Term structure forecasting in affine framework with time-varying volatility

Author Affiliation

Wali Ullah is Associate Professor at the Department of Economics and Finance, Institute of Business Administration (IBA), Karachi

Faculty / School

Faculty of Business Administration (FBA)

Department

Department of Economics

Was this content written or created while at IBA?

Yes

Document Type

Article

Source Publication

Statistical Methods and Applications

ISSN

1618-2510

Disciplines

Mathematics | Statistics and Probability

Abstract

This study extends the affine Nelson–Siegel model by introducing the time-varying volatility component in the observation equation of yield curve, modeled as a standard EGARCH process. The model is illustrated in state-space framework and empirically compared to the standard affine and dynamic Nelson–Siegel model in terms of in-sample fit and out-of-sample forecast accuracy. The affine based extended model that accounts for time-varying volatility outpaces the other models for fitting the yield curve and produces relatively more accurate 6- and 12-month ahead forecasts, while the standard affine model comes with more precise forecasts for the very short forecast horizons. The study concludes that the standard and affine Nelson–Siegel models have higher forecasting capability than their counterpart EGARCH based models for the short forecast horizons, i.e., 1 month. The EGARCH based extended models have excellent performance for the medium and longer forecast horizons.

Indexing Information

HJRS - X Category, Scopus, Web of Science - Science Citation Index Expanded (SCI)

Journal Quality Ranking

Impact Factor: 1.437

Publication Status

Published

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