Multifractal Modeling of the US Treasury Term Structure and Fed Funds Rate

Jamdee, Sutthisit; Los, Cornelis A.

doi:10.2139/ssrn.675943

Cited by 4 publications

(13 citation statements)

References 51 publications

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“…As stated by Jamdee and Los [46], the same result can be obtained through the plot of partition function, which is approximately parallel to the horizontal axis at a special q moment. As for the second equation, multifractal spectrum f pαq is the Legendre transformation of the scaling function τ pqq, that is the Legendre transformation enables the obtaining of the multifractal spectrum f pαq.…”

Section: The Multifractal Model Of Asset Returnssupporting

confidence: 73%

“…Similarly, following the same procedure, Fillol [45] showed that MMAR replicates the scaling properties of the French Stock Market (CAC40) Index returns better than other models. In another study, Jamdee and Los [46,47] compared the performance of MMAR with GARCH, FIGARCH and geometric Brownian motion simulations for the scaling properties of the U.S. Treasury rates and the Fed funds rates. More recently, Batten et al [48] analyzed the multifractal features of EUR/USD returns through a modified version of the MMAR model and showed that MMAR outperformed both conditional and unconditional coverage statistics.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…As for the second equation, multifractal spectrum f pαq is the Legendre transformation of the scaling function τ pqq, that is the Legendre transformation enables the obtaining of the multifractal spectrum f pαq. As demonstrated by Jamdee and Los [46], the first two parameters of the lognormal distribution are attained through the following equations:…”

Section: The Multifractal Model Of Asset Returnsmentioning

confidence: 99%

“…The purpose of the empirical investigation is to compare the performance of the GARCH, EGARCH, FIGARCH, MRS-GARCH and MMAR and to determine which model best fits the data in the modeling of stock market index returns. In similar studies, such as Calvet and Fisher [44], Fillol [45] and Jamdee and Los [46], the authors limited the models to normal distributions and used only the GARCH, FIGARCH and GBM in comparing the performance of models with MMAR. In this study, we added the EGARCH and MRS-GARCH models to enhance the model diversity and also used different types of distributions under the FIGARCH and MRS-GARCH models in order to obtain the most efficient results.…”

Section: Empirical Analysismentioning

confidence: 99%

“…In cases where any function has the scaling property, the logarithmic graph of the partition function versus time increments should be approximately linear [46]. In addition, the q value, which is parallel to the horizontal axis, presents the related Hurst exponent H value.…”

Section: Parameter Estimationsmentioning

confidence: 99%

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Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets

Günay

2016

IJFS

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Abstract:In this study, the performance of the Multifractal Model of Asset Returns (MMAR) was examined for stock index returns of four emerging markets. The MMAR, which takes into account stylized facts of financial time series, such as long memory, fat tails and trading time, was developed as an alternative to the ARCH family models. Empirical analysis of the study consists of two sections. In the first section, we estimated the parameters of GARCH, EGARCH, FIGARCH, MRS-GARCH and MMAR for the stock index returns of Croatia, Greece, Poland and Turkey. In the second section, 1000 paths were obtained for each model using Monte Carlo simulations. We then compared the scaling function values of simulated and original time series for different q orders (1-5). According to the obtained results, the MMAR is mostly superior to other models and presents the best replica of the original time series. Another important finding is the achievement of the MRS-GARCH. We found that for lower levels of persistency (long memory) of return series, the performance of the MRS-GARCH excels, and for H = 0.5, it narrowly outperforms the MMAR.

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Section: The Multifractal Model Of Asset Returnssupporting

confidence: 73%

Section: Literature Reviewsmentioning

confidence: 99%

Section: The Multifractal Model Of Asset Returnsmentioning

confidence: 99%

Section: Empirical Analysismentioning

confidence: 99%

Section: Parameter Estimationsmentioning

confidence: 99%

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Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets

Günay

2016

IJFS

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Hurst exponent analysis in Turkish stock market

Gürsakal

Aydin

Gürsakal

et al. 2009

IJSE

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The volatility model of the ASEAN Stock Indexes

Singagerda

Septarina

Sanusi

2019

Investment Management and Financial Innovations

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This research study examines the characteristics of the Association of Southeast Asian Nations (ASEAN) volatility of stock indexes. The following models are used in this research: Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH), Glosten Jaganathan Runkle Generalized Autoregressive Conditional Heteroscedasticity (GJR-GARCH), and Multifractal Model of Asset Return (MMAR). The research also used the data from the ASEAN country members’ (the Philippines, Indonesia, Malaysia, Singapore, and Thailand) stock indexes for the period from January 2002 until 31 January 2016 to determine the suitable model.Meanwhile, the results of the MMAR parameter showed that the returns of the countries have a characteristic called long-term memory. The authors found that the scaling exponents are associated with the characteristics of the specific markets including the ASEAN member countries and can be used to differentiate markets in their stage of development. Finally, the simulated data are compared with the original data by scaling function where most of the stock markets of the selected ASEAN countries have long-term memory with the scaling behavior of information asymmetry. Some of the countries such as the Philippines and Indonesia have their own alternative models using GARCH and EGARCH due to the possibility of leverage. Generally, MMAR is the best model for use in ASEAN market, because this model considered Hurst exponent as a parameter of long-term memory that indicates persistent behavior.

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Multifractal Modeling of the US Treasury Term Structure and Fed Funds Rate

Cited by 4 publications

References 51 publications

Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets

Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets

Hurst exponent analysis in Turkish stock market

The volatility model of the ASEAN Stock Indexes

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