Abstract
The purpose of this paper is to determine the impact of rare disaster risks, captured by the El Niño-Southern Oscillation (ENSO) cycle, on the volatility of Treasury securities of the United States (US) involving 1- to 360-month maturities. We use a random coefficient panel-data-based heterogeneous autoregressive-realized variance (HAR-RV) model over the monthly period of 1961:06 to 2019:12, with the monthly RV derived from the sum of squared daily changes in yield within a month. Our results show a positive and statistically significant (at the 1% level) impact of the ENSO cycle on RV, with the results being robust to alternative metrics of the ENSO, consideration of lagged values, and decomposition of the ENSO cycle into El Niño and La Niña phases, with the former having a relatively stronger effect. Based on the panel estimation method using heterogeneous slope coefficients, we find that the impact on the entire term-structure is positive yet stronger at the two-ends and the middle-part of the term-structure. Our findings have important implications for investors in US Treasury securities.
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Estimation code will be available upon request.
Notes
We do not use this dataset as it ends in 2016.
The data is downloadable from: https://sites.google.com/view/jingcynthiawu/yield-data?authuser=0.
Conventionally, the time-varying volatility is modelled, and the fit assessed, using various generalized autoregressive conditional heteroscedastic (GARCH) models, under which the conditional variance is a deterministic function of model parameters and past data. Alternatively, some papers consider stochastic volatility (SV) models, where the volatility is a latent variable that follows a stochastic process. Irrespective of whether one uses a GARCH or SV model, the underlying estimate of volatility is not model-free as in the case of RV.
See the discussion of Anthony Barnston of the National Oceanic and Atmospheric Administration here: https://www.climate.gov/news-features/blogs/enso/why-are-there-so-many-enso-indexes-instead-just-one for further details.
The impacts are actually all significant at the 1% or 5% levels, complete details of which are available upon request from the authors.
While a full-fledged forecasting analysis based on recursive and/or rolling window for multi-steps-ahead of the entire yield curve involving maturities of 1 month to 360 months is beyond the scope of this paper, we have conducted a preliminary forecasting experiment, as suggested by an anonymous referee. In this regard, we estimated Eq. (1) with and without EQSOI over the in-sample period of 1961:06 to 2018:12, and produced one-month-ahead forecast for the entire yield curve over 2019:01 to 2019:12, i.e., using a fixed-scheme of estimation involving the model parameters. As can be seen from Figure A1 in the Appendix of the paper, the root mean square errors (RMSEs) for the entire yield curve with EQSOI included in the model are consistently lower than the corresponding RMSEs obtained from the model without the ENSO-predictor. In other words, we do find evidence of the role of EQSOI in producing forecasting gains for the US Treasury securities of maturities associated with 1 month to 360 months.
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RE: Model estimation and writing; RG: conceptualization, supervision, writing; JN: model estimation and writing; EB: data curation and writing.
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We would like to thank two anonymous referees for many helpful comments. Any remaining errors are solely ours
Appendix
Appendix
Out-of-sample forecasting of US Treasury securities with 1- to 360-month maturities due to EQSOI anomalies. Note: See Table 1 column number 2, i.e., Model 1, and also Notes to Table 1. Model 1(a) includes EQSOI anomalies (incl_eqsoi_anom), while Model 1(b) excludes (excl_eqsoi_anom) the same to produce out-of-sample forecasts over the period 2019:01–2019:12, using an in-sample of 1961:06–2018:12. RMSE is the root mean square errors of these two models
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van Eyden, R., Gupta, R., Nel, J. et al. Rare disaster risks and volatility of the term-structure of US Treasury Securities: The role of El Niño and La Niña events. Theor Appl Climatol 148, 383–389 (2022). https://doi.org/10.1007/s00704-021-03910-8
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DOI: https://doi.org/10.1007/s00704-021-03910-8