Forecasting Yield Curves in an Adaptive Framework

• Autorzy: Ying Chen , Bo Li
• Języki publikacji: EN

Abstrakty

EN

Forecasting yield curves with regime switches is important in academia and financial industry. As the number of interest rate maturities increases, it poses difficulties in estimating parameters due to the curse of dimensionality. To deal with such a feature, factor models have been developed. However, the existing approaches are restrictive and largely based on the stationarity assumption of the factors. This inaccuracy creates non-ignorable financial risks, especially when the market is volatile. In this paper, a new methodology is proposed to adaptively forecast yield curves. Specifically, functional principal component analysis (FPCA) is used to extract factors capable of representing the features of yield curves. The local AR(1) model with time-dependent parameters is used to forecast each factor. Simulation and empirical studies reveal the superiority of this method over its natural competitor, the dynamic Nelson-Siegel (DNS) model. For the yield curves of the U.S. and China, the adaptive method provides more accurate 6- and 12-month ahead forecasts. (original abstract)

Słowa kluczowe

EN

Forecasting; Yield curve; Estimation; Financial risk; Principal Component Analysis (PCA)

PL

Prognozowanie; Krzywa dochodowości; Estymacja; Ryzyko finansowe; Analizy głównych komponentów

• Czasopismo: Central European Journal of Economic Modelling and Econometrics (CEJEME)
• Rocznik: 2011
• Tom: 3
• Numer: nr 4
• Strony: 237--259

Twórcy

autor

Ying Chen

• National University of Singapore

autor

Bo Li

• National University of Singapore

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Identyfikatory

DOI

DOI: 10.24425/cejeme.2011.119314