Cogprints

What can we see from Investment Simulation based on Generalized (m,2)-Zipf law?

Situngkir, Mr Hokky and Surya, Dr Yohanes (2005) What can we see from Investment Simulation based on Generalized (m,2)-Zipf law? [Departmental Technical Report] (In Press)

Full text available as:

[img]
Preview
 PDF
805Kb

Abstract

The paper revisits the investment simulation based on strategies exhibited by Generalized (m,2)-Zipf law to present an interesting characterization of the wildness in financial time series. The investigations of dominant strategies on each specific time series shows that longer words dominant in larger time scale exhibit shorter dominant ones in smaller time scale and vice versa. Moreover, denoting the term wildness based on persistence over short term trend and memory represented by particular length of words, we can see how wild historical fluctuations over time series data coped with the Zipf strategies.

Item Type:Departmental Technical Report
Keywords:eneralized (m,2)-Zipf law, time series, fluctuations, investment
Subjects:Computer Science > Statistical Models
Computer Science > Complexity Theory
Electronic Publishing > Economics
ID Code:4337
Deposited By: Situngkir, Mr Hokky
Deposited On:02 May 2005
Last Modified:11 Mar 2011 08:56

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Ausloos, M., and Bronlet, Ph. (2002). "Strategy for Investment from Zipf Law(s)". Physica A 324:30-7.

Ausloos, M., and Ivanova, K. (1999). "Precise (m,k)-Zipf diagram analysis of mathematical and financial time series when m = 6, k=2". Physica A 270:526-542

Baxter, M., and Andrew, R. (1997). Financial Calculus: An Introduction to Derivative Pricing. Cambridge UP.

Bronlet, Ph., and Ausloos, M. (2004). Generalized (m,k)-Zipf Law for Fractional Brownian motion-like Time Series with or without Effect of an additional linear trend. Pre-print:arxiv:cond-mat/0209306

Gammel, B. M. (1998). "Hurst's rescaled range statistical analysis for pseudorandom number generators used in physical simulations". Physical Review E 58(2):2586-97.

Situngkir, H., and Surya, Y., (2004). Agent-Based Model Construction in Financial Economic System. Working Paper WPA2004. Bandung Fe Institute. Pre-print: arxiv:nlin.AO/0403041.

Situngkir, H., and Surya, Y. (2005), Simulasi Investasi dengan Hukum Pangkat Zipf: Analisis Zipf-(m,2) dalam Teks Data Indeks Keuangan. Working Paper WPC2005. Bandung Fe Institute.

Vanderwalle, N., Brisbois, F., and Lefebvre, P.H. (2000). "Managing Both Sign and Size of Fluctuations within the n-Zipf Framework". International Journal of Theoretical and Applied Finance 3(3):409-414.

Metadata

Repository Staff Only: item control page

Cogprints is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.