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Relevant bibliographies by topics / Financial time series Hurst exponent analysis

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Academic literature on the topic 'Financial time series Hurst exponent analysis'

Author: Grafiati

Published: 9 March 2023

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Journal articles on the topic "Financial time series Hurst exponent analysis"

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SANG, Hong-wei, Tian Ma, and Shuo-zhong Wang. "Hurst exponent analysis of financial time series." Journal of Shanghai University (English Edition) 5, no. 4 (2001): 269–72. http://dx.doi.org/10.1007/s11741-001-0037-1.

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Střelec, Luboš. "Searching for long memory effects in time series of central Europe stock market indices." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 56, no. 3 (2008): 187–200. http://dx.doi.org/10.11118/actaun200856030187.

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This article deals with one of the important parts of applying chaos theory to financial and capital markets – namely searching for long memory effects in time series of financial instruments. Source data are daily closing prices of Central Europe stock market indices – Bratislava stock index (SAX), Budapest stock index (BUX), Prague stock index (PX) and Vienna stock index (ATX) – in the period from January 1998 to September 2007. For analysed data R/S analysis is used to calculate the Hurst exponent. On the basis of the Hurst exponent is characterized formation and behaviour of analysed finan

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EVERTSZ, CARL J. G. "FRACTAL GEOMETRY OF FINANCIAL TIME SERIES." Fractals 03, no. 03 (1995): 609–16. http://dx.doi.org/10.1142/s0218348x95000539.

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A simple quantitative measure of the self-similarity in time-series in general and in the stock market in particular is the scaling behavior of the absolute size of the jumps across lags of size k. A stronger form of self-similarity entails that not only this mean absolute value, but also the full distributions of lag-k jumps have a scaling behavior characterized by the above Hurst exponent. In 1963, Benoit Mandelbrot showed that cotton prices have such a strong form of (distributional) self-similarity, and for the first time introduced Lévy’s stable random variables in the modeling of price r

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Zhuravka, Fedir, Hanna Filatova, Petr Šuleř, and Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis." Investment Management and Financial Innovations 18, no. 1 (2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from Decembe

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CHAKRABORTI, A., and M. S. SANTHANAM. "FINANCIAL AND OTHER SPATIO-TEMPORAL TIME SERIES: LONG-RANGE CORRELATIONS AND SPECTRAL PROPERTIES." International Journal of Modern Physics C 16, no. 11 (2005): 1733–43. http://dx.doi.org/10.1142/s0129183105008230.

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In this paper, we review some of the properties of financial and other spatio-temporal time series generated from coupled map lattices, GARCH(1,1) processes and random processes (for which analytical results are known). We use the Hurst exponent (R/S analysis) and detrended fluctuation analysis as the tools to study the long-time correlations in the time series. We also compare the eigenvalue properties of the empirical correlation matrices, especially in relation to random matrices.

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Mnif, Emna, Bassem Salhi, and Anis Jarboui. "Herding behaviour and Islamic market efficiency assessment: case of Dow Jones and Sukuk market." International Journal of Islamic and Middle Eastern Finance and Management 13, no. 1 (2019): 24–41. http://dx.doi.org/10.1108/imefm-10-2018-0354.

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Purpose The purpose of this paper is to present the Islamic stock and Sukuk market efficiency and focus on the presence of investor herding behaviour (HB) captured by Hurst exponent estimation. Design/methodology/approach The Hurst exponent was estimated with various methods. The authors studied the evolving efficiency of the “Dow Jones” indices from 1 January 2010 to 30 December 2016 using a rolling sample of the Hurst exponent. In addition, they used a time-varying parameter method based on the Hurst of delayed returns. After that, the robust Hurst method was considered. In the next step, th

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FERNÁNDEZ-MARTÍNEZ, M., M. A. SÁNCHEZ-GRANERO, M. J. MUÑOZ TORRECILLAS, and BILL MCKELVEY. "A COMPARISON OF THREE HURST EXPONENT APPROACHES TO PREDICT NASCENT BUBBLES IN S&P500 STOCKS." Fractals 25, no. 01 (2017): 1750006. http://dx.doi.org/10.1142/s0218348x17500062.

