Applications of Methods and Algorithms of Nonlinear Dynamics in Economics and Finance

摘要: The traditional financial econometric studies presume the underlying data generating processes (DGP) of time series observations to be linear and stochastic. These assumptions were taken face value for a long time; however, recent advances in dynamical systems theory algorithms have enabled researchers observe complicated dynamics data, test validity these assumptions. developments include delay embedding state space reconstruction system from scalar series, methods detecting chaotic by computation invariants such as Lyapunov exponents correlation dimension, surrogate analysis well other testing nonlinearity, mutual prediction method synchronization oscillating systems. In this chapter, we will discuss methods, review empirical results authors chapter undertaken over last decade half. Given methodological computational decades, explored possibility nonlinear, deterministic that examined. We conjectured presence nonlinear may been blurred strong noise which could give appearance randomness series. Accordingly, using dynamics, aimed tackle set lingering problems linear, stochastic approaches econometrics unable address successfully. believe our successfully addressed some, if not all, issues. present many chapter.