journal of International Financial Markets, Institutions and Money 19, no. 2 (April 2009): 289–305. http://dx.doi.org/10.1016/j.intfin.2008.01.002.

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46

Wu, Chih-Chiang, and Zih-Ying Lin. "An economic evaluation of stock–bond return comovements with copula-based GARCH models." Quantitative Finance 14, no. 7 (October 23, 2012): 1283–96. http://dx.doi.org/10.1080/14697688.2012.727213.

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47

KAROLYI, G. ANDREW, and RENÉ M. STULZ. "Why Do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements." Journal of Finance 51, no. 3 (July 1996): 951–86. http://dx.doi.org/10.1111/j.1540-6261.1996.tb02713.x.

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48

Chen, Menggen. "Study on the Shock-transmission Mechanism of Stock Price among China, Russia and India." EMAJ: Emerging Markets Journal 4, no. 1 (August 6, 2014): 33–42. http://dx.doi.org/10.5195/emaj.2014.58.

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Researchers pay more and more attention on the price comovement-effect among international stock markets. This paper deals with the transmission mechanism of price shocks among three stock markets of China, Russia and India, with a sample of weekly returns. The results showed that the price fluctuation of each market has an influence on other markets, although the price behavior is significantly independent. The impact of external price innovations will last 5 or 6 weeks usually and disappear after about 8 weeks. The pattern of transmission-mechanism for the price shocks is very different from each other. Besides, a further study revealed that the influence of external shocks on the domestic stock price increased significantly among the three markets after the 2008 international financial crisis.
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49

Yafeh, Yishay, and Stijn Claessens. "Additions to Market Indices and the Comovement of Stock Returns Around the World." IMF Working Papers 11, no. 47 (2011): 1. http://dx.doi.org/10.5089/9781455218950.001.

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50

Horvath, Roman, and Petr Poldauf. "International Stock Market Comovements: What Happened during the Financial Crisis?" Global Economy Journal 12, no. 1 (March 2012): 1850252. http://dx.doi.org/10.1515/1524-5861.1788.

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We investigate the stock market comovements in Australia, Brazil, Canada, China, Germany, Hong Kong, Japan, Russia, South Africa, the UK, and the USA, both at the market and sectoral level in 2000-2010. Using multivariate GARCH models, our results suggest that the correlation among equity returns during the financial crisis (2008-2010) somewhat increased, suggesting that the crisis represented a common shock to all countries. The U.S. stock market is found to be the most correlated with the stock markets in Brazil, Canada and UK. The correlation of U.S. and Chinese stock market is essentially zero before the crisis; it becomes slightly positive during the crisis. The sectoral indices are less correlated than the market indices over the whole period, but, again, the correlations increase during the crisis.
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