Meteor Showers or Heat Waves? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market
Published Date
Publisher
Center for Economic Research, Department of Economics, University of Minnesota
Type
Abstract
This paper defines and tests a form of market efficiency called market
dexterity which requires that asset prices adjust instantaneously and completely
in response to new information. Examining the behavior of the yen/dollar
exchange rate while each of the major markets are open it is possible to test
for informational effects from one market to the next. Assuming that news has
only country specific autocorrelation such as a heat wave. any intra-daily
volatility spillovers (meteor showers) become evidence against market dexterity.
ARCH models are employed to model heteroskedasticity across intra-daily market
segments. Statistical tests lead to the rejection of the heat wave and therefore
the market dexterity hypothesis. Using a volatility type of vector
autoregression we examine the impact of news in one market on the time path of
volatility in other markets.
Keywords
Description
Related to
item.page.replaces
License
Series/Report Number
Discussion Paper
246
246
Funding Information
item.page.isbn
DOI identifier
Previously Published Citation
Engle, R.F., Ito, T. and Lin, W., (1988), "Meteor Showers or Heat Waves? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market", Discussion Paper No. 246, Center for Economic Research, Department of Economics, University of Minnesota.
Other identifiers
Suggested Citation
Engle, Robert F.; Ito, Takatoshi; Lin, Wen-Ling. (1988). Meteor Showers or Heat Waves? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/55524.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.