Surrogate Data Analysis and Stochastic Chaotic Modelling: Application to Stock Exchange Returns Series

Costas Vorlow, Antonios Antoniou and Catherine Kyrtsou

No 27, Computing in Economics and Finance 2004 from Society for Computational Economics

Abstract: We investigate for evidence of complex-deterministic dynamics in financial returns time series. By combining the Surrogate Data Analysis inferential framework with the MG-GARCH (Kyrtsou and Terraza, 2003) modelling approach, we examine whether the sequences are characterized by aperiodic and nonlinear deterministic cycles or pure randomness. Our results support the hypothesis of complex nonlinear and non-stochastic dynamics in the data generating processes. According to our approach, markets can be assumed to be highly complex, high-dimensional, open and dissipative dynamical systems that need feedback as well as other kinds of inputs in order to operate. These inputs may come in the guise of noise or news. The inputs may also control the evolution of the system dynamics and the knowledge of their nature may allow us to forecast the future states of the market with greater accuracy. To this extent the MG-GARCH model provides a valuable insight on how a feedback mechanism can operate within the structure of stock returns processes and explain stylized facts.

Keywords: MG-GARCH; Surrogate Data Analysis; Chaos; Complexity (search for similar items in EconPapers)
JEL-codes: C10 D40 G12 G14 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-fin
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