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1.
This paper examines the interrelations and time-varying correlations for eight assets. One-year rolling correlations reveal that each of the 28 correlations exhibit both positive and negative values. Linear regressions reveal that given macroeconomic and financial variables contain predictive power for different asset return correlations. The term structure of interest rates and consumer sentiment feature as prominent predictor variables. Structural break tests and non-linear regressions indicate a cycling of correlations between high and low risk periods. In seeking to consider the economic content of the interrelations, we construct a safe and risky portfolio and show that the correlation between these portfolios can allow for improved market timing. Further, the safe and risky portfolio returns and correlation exhibit predictive power for macroeconomic conditions and may be used in a leading indicator role. The results presented here should be of interest to investors and policy-makers as well as academics wishing to examine the relations between both asset returns and financial and real markets.  相似文献   

2.
We investigate the impacts of policy and information shocks on the correlation of China’s T-bond and stock returns, using originally the asymmetric dynamic conditional correlation (DCC) model that allows for the coexistence of opposite-signed asymmetries. The co-movements of China’s capital markets react to large macroeconomic policy shocks as evidenced by structural breaks in the correlation following the drastic 2004 macroeconomic austerity. We show that the T-bond market and the bond–stock correlations bear more of the brunt of the macroeconomic contractions. We also find that the bond–stock correlations respond more strongly to joint negative than joint positive shocks, implying that investors tend to move both the T-bond and stock prices in the same direction when the two asset classes have been hit concurrently by bad news, but tend to shift funds from one asset class to the other when hit concurrently by good news. However, the stock–stock correlation is found to increase for joint positive shocks, indicating that investors tend to herd more for joint bullish than joint bearish stock markets in Shanghai and Shenzhen.  相似文献   

3.
We develop a tactical asset allocation strategy that incorporates the effects of macroeconomic variables. The joint distribution of financial asset returns and the macroeconomic variables is modelled using a VAR with a multivariate GARCH (M-GARCH) error structure. As a result, the portfolio frontier is time varying and subject to contagion from the macroeconomic variable. Optimal asset allocation requires that this be taken into account. We illustrate how to do this using three risky UK assets and inflation as a macroeconomic factor. Taking account of inflation generates portfolio frontiers that lie closer to the origin and offers investors superior risk-return combinations.  相似文献   

4.
This paper considers a new approach of analyzing asset dependence by estimating how the distributions (in particular, quantiles) of assets are related. Combining the techniques of quantile regression and copula modeling, I propose the Copula Quantile-on-Quantile Regression approach to estimate the correlation that is associated with the quantiles of asset returns, which is able to uncover obscure nonlinear characteristics in asset dependence. The estimation procedure proposed here can also be used for analyzing dependence structures in other settings, such as for studying how macroeconomic covariates are nonlinearly related by looking at the relationship between their quantiles.  相似文献   

5.
Investors have access to a large array of structured and unstructured data. We consider how these data can be incorporated into financial decisions through the lens of the canonical asset allocation decision. We characterize investor preference for simplicity in models of the data used in the asset allocation decision. The simplicity parameters then guide asset allocation along with the usual risk aversion parameter. We use three distinct and diverse macroeconomic data sets to implement the model to forecast equity returns (the equity risk premium). The data sets we use are (a) price‐dividend ratios, (b) an array of macroeconomic series, and (c) text data from the Federal Reserve's Federal Open Market Committee (FOMC) meetings.  相似文献   

6.
We attempt to better understand the varying correlations between stock and bond returns across countries and over sample periods using international data. The observation is that there are two forces that affect the correlation between stock and bond returns. The force that drives a positive correlation is identified as the income effect. The force that drives a negative correlation is identified as the substitution effect. In combination, the two effects help determine the actual correlation between stock and bond returns. We contribute to the literature by proposing an empirical method, the structural vector autoregression (VAR) identification method, to identify the two—income and substitution—effects and to measure the relative importance of the two effects that determine the actual net relation between the two asset returns. We further provide some evidence that the income and substitution effects are related to, among other things, the size of the financial market, the growth and volatility (risk) of the economy, and the business cycle over time. In addition, the framework of the income and substitution effects helps us better understand the automatic stabilizing effects of the dynamic optimal asset allocation during business cycles.  相似文献   

7.
《Journal of Banking & Finance》2006,30(10):2747-2765
We use an affine asset pricing model to jointly value stocks and bonds. This enables us to derive endogenous correlations and to explain how economic fundamentals influence the correlation between stock and bond returns. The presented model is implemented for G7 post-war economies and its in-sample and out-of-sample performance is assessed by comparing the correlations generated by the model with conventional statistical measures. The affine framework developed in this paper is found to generate stock–bond correlations that are in line with empirically observed figures.  相似文献   

8.
Postwar U.S. data are characterized by negative correlations between real equity returns and inflation and by positive correlations between real equity returns and money growth. These patterns are closely matched quantitatively by an equilibrium monetary asset pricing model. The model also implies negative correlations between expected asset returns and expected inflation, and it predicts that the inflation-asset return correlation will be more strongly negative when inflation is generated by fluctuations in real economic activity than when it is generated by monetary fluctuations.  相似文献   

9.
International consumption risk sharing studies often generate counterfactual implications for asset return behavior with potentially misleading results. We address this contradiction using data moments of consumption and asset returns to fit a canonical international consumption risk sharing framework. Introducing persistent consumption risk, we find that its correlation across countries is more important for risk sharing than that of transitory risk. To identify these risk components, we jointly exploit the comovement of equity returns and consumption. This identification implies high correlations in persistent consumption risk, suggesting a strong degree of existing risk sharing despite low consumption correlations in the data.  相似文献   

10.
How important are volatility fluctuations for asset prices and the macroeconomy? We find that an increase in macroeconomic volatility is associated with an increase in discount rates and a decline in consumption. We develop a framework in which cash flow, discount rate, and volatility risks determine risk premia and show that volatility plays a significant role in explaining the joint dynamics of returns to human capital and equity. Volatility risk carries a sizable positive risk premium and helps account for the cross section of expected returns. Our evidence demonstrates that volatility is important for understanding expected returns and macroeconomic fluctuations.  相似文献   

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