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Multifractal models and simulations ...

Jamdee, Sutthisit.

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Multifractal models and simulations of the United States term structure.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multifractal models and simulations of the United States term structure./
Author:	Jamdee, Sutthisit.
Description:	162 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-04, Section: A, page: 1417.
Contained By:	Dissertation Abstracts International66-04A.
Subject:	Business Administration, Banking. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3171095
ISBN:	0542075911

Multifractal models and simulations of the United States term structure.
Jamdee, Sutthisit.

Multifractal models and simulations of the United States term structure. - 162 p.

Source: Dissertation Abstracts International, Volume: 66-04, Section: A, page: 1417.

Thesis (Ph.D.)--Kent State University, 2005.

Asset pricing modelers attempt to identify price diffusion processes from empirical financial market data. In particular, the Geometric Brownian Motion and the GARCH models are currently popular in these efforts. In contrast, for the first time this dissertation identifies Multifractal Models of Asset Return (MMARs) from the eight nodal term structure series of US Treasury rates as well as Fed Funds rate and, after proper synthesis, simulates those MMARs. The model performance results of these simulations are then compared with not only the original empirical time series, but also with the simulated results from the corresponding Brownian Motion and GARCH processes. The major findings are that the eight different maturity US Treasury and the Fed Funds rates are multifractal processes. The MMAR outperforms both the GBM and GARCH (1,1) in terms of scaling distribution preservation over time and investment horizons. In addition, this dissertation uses the nose-data ratio to improve the Holder-Hurst identification for the power spectrum method. Identified distributions of all simulated processes are compared with the empirical distributions in snapshot and over time-scale (frequency) analyses. The findings suggest that the simulated MMAR can replicate all attributes of the empirical distributions, while the simulated GBM and GARCH (1,1) processes cannot preserve the thick-tails, high peaks, and skewness. The wavelet scalograms, used to investigate the variance over time and scales, reveal the superiority of the MMAR for modeling the Treasury rates over the GBM and GARCH (1,1). When the MMAR is applied to the Fed Funds rate, the results are surprisingly different from those of the Treasury rates. The MMAR at this stage cannot produce a complete term structure model, because it cannot completely model the dynamic structure of the term structure.

ISBN: 0542075911Subjects--Topical Terms:

1018458
Business Administration, Banking.

Multifractal models and simulations of the United States term structure.
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100 1 $a Jamdee, Sutthisit. $3 1905581
245 1 0 $a Multifractal models and simulations of the United States term structure.
300 $a 162 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-04, Section: A, page: 1417.
500 $a Adviser: Cornelis A. Los.
502 $a Thesis (Ph.D.)--Kent State University, 2005.
520 $a Asset pricing modelers attempt to identify price diffusion processes from empirical financial market data. In particular, the Geometric Brownian Motion and the GARCH models are currently popular in these efforts. In contrast, for the first time this dissertation identifies Multifractal Models of Asset Return (MMARs) from the eight nodal term structure series of US Treasury rates as well as Fed Funds rate and, after proper synthesis, simulates those MMARs. The model performance results of these simulations are then compared with not only the original empirical time series, but also with the simulated results from the corresponding Brownian Motion and GARCH processes. The major findings are that the eight different maturity US Treasury and the Fed Funds rates are multifractal processes. The MMAR outperforms both the GBM and GARCH (1,1) in terms of scaling distribution preservation over time and investment horizons. In addition, this dissertation uses the nose-data ratio to improve the Holder-Hurst identification for the power spectrum method. Identified distributions of all simulated processes are compared with the empirical distributions in snapshot and over time-scale (frequency) analyses. The findings suggest that the simulated MMAR can replicate all attributes of the empirical distributions, while the simulated GBM and GARCH (1,1) processes cannot preserve the thick-tails, high peaks, and skewness. The wavelet scalograms, used to investigate the variance over time and scales, reveal the superiority of the MMAR for modeling the Treasury rates over the GBM and GARCH (1,1). When the MMAR is applied to the Fed Funds rate, the results are surprisingly different from those of the Treasury rates. The MMAR at this stage cannot produce a complete term structure model, because it cannot completely model the dynamic structure of the term structure.
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650 4 $a Business Administration, General. $3 1017457
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792 $a 2005
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