In this study we employ augmented and switching time series models to find possible existence of business cycle asymmetries in U.S. stock returns. Our approach is fully parametric and testing strategy is robust to any conditional heteroskedasticity, and outliers that may be present. We also approximate in sample as well as out-of-sample forecasts from artificial neural networks for testing business cycle nonlinearities in U.S. stock returns. Our results based on nonlinear augmented and switching time series models show a strong evidence of business cycle asymmetries in conditional mean dynamics of U.S. stock returns. These results also show that conditional heteroskedasticity is unimportant when testing for asymmetries in conditional mean. Moreover, the conditional volatility in stock returns is asymmetric and is more pronounced in recessions than in expansion phase of business cycles. Similarly, the results based on neural network models show a statistically significant evidence of business cycle nonlinearities in US stock returns. The magnitude of these nonlinearities is more obvious in post World War II era than in the full sample period.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions G19 - Financial Economics - - General Financial Markets - - - Other
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.: