Evidence for deterministic nonlinear dynamics in financial time series data

M. Small; C. K. Tse

doi:10.1109/CIFER.2003.1196280

Evidence for deterministic nonlinear dynamics in financial time series data

M. Small, C. K. Tse

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

2 Citations (Scopus)

Abstract

Intra-day measurements of three time series (DJIA, gold fixings and USD-JPY exchange rates) are examined for evidence of deterministic nonlinear dynamics. Standard linear surrogate techniques and estimation of dynamic invariants demonstrate that linear noise models are insufficient to explain dynamic variability in intra-day returns. Therefore, the data may not be modeled as a monotonic nonlinear transformation of linearly filtered noise. Furthermore, a new nonlinear surrogate technique is employed to demonstrate that conditional heteroskedastic models are also insufficient to model this data. We conclude that the most likely model of the data is a nonlinear dynamical system driven by high dimensional dynamics (noise).

Original language	English
Title of host publication	2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	339-346
Number of pages	8
Volume	2003-January
ISBN (Electronic)	0780376544
DOIs	https://doi.org/10.1109/CIFER.2003.1196280
Publication status	Published - 1 Jan 2003
Externally published	Yes
Event	2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Hong Kong, China Duration: 20 Mar 2003 → 23 Mar 2003

Conference

Conference	2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003
Country/Territory	China
City	Hong Kong
Period	20/03/03 → 23/03/03

Access to Document

10.1109/CIFER.2003.1196280

Cite this

Small, M., & Tse, C. K. (2003). Evidence for deterministic nonlinear dynamics in financial time series data. In 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings (Vol. 2003-January, pp. 339-346). Article 1196280 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CIFER.2003.1196280

@inproceedings{88be89be619c4ee8abb2443ce34ba200,

title = "Evidence for deterministic nonlinear dynamics in financial time series data",

abstract = "Intra-day measurements of three time series (DJIA, gold fixings and USD-JPY exchange rates) are examined for evidence of deterministic nonlinear dynamics. Standard linear surrogate techniques and estimation of dynamic invariants demonstrate that linear noise models are insufficient to explain dynamic variability in intra-day returns. Therefore, the data may not be modeled as a monotonic nonlinear transformation of linearly filtered noise. Furthermore, a new nonlinear surrogate technique is employed to demonstrate that conditional heteroskedastic models are also insufficient to model this data. We conclude that the most likely model of the data is a nonlinear dynamical system driven by high dimensional dynamics (noise).",

keywords = "Chaos, Contamination, Data analysis, Exchange rates, Extraterrestrial measurements, Noise measurement, Nonlinear dynamical systems, Pollution measurement, Testing, Time measurement",

author = "M. Small and Tse, {C. K.}",

year = "2003",

month = jan,

day = "1",

doi = "10.1109/CIFER.2003.1196280",

language = "English",

volume = "2003-January",

pages = "339--346",

booktitle = "2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 ; Conference date: 20-03-2003 Through 23-03-2003",

}

Small, M & Tse, CK 2003, Evidence for deterministic nonlinear dynamics in financial time series data. in 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings. vol. 2003-January, 1196280, IEEE, Institute of Electrical and Electronics Engineers, pp. 339-346, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003, Hong Kong, China, 20/03/03. https://doi.org/10.1109/CIFER.2003.1196280

Evidence for deterministic nonlinear dynamics in financial time series data. / Small, M.; Tse, C. K.
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings. Vol. 2003-January IEEE, Institute of Electrical and Electronics Engineers, 2003. p. 339-346 1196280.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - Evidence for deterministic nonlinear dynamics in financial time series data

AU - Small, M.

AU - Tse, C. K.

PY - 2003/1/1

Y1 - 2003/1/1

N2 - Intra-day measurements of three time series (DJIA, gold fixings and USD-JPY exchange rates) are examined for evidence of deterministic nonlinear dynamics. Standard linear surrogate techniques and estimation of dynamic invariants demonstrate that linear noise models are insufficient to explain dynamic variability in intra-day returns. Therefore, the data may not be modeled as a monotonic nonlinear transformation of linearly filtered noise. Furthermore, a new nonlinear surrogate technique is employed to demonstrate that conditional heteroskedastic models are also insufficient to model this data. We conclude that the most likely model of the data is a nonlinear dynamical system driven by high dimensional dynamics (noise).

AB - Intra-day measurements of three time series (DJIA, gold fixings and USD-JPY exchange rates) are examined for evidence of deterministic nonlinear dynamics. Standard linear surrogate techniques and estimation of dynamic invariants demonstrate that linear noise models are insufficient to explain dynamic variability in intra-day returns. Therefore, the data may not be modeled as a monotonic nonlinear transformation of linearly filtered noise. Furthermore, a new nonlinear surrogate technique is employed to demonstrate that conditional heteroskedastic models are also insufficient to model this data. We conclude that the most likely model of the data is a nonlinear dynamical system driven by high dimensional dynamics (noise).

KW - Chaos

KW - Contamination

KW - Data analysis

KW - Exchange rates

KW - Extraterrestrial measurements

KW - Noise measurement

KW - Nonlinear dynamical systems

KW - Pollution measurement

KW - Testing

KW - Time measurement

UR - http://www.scopus.com/inward/record.url?scp=84941353562&partnerID=8YFLogxK

U2 - 10.1109/CIFER.2003.1196280

DO - 10.1109/CIFER.2003.1196280

M3 - Conference paper

AN - SCOPUS:84941353562

VL - 2003-January

SP - 339

EP - 346

BT - 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003

Y2 - 20 March 2003 through 23 March 2003

ER -

Evidence for deterministic nonlinear dynamics in financial time series data

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this