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Since the pioneer contributions due to Vandewalle and Ausloos, the Hurst exponent has been applied by econophysicists as a useful indicator to deal with investment strategies when such a value is above or below [Formula: see text], the Hurst exponent of a Brownian motion. In this paper, we hypothesize that the self-similarity exponent of financial time series provides a reliable indicator for herding behavior (HB) in the following sense: if there is HB, then the higher the price, the more the people will buy. This will generate persistence in the stocks which we shall measure by their self-sim

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Yujun, Yang, Li Jianping, and Yang Yimei. "Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy." International Journal of Modern Physics C 28, no. 02 (2017): 1750028. http://dx.doi.org/10.1142/s0129183117500280.

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This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characte

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Wątorek, Marcin, and Bartosz Stawiarski. "Log-Periodic Power Law and Generalized Hurst Exponent Analysis in Estimating an Asset Bubble Bursting Time." e-Finanse 12, no. 3 (2016): 49–58. http://dx.doi.org/10.1515/fiqf-2016-0001.

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Abstract We closely examine and compare two promising techniques helpful in estimating the moment an asset bubble bursts. Namely, the Log-Periodic Power Law model and Generalized Hurst Exponent approaches are considered. Sequential LPPL fitting to empirical financial time series exhibiting evident bubble behavior is presented. Estimating the critical crash-time works satisfactorily well also in the case of GHE, when substantial „decorrelation“ prior to the event is visible. An extensive simulation study carried out on empirical data: stock indices and commodities, confirms very good performanc

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Sidorov, Sergei, Alexey Faizliev, and Vladimir Balash. "Measuring long-range correlations in news flow intensity time series." International Journal of Modern Physics C 28, no. 08 (2017): 1750103. http://dx.doi.org/10.1142/s0129183117501030.

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We consider the flow intensity of economic and financial news taken from a nine-month period of 2015. This data is found to be well approximated by a persistent self-affine walk. It is characterized by a Hurst exponent of [Formula: see text] over three orders of magnitude in time ranging from minutes to several days. In this paper, we use the Detrended Fluctuation Analysis (DFA) of order 1, Rescaled Range Analysis (R/S) and Fourier Transform Method (FTM) to examine long-range auto-correlation and self-similarity of time series of news flow intensity. DFA method allowed us to reveal a strong sc

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Dissertations / Theses on the topic "Financial time series Hurst exponent analysis"

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Bovina, Dario. "Scaling and modelization of financial time series." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426464.

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This thesis is devoted to the characterization of the invariance under rescaling of financial time series. Our main result is that the multiscaling observed until now in many financial time series could be a spurious effect. We proved that a probabilistic mechanism working in the empirical statistical analysis of a single time series and based on the power law tails of the density function of the returns, can affect the outcoming Hurst exponent and leads to a strong spurious multiscaling even for a strictly simple scaling underlying process. Since this effect is due only to the availability of

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Vilhanová, Vanda. "Aplikace R/S analýzy na finančních trzích." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-10996.

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The aim of this graduation thesis is the descriptiton of R/S analysis and it's aplication on chosen time series of share prices and exchange rates. Some main models of financial time series will be mentioned in the second chapter. There will described basic linear models of stationary and non stationary time series and models of volatility. Then we will focus on the main theme of this thesis, R/S analysis. The algorithm of R/S analysis and the interpretation of the Hurst exponent will be described in the forth chapter. In the fifth chapter, the R/S analysis will by applied on real data sets. T

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Sawaya, Antonio. "Financial time series analysis : Chaos and neurodynamics approach." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4810.

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This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provid

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Zeman, Martin. "Hurstův exponent a náhodnost v časových řadách." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-75449.

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The main goal of this thesis is to test the ability of the Hurst exponent to recognise some processes with deterministic signal as nonrandom and to test the randomness of daily stock returns of three stocks traded in BCPP. Critical values to determine the critical region of a randomness hypothesis test were set for this purpose. Another goal of the thesis is the description of the Hurst exponent estimation by means of Rescaled Range Analysis and outline some problems accompanying this estimation if the Hurst exponent would be used as a randomness indicator. Within the frame of Rescaled Range A

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Diniz, Natália. "O impacto da janela de Hurst na previsão de séries temporais financeiras." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/96/96133/tde-20122011-165838/.

