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Is There More to Long Memory in Fixed-Income Excess Returns and Volatility than Structural Instability?

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  • Robert A. Connolly, Nuray G½ner, and Kenneth N. Hightower

Abstract

Unlike equity returns, many fixed-income return and volatility measures appear to display considerable long memory. Connolly and G½ner (working paper, 1999) show this holds particularly strongly for shorter-maturity Treasury securities in the U.S. They show that fixed-income return and volatility series for other G-7 countries also display long memory. In a number of recent papers, Granger has argued that long memory in returns may only reflect infrequent structural breaks. He finds the case for long memory in volatility much stronger. Parke (Review of Economics and Statistics, 2000) develops a model that generates long memory through a type of structural shift. In this paper, we show that the extent of long memory depends crucially on whether gross or excess returns are under consideration and we provide a simple demonstration of why this distinction is so important. We also carefully explore the impact of structural instability on tests for long memory using a version of the supLM test developed by Andrews. Briefly, we find that evidence of long memory in both gross and excess returns is very considerably weakened after accounting for structural shifts. By contrast, we find that long memory in excess return volatility appears to exist even after controlling for structural shifts. Finally, our estimates of the long memory parameter for excess return volatility series shift from the nonstationary to the stationary range once we account for structural shifts. As a part of the analysis, we also compare the performance of different estimators of long memory volatility model parameters. We conclude the paper with some discussion of the implications of long memory in excess return volatility for asset pricing tests and risk management.

Suggested Citation

  • Robert A. Connolly, Nuray G½ner, and Kenneth N. Hightower, 2001. "Is There More to Long Memory in Fixed-Income Excess Returns and Volatility than Structural Instability?," Computing in Economics and Finance 2001 223, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:223
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    References listed on IDEAS

    as
    1. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-927, July.
    2. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    3. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics,in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038 Elsevier.
    4. White, Halbert, 1982. "Instrumental Variables Regression with Independent Observations," Econometrica, Econometric Society, vol. 50(2), pages 483-499, March.
    5. Fiorentini, Gabriele & Calzolari, Giorgio & Panattoni, Lorenzo, 1996. "Analytic Derivatives and the Computation of GARCH Estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(4), pages 399-417, July-Aug..
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
    8. Nelson, Daniel B & Cao, Charles Q, 1992. "Inequality Constraints in the Univariate GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 229-235, April.
    9. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    10. repec:bla:restud:v:65:y:1998:i:3:p:361-93 is not listed on IDEAS
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    More about this item

    Keywords

    long memory; structural instability; fixed-income returns; fixed-income volatility;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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