Nonlinearity, Breaks, and Long-Range Dependence in Time Series Models

TY - JOUR

T1 - Nonlinearity, Breaks, and Long-Range Dependence in Time Series Models

AU - Hillebrand, Eric

AU - Medeiros, Marcelo C.

PY - 2016/1/2

Y1 - 2016/1/2

N2 - We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. We develop asymptotic theory for parameter estimation and propose two model-building procedures. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in financial volatility. An out-of-sample analysis shows that modeling these effects can improve forecast performance. Supplementary materials for this article are available online.

AB - We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. We develop asymptotic theory for parameter estimation and propose two model-building procedures. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in financial volatility. An out-of-sample analysis shows that modeling these effects can improve forecast performance. Supplementary materials for this article are available online.

KW - Forecasting

KW - Long memory

KW - Realized variance

KW - Smooth transitions

UR - http://www.scopus.com/inward/record.url?scp=84954523649&partnerID=8YFLogxK

U2 - 10.1080/07350015.2014.985828

DO - 10.1080/07350015.2014.985828

M3 - Journal article

SN - 0735-0015

VL - 34

SP - 23

EP - 41

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

IS - 1

ER -