Learning-Induced Securities Price Volatility
This paper tests whether the high average returns on the S&P 500 index in recent history can be attributed to mistaken expectations (the ex-ante risk premium -- taken to be constant -- is systematically less than the ex-post measured risk premium), or, alternatively, whether can they be explained as the result of selection bias (the U.S. experience is exceptional). The tests reject these hypotheses over the periods 1/81 to 12/97 (p = 0.02), and 1/41-12/60 (p = 0.03). They do not reject over the periods 1/28-12/40 and 1/61-12/80. The tests are based on a bound that the ex-post Sharpe ratios impose on the volatility of the ratio of the market's prior and posterior beliefs about future outcomes. The bound derives from a property of Bayesian learning first noted in an earlier paper. Qualitatively, for the bound not to be violated, higher absolute mean excess returns may need to be accompanied with higher volatility. This should be interpreted as predicting that large price movements (positive as well as negative) may have to be erratic. We confirm this prediction for the S&P 500 data.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||05 Jul 2000|
|Date of revision:|
|Contact details of provider:|| Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain|
Fax: +34 93 542 17 46
Web page: http://enginy.upf.es/SCE/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:sce:scecf0:299. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.