Stock Return Correlations Research Articles

Empirical Evidence of Co-Movements between China and Global Stock Markets

This paper investigates the dynamic correlations between Chinese stock return and global markets at both market and sectoral levels. Statistics suggest that stock-return correlations across markets are time-varying and display structural breaks. The evidence indicates that the stock returns of financial sector exhibit the highest correlation across countries among 10 sectors. Low correlations present in Health Cares, Telecommunications, and Utilities sectors. The correlations tied closely with geographic location – the correlation with Hong Kong is highest followed by South Koran, Japan, Europe and US markets. The time-series stock-return correlations are positively correlated with the conditional variance and variance premiums.

Determinants of stock market comovements among US and emerging economies during the US financial crisis

By analyzing the dynamic conditional correlations (DCC) of the daily stock returns of 10 emerging economies in comparison with those of the US for the period of 2006–2010, we find different patterns of crisis spillover among 10 emerging economies. While a group of countries has three distinctive phases of crisis spillover (contagion, herding, and post-crisis adjustment), other groups show different phases of crisis spillover. It is also shown that increases in CDS spread and TED spread decrease conditional correlations while increases in foreign institutional investment, exchange market volatility, and the VIX index of the S&P 500 increase conditional correlations.

A Cross-Sectional Correlation Robust Test of Abnormal Stock Returns in Long-Horizon Event Studies

This paper proposes a novel standardized test for abnormal returns in long-horizon event studies that takes into account cross-sectional correlation, autocorrelation, and hetersoskedasticity of stock returns. Extensive simulation analyses demonstrate improved size and power of testing relative to existing long-run test methodologies. Application to initial public offerings and seasoned equity offerings further demonstrates robustness to extreme return outliers inherent in these long-run studies.

Dynamic Equilibrium with Two Stocks, Heterogeneous Investors, and Portfolio Constraints

We study dynamic equilibrium in a Lucas economy with two stocks, two heterogeneous constant relative risk aversion investors, and portfolio constraints. We focus on margin and leverage constraints, which restrict access to credit. We find a positive relationship between the amount of leverage in the economy and magnitudes of stock return correlations and volatilities. Tighter constraints generate rich patterns in correlations and volatilities, make them less countercyclical, increase risk premia proportionally to assets' margins, and increase prices of low-margin assets more than prices of high-margin assets. We derive closed-form solutions for the unconstrained case and the case of leverage constraints.

Was There a Contagion during the Asian Crises?

The contagion of financial crises surrounding the markets around the world has been in the forefront of academic and public discussions. In this paper, we attempt to study the “contagion effect” of the stock market crises around the world by studying the correlations of global stock returns and volatility. We analyze the daily returns of major stock indexes around the world to discover the timing and path of the transmission of shocks that manifest themselves in stock market returns. We construct VARs of major stock market index returns and volatilities. Our work differs from the literature in analyzing spillover effects between emerging markets and other major stock markets.

Dynamic Equilibrium with Two Stocks, Heterogeneous Investors, and Portfolio Constraints

We study dynamic general equilibrium in a Lucas economy with two trees, one consumption good, two CRRA investors with heterogeneous risk aversions, and portfolio constraints. We focus on margin and leverage constraints, which restrict access to credit markets. We find positive relationship between the amount of leverage in the economy and magnitudes of conditional stock return correlations and volatilities. Tighter constraints give rise to rich saddle-type patterns in correlations and volatilities, make them less countercyclical, increase risk premia proportionally to assets' margins, and increase price-dividend ratios of low-margin assets more than those of high-margin assets. The paper offers a new methodology for solving models with constraints, and derives closed-form solutions for the unconstrained case and the case of leverage constraints.

BRIC and the U.S. financial crisis: An empirical investigation of stock and bond markets

We examine empirical evidence of the behavior of stocks and bonds from BRIC nations by using daily data from January 2003 to July 2010. We present unconditional and conditional empirical results depending upon a simple measure of U.S. financial stress. In the long term, BRIC bond markets deviate much more from the U.S. financial stress measure than the BRIC bonds and stocks that deviate among themselves. Stock and bond return correlations for Brazil and Russia are significantly large and negative. The own correlations are more important in determining the evolution of the conditional correlations relative to unexpected news. Dynamic conditional correlations between stock returns, bond returns and U.S. financial stress increase after the Lehman Brothers' event in September 2008, except for the bond returns in India.