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Sabe-se que, na literatura, existem muitos modelos para se fazer previsão para séries temporais financeiras. Sabe-se também que não há um modelo perfeito e que os mais utilizados atualmente são os modelos de redes neurais recorrentes e os da família GARCH. Referências internacionais apontam que existe uma técnica de medição de uma janela temporal para se identificar o tipo de comportamento existente em uma série temporal; tal técnica é conhecida como Expoente de Hurst. É uma medida que qualifica a série como persistente ou anti-persistente. Este trabalho analisou se o Expoente de Hurst, interf

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Ellis, Craig. "An investigation of long-term dependence in time-series data." Thesis, View thesis, 1998. http://handle.uws.edu.au:8081/1959.7/242.

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Traditional models of financial asset yields are based on a number of simplifying assumptions. Among these are the primary assumptions that changes in asset yields are independent, and that the distribution of these yields is approximately normal. The development of financial asset pricing models has also incorporated these assumptions. A general feature of the pricing models is that the relationship between the model variables is fundamentally linear. Recent empirical research has however identified the possibility for these relations to be non-linear. The empirical research focused primarily

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Raimundo, Milton Saulo. "Desenvolvimento de um modelo adaptativo baseado em um sistema SVR-Wavelet híbrido para previsão de séries temporais financeiras." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-13072018-143525/.

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A necessidade de antecipar e identificar variações de acontecimentos apontam para uma nova direção nos mercados de bolsa de valores e vem de encontro às análises das oscilações de preços de ativos financeiros. Esta necessidade leva a argumentar sobre novas alternativas na predição de séries temporais financeiras utilizando métodos de aprendizado de máquinas e vários modelos têm sido desenvolvidos para efetuar a análise e a previsão de dados de ativos financeiros. Este trabalho tem por objetivo propor o desenvolvimento de um modelo de previsão adaptativo baseado em um sistema SVR-wavelet híbrid

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Karangwa, Innocent. "Comparing South African financial markets behaviour to the geometric Brownian Motion Process." Thesis, University of the Western Cape, 2008. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_4787_1363778247.

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<p>This study examines the behaviour of the South African financial markets with regards to the Geometric Brownian motion process. It uses the daily, weekly, and monthly stock returns time series of some major securities trading in the South African financial market, more specifically the US dollar/Euro, JSE ALSI Total Returns Index, South African All Bond Index, Anglo American Corporation, Standard Bank, Sasol, US dollar Gold Price , Brent spot oil price, and South African white maize near future. The assumptions underlying the&nbsp<br>Geometric Brownian motion in finance, namely the stationa

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Madison, Guy. "Functional modelling of the human timing mechanism." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2001. http://publications.uu.se/theses/91-554-5012-1/.

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Smigelski, Jeffrey Ralph. "Water Level Dynamics of the North American Great Lakes:Nonlinear Scaling and Fractional Bode Analysis of a Self-Affine Time Series." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1379087351.

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Book chapters on the topic "Financial time series Hurst exponent analysis"

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Soskin, Marat, and Vasyl Vasil’ev. "Dynamic Singular Vector Speckle Fields and Their Hurst Exponent Time Analysis." In Springer Series in Optical Sciences. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7315-7_8.

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Li, Xinjuan, Jie Yu, Lingyu Xu, and Gaowei Zhang. "Time Series Classification with Deep Neural Networks Based on Hurst Exponent Analysis." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_21.

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Bal, Anirban, Debayan Ganguly, and Kingshuk Chatterjee. "Stationarity and Self-similarity Determination of Time Series Data Using Hurst Exponent and R/S Ration Analysis." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4367-2_57.

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Lawal Kane, Ibrahim, and Venkatesan Madha Suresh. "Fractal Scaling Properties in Rainfall Time Series: A Case of Thiruvallur District, Tamil Nadu, India." In Groundwater Management and Resources. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.100631.

