This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 31
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract - #309237
Title: Predicting Long-Horizon Returns Using Historical Volatility: Statistical Significance and Modeling
Author(s): Natalia Sizova*+
Companies: Rice University
Address: Baker Hall, 253 , Houston, TX, 77251-1892,
Keywords: asset-return predictability ; fractional integration
Abstract:

This paper uses the information from risk-return regressions to compare short-memory versus long-memory modeling frameworks of financial volatility. We show that while short- and long-memory models are equally successful in explaining the autocorrelation structure of financial volatility, only the latter can explain both the higher predictability of returns over longer horizons and the lower predictability of volatility.

This puzzling combination of higher predictability of returns and lower predictability of volatility was documented by Bandi and Perron (2008) and is re-examined in this paper. Further, this paper offers an econometric framework that is best suited for the modeling of the long-run predictability in returns when the predicting variable is volatility. Once the framework is established, it is used to test the statistical significance of the predictability in returns.


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