Dynamic correlation analysis of US financial crisis and contagion: evidence from four OECD countries

By analysing the Dynamic Conditional Correlations (DCC) of the daily stock returns of four OECD countries with that of the US for the period 2006–2010, we could find a process of increasing correlations (contagion) in the first phase of the US financial crisis and an additional increase of correlations (herding) during the second phase of the US financial crisis for the UK, Australia and Switzerland. However, the impact of the US financial crisis on Japan was limited to the increase in correlation volatilities in the first phase. We also propose a new approach (DCC Multivariate Generalized Autoregressive Conditional Heteroscedastic model with Exogenous variables (DCCX-MGARCH)) that allows simultaneous estimation of the DCC and their determinants, which can be used to identify channels of contagion. It is shown that an increase in VIX stock market index increases conditional correlations but an increase in the TED spread and relative stock market capitalization decrease conditional correlations of stock returns between four OECD countries and the US.

Real and Financial Integration in East Asia

AbstractThis paper examines the real and financial integration of East Asian economies, by comparing the degree of real vs financial and global vs regional integration, before vs after the Asian crisis. First, price and quantity measures such as the size of intra‐ and inter‐regional trade, cross‐border financial assets, stock return correlation, and interest rate differentials are investigated. Second, the structural panel vector autoregression (VAR) model is constructed to analyze macroeconomic consequences of real and financial integration such as cross‐country output and consumption relation. The results suggest that (i) the degree of real integration significantly increased after the crisis, both regionally and globally; (iii) quantity and price measures showed an increased financial integration after the crisis, but the consumption relation did not; (iv) the degree of regional financial integration is smaller than that of global financial integration, based on the consumption relation; and (v) financial integration lags real integration, especially for regional integration.

A simple indicator of systemic risk

We examine the relevance and effectiveness of stock return correlations among financial institutions as an indicator of systemic risk. By analyzing the trends and fluctuations of daily stock return correlations and default correlations among the 22 largest bank holding companies and investment banks from 1988 to 2008, we find that daily stock return correlation is a simple, robust, forward-looking, and timely systemic risk indicator. There is an increasing trend in stock return correlation among banks, whereas there is no obvious correlation trend among non-banks. We also disaggregate the stock returns into systematic and idiosyncratic components and find that the correlation increases are largely driven by the increases in correlations between banks’ idiosyncratic risks, which give rise to increasing systemic risk. Correlation spikes tend to predict or coincide with significant economic or market events, especially during the 2007–2008 financial crisis. Furthermore, we show that stock return correlations offer a perspective on the level of systemic risk in the financial sector that is not already captured by default correlations. Stock return correlations are not subject to data limitations or model specification errors that other potential systemic risk measures may face. Therefore, we recommend that regulators and businesses monitor daily stock return correlations among those large and highly leveraged financial institutions to track the level of systemic risk.

Skewness in Stock Returns: Reconciling the Evidence on Firm Versus Aggregate Returns

Aggregate stock market returns display negative skewness. Firm stock returns display positive skewness. The large literature that tries to explain the first stylized fact ignores the second. This article provides a unified theory that reconciles the two facts by explicitly modeling firm-level heterogeneity. I build a stationary asset pricing model of firm announcement events where firm returns display positive skewness. I then show that cross-sectional heterogeneity in firm announcement events can lead to conditional asymmetric stock return correlations and negative skewness in aggregate returns. I provide evidence consistent with the model predictions. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

STOCK MARKET DIFFERENCES IN CORRELATION-BASED WEIGHTED NETWORK

We examined the sector dynamics of Korean stock market in relation to the market volatility. The daily price data of 360 stocks for 5019 trading days (from January, 1990 to August, 2008) in Korean stock market are used. We performed the weighted network analysis and employed four measures: the average, the variance, the intensity, and the coherence of network weights (absolute values of stock return correlations) to investigate the network structure of Korean stock market. We performed regression analysis using the four measures in the seven major industry sectors and the market (seven sectors combined). We found that the average, the intensity, and the coherence of sector (subnetwork) weights increase as market becomes volatile. Except for the "Financials" sector, the variance of sector weights also grows as market volatility increases. Based on the four measures, we can categorize "Financials," "Information Technology" and "Industrials" sectors into one group, and "Materials" and "Consumer Discretionary" sectors into another group. We investigated the distributions of intrasector and intersector weights for each sector and found the differences in "Financials" sector are most distinct.

Asymmetric News and Asset Pricing

This paper shows that in asset pricing the information environment gives rise to a systematic risk factor when the informativeness of future news events varies with their content (i.e., bad news and good news are not equally informative). The paper further shows that in such cases (cross) serial correlation in stock returns arises. The results provide new insights on on the relation between disclosure and cost of capital and on empirically documented patterns in stock returns like momentum and post earnings announcement drift.

US and European stock market crashes: Any evidence of interdependence?

