The investigator studies the continuous-time fractionally-integrated Auto-Regressive Moving-Average processes and their variants, based on recent advances in stochastic calculus of fractional Brownian motion. Such models provide a general framework for analyzing univariate or multivariate discrete-time data sampled from an underlying strongly-dependent continuous-time process. In particular, the investigator develops methods for studying volatility, fractional co-integration and temporal aggregation of long-memory time series data.

Time series data are data collected sequentially over time, and they abound in science and other fields, e.g., finance. The investigator studies new methods for analyzing time series data with long-memory temporal patterns. The developed methodologies furnish general tools for analyzing changes in the volatility pattern in the data, exploring structural relationships within a set of time series data, and assessing effects of aggregating the data over longer observational periods. These methods have applications in various fields, e.g., pricing of financial derivatives.

Agency
National Science Foundation (NSF)
Institute
Division of Mathematical Sciences (DMS)
Application #
0405267
Program Officer
Gabor J. Szekely
Project Start
Project End
Budget Start
2004-06-01
Budget End
2008-05-31
Support Year
Fiscal Year
2004
Total Cost
$187,359
Indirect Cost
Name
University of Iowa
Department
Type
DUNS #
City
Iowa City
State
IA
Country
United States
Zip Code
52242