On estimability of parsimonious term structure models: an experiment with the Nelson–Siegel specification

Virmani, Vineet

doi:10.1080/13504851.2012.657343

Cited by 5 publications

(2 citation statements)

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“…It is well known that with slightly different 'fixed' values of 𝜆, the coefficients b i,𝓁 can take on very different, often erratically behaved, values, limiting their usefulness and interpretability. Such findings are discussed in Vermani (2012) and Annaert et al (2013), among others. The Nelson-Siegel model is highly nonlinear in its four parameters, which may cause estimation difficulties.…”

Section: Introductionmentioning

confidence: 72%

Inference in functional factor models with applications to yield curves

Horváth

Kokoszka

VanderDoes

et al. 2022

Journal Time Series Analysis

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This article develops a set of inferential methods for functional factor models that have been extensively used in modelling yield curves. Our setting accommodates both temporal dependence and heteroskedasticity. First, we introduce an estimation approach based on minimizing the least-squares loss function and establish the consistency and asymptotic normality of the estimators. Second, we propose a goodness-of-fit test that allows us to determine whether a specific model fits the data. We derive the asymptotic distribution of the test statistics, and this leads to a significance test. A simulation study establishes the good finite-sample performance of our inferential methods. An application to US and UK yield curves demonstrates the generality of our framework, which can accommodate both sparsely and densely observed yield curves.

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Section: Introductionmentioning

confidence: 72%

Inference in functional factor models with applications to yield curves

Horváth

Kokoszka

VanderDoes

et al. 2022

Journal Time Series Analysis

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“…However, surprisingly, the SNS model does not perform better than the DNS model even here. The static form of NS models is documented to suffer from the possibility of local minima and flat likelihood surface during the optimization (Virmani, 2012), which might influence the in‐sample fit and the parameter estimates' stability both. The difference in the RWMSE across the four models is marginal (in the range of 0.1%–0.3%) for most of the stocks.…”

Section: Resultsmentioning

confidence: 99%

Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach

Kumar

Agarwalla

Varma

et al. 2023

Journal of Futures Markets

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While there is a large literature on modeling volatility smile in options markets, most such studies are eventually focused on the forecasting performance of the model parameters and not on the applicability of the models in a trading environment. Drawing on the analogy of volatility smile like a term structure in the context of interest rates in fixed‐income markets, we evaluate the performance of the Dynamic Nelson–Siegel (DNS) approach to modeling the dynamics of volatility smile in a trading environment against competing alternatives. Using model‐based mispricing as our sorting criterion, and deploying a trading strategy of going long the options in the upper deciles and going short the options in the lower deciles, we show that dynamic models consistently outperform their static counterparts, with the worst dynamic model outperforming the best static model in terms of the percentage of mean returns from the trading portfolios and the Sharpe ratio. Specifically, we find that the DNS model consistently outperforms all other competing specifications on most of our selected criteria.

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