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Frontiers in financial dynamics

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  • Carey, Michelle
  • Gath, Eugene G.
  • Hayes, Kevin

Abstract

In recent decades, mathematical motivated financial models have been used to understand the complexity and intermittent nature of financial market instruments. Typically, applied mathematics models a physical system by specifying and quantifying the physical laws to which the process should theoretically conform. Such theoretical models are often represented as differential equations. The solutions of these differential equations have been shown to have poor compliance with observed financial data which has been attributed to difficulties in correctly estimating the parameters of the differential equation. Generalised smoothing provides a comprehensive evaluation of financial dynamics as it accurately estimates data driven parameters for differential equations and produces a fitted curve that incorporates the theoretical specifications implied by the differential equation while adhering to the observed financial data. This article demonstrates the merit for a generalised smoothing approach to modeling financial dynamics by examining instantaneous forward yield curves within a generalised smoothing framework.

Suggested Citation

  • Carey, Michelle & Gath, Eugene G. & Hayes, Kevin, 2014. "Frontiers in financial dynamics," Research in International Business and Finance, Elsevier, vol. 30(C), pages 369-376.
    • Handle: RePEc:eee:riibaf:v:30:y:2014:i:c:p:369-376
    DOI: 10.1016/j.ribaf.2012.08.006
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    References listed on IDEAS

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    1. John H. Wood, 1983. "Do yield curves normally slope up? The term structure of interest rates, 1862–1982," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 7(Jul), pages 17-23.
    2. Qiang Dai & Kenneth Singleton, 2003. "Term Structure Dynamics in Theory and Reality," The Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 631-678, July.
    3. Jiguo Cao & James Ramsay, 2007. "Parameter cascades and profiling in functional data analysis," Computational Statistics, Springer, vol. 22(3), pages 335-351, September.
    4. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
    5. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
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    Cited by:

    1. Mudakkar, Syeda Rabab & Uppal, Jamshed Y., 2018. "Stability of cross-market bivariate return distributions during financial turbulence," Research in International Business and Finance, Elsevier, vol. 45(C), pages 389-401.

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