Modeling volatility changes in the 10-year Treasury
AbstractThis paper examines the daily volatility of changes in the 10-year Treasury note utilizing the iterated cumulative sums of squares algorithm [C. Inclan, G. Tiao, Use of cumulative sums of squares for retrospective detection of changes of variance, J. Am. Stat. Assoc. 89 (1994) 913–923]. The ICSS algorithm can detect regime shifts in the volatility of the interest rate changes. A general model allows for endogenously determined changes in variance while the more restrictive model forces the variance to follow the same process throughout the sample period. A comparison of the out-of-sample volatility forecasting performance of two competing models is made using asymmetric error measures. The asymmetric error statistics penalize models for under- or over-predicting volatility. The results shed light on the importance of ignoring volatility regime shifts when performing out-of-sample forecasts. The findings are important to financial market participants who require accurate forecasts of future volatility in order to implement and evaluate asset performance.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 369 (2006)
Issue (Month): 2 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Forecasting; Volatility; Asymmetric forecast evaluation; Interest rate; Regime shifts;
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