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In the present study, the features of rainfall time series (1971–2016) in 9 meteorological regions of Thiruvallur, Tamil Nadu, India that comprises Thiruvallur, Korattur_Dam, Ponneri, Poondi, Red Hills, Sholingur, Thamaraipakkam, Thiruvottiyur and Vallur Anicut were studied. The evaluation of rainfall time series is one of the approaches for efficient hydrological structure design. Characterising and identifying patterns is one of the main objectives of time series analysis. Rainfall is a complex phenomenon, and the temporal variation of this natural phenomenon has been difficult to characterise and quantify due to its randomness. Such dynamical behaviours are present in multiple domains and it is therefore essential to have tools to model them. To solve this problem, fractal analysis based on Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) analysis were employed. The fractal analysis produces estimates of the magnitude of detrended fluctuations at different scales (window sizes) of a time series and assesses the scaling relationship between estimates and time scales. The DFA and (R/S) gives an estimate known as Hurst exponent (H) that assumes self-similarity in the time series. The results of H exponent reveals typical behaviours shown by all the rainfall time series, Thiruvallur and Sholingur rainfall region have H exponent values within 0.5 < H < 1 which is an indication of persistent behaviour or long memory. In this case, a future data point is likely to be followed by a data point preceding it; Ponneri and Poondi have conflicting results based on the two methods, however, their H values are approximately 0.5 showing random walk behaviour in which there is no correlation between any part and a future. Thamaraipakkam, Thiruvottiyur, Vallur Anicut, Korattur Dam and Red Hills have H values less than 0.5 indicating a property called anti-persistent in which an increase will tend to be followed by a decrease or vice versa. Taking into consideration of such features in modelling, rainfall time series could be an exhaustive rainfall model. Finding appropriate models to estimate and predict future rainfalls is the core idea of this study for future research.

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Conference papers on the topic "Financial time series Hurst exponent analysis"

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Alperovich, Y., M. Alperovich, and A. Spiro. "Analysis methods of financial time series based on the use of the hurst exponent." In 2017 Tenth International Conference Management of Large-Scale System Development (MLSD). IEEE, 2017. http://dx.doi.org/10.1109/mlsd.2017.8109589.

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Kumar, J., P. Manchanda, A. H. Siddiqi, M. Brokate, and A. K. Gupta. "ESTIMATION OF HURST EXPONENT FOR THE FINANCIAL TIME SERIES." In MODELLING OF ENGINEERING AND TECHNOLOGICAL PROBLEMS: International Conference on Modelling and Engineering and Technological Problems (ICMETP) and the 9th Biennial National Conference of Indian Society of Industrial and Applied Mathematics (ISIAM). AIP, 2009. http://dx.doi.org/10.1063/1.3183547.

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Guozhi Wang. "Notice of Retraction: Time-dependent Hurst exponent in financial time series in China financial market." In 2010 2nd International Conference on Advanced Computer Control (ICACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5486883.

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Gospodinova, Evgeniya. "Fractal Time Series Analysis by Using Entropy and Hurst Exponent." In CompSysTech '22: International Conference on Computer Systems and Technologies 2022. ACM, 2022. http://dx.doi.org/10.1145/3546118.3546133.

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Shimazu, Kohei, and Ikusaburo Kurimoto. "Hurst Exponent Analysis for Differenced Time Series of EEG Signal at The Metacognition Occurrence During Answer Induced Task." In 2022 61st Annual Conference of the Society of Instrument and Control Engineers (SICE). IEEE, 2022. http://dx.doi.org/10.23919/sice56594.2022.9905803.

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Reports on the topic "Financial time series Hurst exponent analysis"

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Soloviev, Vladimir, Andrii Bielinskyi, Oleksandr Serdyuk, Victoria Solovieva, and Serhiy Semerikov. Lyapunov Exponents as Indicators of the Stock Market Crashes. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4131.

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The frequent financial critical states that occur in our world, during many centuries have attracted scientists from different areas. The impact of similar fluctuations continues to have a huge impact on the world economy, causing instability in it concerning normal and natural disturbances [1]. The an- ticipation, prediction, and identification of such phenomena remain a huge chal- lenge. To be able to prevent such critical events, we focus our research on the chaotic properties of the stock market indices. During the discussion of the re- cent papers that have been devoted to the chaotic beh

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