This paper examines the response of Stock Prices (SPs) in Germany, Italy, and the UK to shocks to US Stock Prices (USSPs) using Vector Error Correction Models (VECMs) and cross-country stock return correlations. Our results yield clear implications. Positive shocks to USSPs lead to significant, positive, responses in European SPs. Shocks to US stock prices also explain over 43% of the forecast error variance in European SPs. Shocks to domestic variables never cause significant responses in European SPs. These results indicate strong interdependence between the US stock market and stock markets in the UK, Germany and Italy.

Measuring the effects of geographical distance on stock market correlation

Recent studies suggest that the correlation of stock returns increases with decreasing geographical distance. However, there is some debate on the appropriate methodology for measuring the effects of distance on correlation. We modify a regression approach suggested in the literature and complement it with an approach from spatial statistics, the mark correlation function. For the stocks contained in the S&P 500 that we examine, both approaches lead to similar results. Contrary to previous studies we find that beyond 50 miles geographical proximity is irrelevant for stock return correlations. For distances below 50 miles, we can show that the magnitude of local correlations varies with investor sentiment.

International portfolio diversification is better than you think

Using aggregate data on bilateral cross-border equity holdings, we investigate whether investors correctly hedge their over-exposure to domestic risk (the well-known equity home bias) by investing in foreign stock markets that have low correlation with their home stock market. To deal with the endogeneity of stock return correlations, we instrument current correlations with past correlations. Controlling for many determinants of international portfolios, we find that, all else equal, investors do tilt their foreign holdings towards countries, which offer better diversification opportunities. The diversification motive that we uncover is stronger for source countries exhibiting a higher level of home bias.

The Systematic and Non-Systematic Variations in the Correlations of Stock Returns

Correlations of stock returns change randomly through time. The variations in correlations are themselves correlated across pairs of stocks. In an equilibrium model, investor uncertainty regarding the growth rate of the economy is the factor driving systematic variations in R square, pairwise correlations, market volatility and volatilities of interest rates. uncertainty regarding the idiosyncratic component of firm growth rate explains non-systematic variations in R square. Consistent with the model, data show that the volatility of interest rates represents investor uncertainty and predicts the average in the stock market.

The Determinants of Stock and Bond Return Comovements

We study the economic sources of stock‐bond return comovements and their time variation using a dynamic factor model. We identify the economic factors employing a semistructural regime-switching model for state variables such as interest rates, inflation, the output gap, and cash flow growth. We also view risk aversion, uncertainty about inflation and output, and liquidity proxies as additional potential factors. We find that macroeconomic fundamentals contribute little to explaining stock and bond return correlations but that other factors, especially liquidity proxies, play a more important role. The macro factors are still important in fitting bond return volatility, whereas the “variance premium” is critical in explaining stock return volatility. However, the factor model primarily fails in fitting covariances. (JEL G11, G12, G14, E43, E44) Stock and bond returns in the United States display an average correlation of about 19% during the post-1968 period. Shiller and Beltratti (1992) underestimate the empirical correlation using a present value with constant discount rates, whereas Bekaert, Engstrom, and Grenadier (2005) overestimate it in a consumption-based asset pricing model with stochastic risk aversion. Yet, these models generate realistically positive correlations using economic state variables.

A Simple Indicator of Systemic Risk

We examine the relevance and effectiveness of stock return correlations among financial institutions as an indicator of systemic risk. By analyzing the trends and fluctuations of daily stock return correlations and default correlations among the 22 largest bank holding companies and investment banks from 1988 to 2008, we find that daily stock return correlation is a simple, robust, forward-looking, and timely systemic risk indicator. There is an increasing trend in stock return correlation among banks, whereas there is no obvious correlation trend among non-banks. Correlation spikes tend to predict or coincide with significant economic or market events, especially during the 2007-2008 financial crisis. Furthermore, stock return correlations can offer a perspective on the level of systemic risk in the financial sector that is not already captured by default correlations. Stock return correlations are not subject to data limitations or model specification errors that other potential systemic risk measures may face. Therefore, we recommend that regulators and businesses monitor daily stock return correlations among financial institutions to track the level of systemic risk.

Co-movement of Fundamentals: Structural Changes in the Business Cycle

The co-movement of stocks and of fundamentals changes across the business cycle. Empirical studies have shown that the correlation of stock returns is stronger in crisis. We show that the correlation of fundamentals is the highest during crisis using a large sample of quarterly firm revenues aggregated to industry data from 1969 to 2009. Fundamentals drive the expectation of market participants on stock and bond prices. Structural changes in correlation of fundamentals therefore have implications on diversification decisions in equity portfolio analysis and credit risk management. The higher correlation in times of crisis increases the downside risk and the bankruptcy probability of a portfolio. Both correlations between industries and the aggregate market and correlations between earnings confirm our findings.