IDEAS home Printed from https://ideas.repec.org/p/boc/bocoec/320.html
   My bibliography  Save this paper

Nonlinear Nonparametric Prediction of the 90-Day T-Bill Rate

Author

Listed:
  • John Barkoulas

    () (Boston College)

  • Christopher F. Baum

    () (Boston College)

  • Joseph Onochie

    (Baruch College)

Abstract

We employ a nonlinear, nonparametric method to model the stochastic behavior of changes in the 90-day U.S. T-bill rate. The estimation technique is locally weighted regression (LWR), a nearest-neighbor method, and the forecasting criteria are the root mean square error (RMSE) and mean absolute deviation (MAD). We compare the forecasting performance of the nonparametric fit to the performance of two benchmark linear models: an autoregressive model and a random-walk-with-drift model. The nonparametric fit results in significant improvements in forecasting accuracy as compared to benchmark linear models both in-sample and out-of-sample, thus establishing the presence of substantial nonlinear mean predictability of changes in the 90-day T-bill rate.

Suggested Citation

  • John Barkoulas & Christopher F. Baum & Joseph Onochie, 1996. "Nonlinear Nonparametric Prediction of the 90-Day T-Bill Rate," Boston College Working Papers in Economics 320., Boston College Department of Economics.
  • Handle: RePEc:boc:bocoec:320
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/EC-P/wp320.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    2. Hamilton, James D., 1988. "Rational-expectations econometric analysis of changes in regime : An investigation of the term structure of interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 385-423.
    3. David Heath & Robert Jarrow & Andrew Morton, 2008. "Bond Pricing And The Term Structure Of Interest Rates: A New Methodology For Contingent Claims Valuation," World Scientific Book Chapters,in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 13, pages 277-305 World Scientific Publishing Co. Pte. Ltd..
    4. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters,in: Theory Of Valuation, chapter 8, pages 229-288 World Scientific Publishing Co. Pte. Ltd..
    5. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
    6. James M. Nason, 1988. "The equity premium and time-varying risk behavior," Finance and Economics Discussion Series 11, Board of Governors of the Federal Reserve System (U.S.).
    7. Hodrick, Robert J., 1989. "Risk, uncertainty, and exchange rates," Journal of Monetary Economics, Elsevier, vol. 23(3), pages 433-459, May.
    8. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    9. Brock, W.A. & Dechert, W.D. & LeBaron, B. & Scheinkman, J.A., 1995. "A Test for Independence Based on the Correlation Dimension," Working papers 9520, Wisconsin Madison - Social Systems.
    10. Richard Baldwin & Richard K. Lyons, 1988. "The Mutual Amplification Effect of Exchange Rate Volatility and Unresponsive Trade Prices," NBER Working Papers 2677, National Bureau of Economic Research, Inc.
    11. Meese, Richard A & Rose, Andrew K, 1990. "Nonlinear, Nonparametric, Nonessential Exchange Rate Estimation," American Economic Review, American Economic Association, vol. 80(2), pages 192-196, May.
    12. Abel, Andrew B., 1988. "Stock prices under time-varying dividend risk : An exact solution in an infinite-horizon general equilibrium model," Journal of Monetary Economics, Elsevier, vol. 22(3), pages 375-393.
    13. Hsieh, David A, 1991. " Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    14. Christopher A. Sims, 1980. "Martingale-Like Behavior of Prices," NBER Working Papers 0489, National Bureau of Economic Research, Inc.
    15. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    16. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    17. Pfann, Gerard A. & Schotman, Peter C. & Tschernig, Rolf, 1996. "Nonlinear interest rate dynamics and implications for the term structure," Journal of Econometrics, Elsevier, vol. 74(1), pages 149-176, September.
    18. Longstaff, Francis A & Schwartz, Eduardo S, 1992. " Interest Rate Volatility and the Term Structure: A Two-Factor General Equilibrium Model," Journal of Finance, American Finance Association, vol. 47(4), pages 1259-1282, September.
    19. Cox, John C & Ingersoll, Jonathan E, Jr & Ross, Stephen A, 1985. "An Intertemporal General Equilibrium Model of Asset Prices," Econometrica, Econometric Society, vol. 53(2), pages 363-384, March.
    20. Cleveland, William S. & Devlin, Susan J. & Grosse, Eric, 1988. "Regression by local fitting : Methods, properties, and computational algorithms," Journal of Econometrics, Elsevier, vol. 37(1), pages 87-114, January.
    21. Anderson, Heather M, 1997. "Transaction Costs and Non-linear Adjustment towards Equilibrium in the US Treasury Bill Market," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(4), pages 465-484, November.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    interest rates; T-bill rate; forecasting; long memory;

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:bocoec:320. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum). General contact details of provider: http://edirc.repec.org/data/debocus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.