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NONPARAMETRIC MODELING OF THE CROSS-MARKET FEEDBACK EFFECT

NONPARAMETRIC MODELING OF THE CROSS-MARKET FEEDBACK EFFECT. Kernel-Based Estimation with Uncorrelated Innovation Process Kernel-Based Estimation with Autocorrelated Innovation Process Kernel and bandwidth selection Applications References.

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NONPARAMETRIC MODELING OF THE CROSS-MARKET FEEDBACK EFFECT

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  1. NONPARAMETRIC MODELING OF THE CROSS-MARKET FEEDBACK EFFECT

  2. Kernel-Based Estimation with Uncorrelated Innovation Process • Kernel-Based Estimation with Autocorrelated Innovation Process • Kernel and bandwidth selection • Applications • References

  3. Kernel-Based Estimation with Uncorrelated Innovation Process

  4. Kernel-Based Estimation with Autocorrelated Innovation Process - 1

  5. Kernel-Based Estimation with Autocorrelated Innovation Process - 2 m(x)=(1-0.5x)(1-0.8x)(1-x)(1-1.2x) (1)

  6. Kernel-Based Estimation with Autocorrelated Innovation Process - 3

  7. Kernel-Based Estimation with Autocorrelated Innovation Process - 4

  8. Kernel-Based Estimation with Autocorrelated Innovation Process - 5

  9. Kernel-Based Estimation with Autocorrelated Innovation Process - 6 Opsomer, Wang and Yang (2000) Carroll, Linton, Mammen and Xiao (2002)

  10. Kernel-Based Estimation with Autocorrelated Innovation Process - 7 1. Calculate a preliminary estimate of m: 2. Calculate the corresponding residuals: 3. Consider a -th order autoregression of 4. Calculate an approximation of 5. The estimator of m(x) then is:

  11. Kernel and bandwidth selection - 1

  12. Kernel and bandwidth selection - 2

  13. Applications

  14. Application 1

  15. Application 2

  16. Application 3

  17. Application 4

  18. Application 5

  19. Application 6

  20. Application 7

  21. References • Alexander, C. (2001), Market Models: A Guide to Financial Data Analysis, John Wiley & Sons, Chichester, UK • Andersen, T. G., T. Bollerslev and F. X. Diebold (2003), “Some Like It Smooth, and Some Like It Rough: Untangling Continuous and Jump Components in Measuring, Modeling and Forecasting asset Return Volatility”, PIER Working Paper 03-025 • Andersen, T. G., T. Bollerslev and F. X. Diebold (2002), “Parametric and Nonparametric Volatility Measurement”, NBER Technical Working Paper 279 • Andersen, T. G., T. Bollerslev, F. X. Diebold and P. Labys (2001), “Modeling and Forecasting Realized Volatility”, NBER Working Paper 8160 • Campbell, J. Y., A. W. Lo and A. C. MacKinlay (1997), The Econometrics of Financial Markets, Princeton University Press, Princeton, New Jersey • Carroll, R. J., O. B. Linton, E. Mammen, Z. Xiao (2002), “More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors”, Discussion Paper Nr. EM/02/435, The Suntory Centre, London School of Economics and Political Science • Gasser, Th. (2001), “Practical and Theoretical Aspects of Nonparametric Function Fitting”, Euroworkshop on Statistical Modeling 2001, Universitat Zurich • Green, W. H. (1993), “Econometric Analysis”, Macmillan Publishing Company, New York • Neumann, M. H. (1995), “Automatic Bandwidth Choice and Confidence Intervals in Nonparametric Regression”, The Annals of Statistics, Vol. 23, No. 6 • Opsomer, J., Y. Wang and Y. Yang (2000), “Nonparametric Regression with Correlated Errors”, Manuscript • Pagan, A. and A. Ullah (1999), Nonparametric Econometrics, Cambridge University Press, Cambridge, UK • Pyndick, R. S. and D. L. Rubinfeld (1998), Econometric Models and Economic Forecasts, McGraw-Hill, Singapore • Ruppert, D., A. P. Wand, U. Holst and O. Hossjer (1995), “Local Polinomial Variance Function Estimation”, School of Operations Research and Industrial Engineering, Cornell University

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