The benefits of bagging for forecast models of realized volatility

TY - JOUR

T1 - The benefits of bagging for forecast models of realized volatility

AU - Hillebrand, Eric

AU - Medeiros, Marcelo C.

PY - 2010

Y1 - 2010

N2 - This article shows that bagging can improve the forecast accuracy of time series models for realized volatility. We consider 23 stocks from the Dow Jones Industrial Average over the sample period 1995 to 2005 and employ two different forecast models, a log-linear specification in the spirit of the heterogeneous autoregressive model and a nonlinear specification with logistic transitions. Both forecast model types benefit from bagging, in particular in the 1990s part of our sample. The log-linear specification shows larger improvements than the nonlinear model. Bagging the log-linear model yields the highest forecast accuracy on our sample.

AB - This article shows that bagging can improve the forecast accuracy of time series models for realized volatility. We consider 23 stocks from the Dow Jones Industrial Average over the sample period 1995 to 2005 and employ two different forecast models, a log-linear specification in the spirit of the heterogeneous autoregressive model and a nonlinear specification with logistic transitions. Both forecast model types benefit from bagging, in particular in the 1990s part of our sample. The log-linear specification shows larger improvements than the nonlinear model. Bagging the log-linear model yields the highest forecast accuracy on our sample.

KW - Bagging

KW - Boostrap

KW - HAR

KW - Realized volatility

UR - http://www.scopus.com/inward/record.url?scp=77956743996&partnerID=8YFLogxK

U2 - 10.1080/07474938.2010.481554

DO - 10.1080/07474938.2010.481554

M3 - Journal article

AN - SCOPUS:77956743996

SN - 0747-4938

VL - 29

SP - 571

EP - 593

JO - Econometric Reviews

JF - Econometric Reviews

IS - 5

ER -