Arbitrage-Free Nelson-Siegel

Author(s):  
Francis X. Diebold ◽  
Glenn D. Rudebusch

This chapter discusses a new class of affine arbitrage-free models that overcome the problems with empirical implementation of the canonical affine arbitrage-free model. This new class is based on the dynamic Nelson–Siegel model (DNS) and retains its empirical tractability. Thus, from one perspective, the chapter takes the theoretically rigorous but empirically problematic affine arbitrage-free model and makes it empirically tractable by incorporating DNS elements. From an alternative perspective, it takes the DNS model and makes it theoretically more satisfactory. DNS is simple and stable to estimate, and it is quite flexible and fits both the cross section and time series of yields remarkably well. However, DNS fails on an important theoretical dimension: It does not impose the restrictions necessary to eliminate opportunities for riskless arbitrage. The lack of freedom from arbitrage motivated Diebold et al. (2005) and Christensen et al. (2011) to introduce the class of arbitrage-free Nelson–Siegel (AFNS) yield curve models, which are affine arbitrage-free term structure models that nevertheless maintain the DNS factor-loading structure.

Econometrica ◽  
1969 ◽  
Vol 37 (3) ◽  
pp. 552
Author(s):  
V. K. Chetty

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Rex W. Douglass ◽  
Thomas Leo Scherer ◽  
Erik Gartzke

AbstractOne of the main ways we try to understand the COVID-19 pandemic is through time series cross section counts of cases and deaths. Observational studies based on these kinds of data have concrete and well known methodological issues that suggest significant caution for both consumers and produces of COVID-19 knowledge. We briefly enumerate some of these issues in the areas of measurement, inference, and interpretation.


2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou ◽  
Chia-Yi Yen

AbstractIn this paper, we investigate the dynamic link between recessions and stock market liquidity by examining the predictive content of illiquidity for US recessions. After controlling for other commonly featured recession predictors such as term spreads and credit spreads, we find that the illiquidity measure proposed by (Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”


2010 ◽  
Vol 18 (3) ◽  
pp. 293-294 ◽  
Author(s):  
Nathaniel Beck

Carter and Signorino (2010) (hereinafter “CS”) add another arrow, a simple cubic polynomial in time, to the quiver of the binary time series—cross-section data analyst; it is always good to have more arrows in one's quiver. Since comments are meant to be brief, I will discuss here only two important issues where I disagree: are cubic duration polynomials the best way to model duration dependence and whether we can substantively interpret duration dependence.


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