The predictive density simulation of the yield curve with a zero lower bound

Kang, Kwi Young

doi:10.1016/j.jempfin.2015.06.002

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Learning can also prolong the non‐linear relationship between asset prices and macroeconomic factors for years after the global financial crisis (Kozlowski et al, 2016). To account for the zero lower bound in interest rates, Kang (2015) proposes an alternative specification applicable for the DNS model. However, the absence of a linear dependence between macroeconomic factors and government bond prices does not necessarily suggest that the connection between these two has disappeared.…”

Section: Related Literaturementioning

confidence: 99%

The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic

Salachas,

Kouretas,

Laopodis

2024

Journal of Forecasting

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This paper tests the accuracy and predictability of two term structure models using both yields‐only and factor‐augmented specifications focusing on the recent COVID‐19 crisis. In addition, we test the predictive ability of the yield curve on economic activity for the United States and other advanced countries. We provide evidence that models with an enhanced information set, including COVID‐19 factors, improve interest rate forecasts for this period. Also, we point out that term structure models can determine future variations in economic activity but are time‐ and country‐sensitive. Finally, out‐of‐sample analysis reveals that the use of factor‐augmented term structure models, to reflect the current economic and market conditions, improves their forecasting accuracy.

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Section: Related Literaturementioning

confidence: 99%

The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic

Salachas,

Kouretas,

Laopodis

2024

Journal of Forecasting

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show abstract

“…For further discussion on and comparison of the DNS and arbitrage-free Nelson-Siegel (AFNS) models, seeDiebold and Rudebusch (2013).5 Related work ofKang (2015) andAbdymomunov et al (2016) does impose a ZLB restriction onto the DNS model, but this essentially boils down to a Bayesian estimation approach that restricts yields to be non-negative and does not reflect the idea of the shadow-rate framework.…”

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confidence: 99%