Relevant bibliographies by topics / ARFIMA model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'ARFIMA model'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "ARFIMA model"

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Garafutdinov, Robert. "Formation of Investment Portfolios of Two Assets Based on Forecast Returns Using the ARFIMA-GARCH Model." Vestnik Volgogradskogo gosudarstvennogo universiteta. Ekonomika, no. 2 (July 2021): 130–36. http://dx.doi.org/10.15688/ek.jvolsu.2021.2.11.

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The paper tests the hypothesis that the formation of investment portfolios of two assets based on predicted returns obtained using fractal models with conditional heteroscedasticity (ARFIMA-GARCH) allows to obtain portfolios with better characteristics than those obtained using the ARFIMA model. A computational experiment on artificial data and real data from the Russian stock market was carried out. The software implementation of the hypothesis testing algorithm was carried out using Python and R programming languages. The following results were obtained. Average absolute forecast error of th
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Eğri˙oğlu, Erol, and Süleyman Günay. "Bayesian model selection in ARFIMA models." Expert Systems with Applications 37, no. 12 (2010): 8359–64. http://dx.doi.org/10.1016/j.eswa.2010.05.047.

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Szolgayová, Elena, Josef Arlt, Günter Blöschl, and Ján Szolgay. "Wavelet based deseasonalization for modelling and forecasting of daily discharge series considering long range dependence." Journal of Hydrology and Hydromechanics 62, no. 1 (2014): 24–32. http://dx.doi.org/10.2478/johh-2014-0011.

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Abstract Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA) model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series
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Panjaitan, Helmi, Alan Prahutama, and Sudarno Sudarno. "PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)." Jurnal Gaussian 7, no. 1 (2018): 96–109. http://dx.doi.org/10.14710/j.gauss.v7i1.26639.

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Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to
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Diaz, John Francis T. "Do Scarce Precious Metals Equate to Safe Harbor Investments? The Case of Platinum and Palladium." Economics Research International 2016 (January 10, 2016): 1–7. http://dx.doi.org/10.1155/2016/2361954.

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This research establishes the predictability and safe harbor properties of two scarce precious metals, namely, platinum and palladium. Utilizing their spot prices, the study concludes intermediate memory in the return structures of both precious metals, which implies the instability of platinum and palladium returns’ persistency in the long run. However, both the ARFIMA-FIGARCH and the ARFIMA-FIAPARCH models confirm long-memory properties in the volatility of the two spot prices. The leverage effects phenomenon is not also present based on the ARFIMA-APARCH and ARFIMA-FIAPARCH models, which ma
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Masa, Argel S., and John Francis T. Diaz. "Long-memory Modelling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)." Margin: The Journal of Applied Economic Research 11, no. 1 (2017): 23–53. http://dx.doi.org/10.1177/0973801016676012.

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This research provides evidence in determining the predictability of exchange-traded notes (ETNs). It utilises commodity, currency and equity ETNs as data samples, and examines the performance of the three combinations of long-memory models, that is, autoregressive fractionally integrated moving average and generalised autoregressive conditional heteroskedasticity (ARFIMA-GARCH), autoregressive fractionally integrated moving average and fractionally integrated generalised autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) and autoregressive fractionally integrated moving average an
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Boutahar, Mohamed. "Optimal prediction with nonstationary ARFIMA model." Journal of Forecasting 26, no. 2 (2007): 95–111. http://dx.doi.org/10.1002/for.1012.

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Garafutdinov, Robert. "Influence of some ARFIMA model parameters on the accuracy of financial time series forecasting." Applied Econometrics 62 (2021): 85–100. http://dx.doi.org/10.22394/1993-7601-2021-62-85-100.

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The influence of ARFIMA model parameters on the accuracy of financial time series forecasting on the example of artificially generated long memory series and daily log returns of RTS index is investigated. The investigated parameters are deviation of the integration order value from its «true» value, as well as the memory «length» considered by the model. Based on the research results, some practical recommendations for modeling using ARFIMA have been formulated.
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Mah, P. J. W., N. A. M. Ihwal, and N. Z. Azizan. "FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 3, no. 2 (2018): 81. http://dx.doi.org/10.24191/mjoc.v3i2.4887.

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Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product. Since fish forecasting is crucial in fisheries management for managers and scientists, time series modelling can be one useful tool. Time series modelling have been used in many fields of studies including the fields of fisheries. In a previous research, the ARIMA and ARFIMA models were used to model marine fish production in Malaysia and the ARF
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Duppati, Geeta, Anoop S. Kumar, Frank Scrimgeour, and Leon Li. "Long memory volatility in Asian stock markets." Pacific Accounting Review 29, no. 3 (2017): 423–42. http://dx.doi.org/10.1108/par-02-2016-0009.

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Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integ
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Dissertations / Theses on the topic "ARFIMA model"

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Omran, Hayan. "Examining the relationship between trading volume, market return volatility and U.S. aggregate mutual fund flow." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12848.

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This thesis consists of three studies which cover topics in the trading volume-market return volatility linkage, stock market return-aggregate mutual fund flow relationship as well as market return volatility-aggregate mutual fund flow interaction. Chapter 2 investigates the issue of volume-volatility linkage in the US market for the period 1990-2012 (S&P 500) and 1992-2012 (Dow Jones). We construct four sub-samples depending on three different structural points (the Asian Financial Crisis, the Dot-Com Bubble and the 2007 Financial Crisis). By employing univariate and bivariate GARCH processes
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MONROY, N. A. J. "Modelo ARFIMA Espaço-Temporal em Estudos de Poluição do Ar." Universidade Federal do Espírito Santo, 2013. http://repositorio.ufes.br/handle/10/3919.

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Made available in DSpace on 2016-08-29T15:09:50Z (GMT). No. of bitstreams: 1 tese_7242_Tese Nataly Adriana Jimenez Monroy.pdf: 1647826 bytes, checksum: 043d39d2450d7eba488a63e03d4918ad (MD5) Previous issue date: 2013-08-28<br>Nos estudos de polui¸c ao atmosf´erica ´e comum observar dados medidos em diferentes posi¸c oes no espa¸co e no tempo, como ´e o caso da medi¸c ao de concentra¸c oes de poluentes em uma cole¸c ao de esta¸c oes de monitoramento. A din amica desse tipo de observa¸c oes pode ser representada por meio de modelos estat´&#305;sticos que consideram a depend encia entre as obse
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Hauser, Michael A. "Maximum Likelihood Estimators for ARMA and ARFIMA Models. A Monte Carlo Study." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/794/1/document.pdf.

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We analyze by simulation the properties of two time domain and two frequency domain estimators for low order autoregressive fractionally integrated moving average Gaussian models, ARFIMA (p,d,q). The estimators considered are the exact maximum likelihood for demeaned data, EML, the associated modified profile likelihood, MPL, and the Whittle estimator with, WLT, and without tapered data, WL. Length of the series is 100. The estimators are compared in terms of pile-up effect, mean square error, bias, and empirical confidence level. The tapered version of the Whittle likelihood turns out to be a
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SOUZA, LEONARDO ROCHA. "BOOTSTRAP IMPLEMENTATION IN THE PARAMETERS ESTIMATION OF ARFIMA MODELS AND MONTECARLO SIMULATIONS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1997. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8695@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Nesta tese de mestrado, foram analisados aspectos, propriedades, utilidade e desempenho do bootstrap, um método de reamostragem, na estimação de um parâmetro relacionado à memória longa, ou longa dependência, em séries temporais. Entre outras coisas, obtém-se estimativas do desvio-padrão do estimador do parâmetro, e um teste de hipóteses para o parâmetro. O bootstrap pode conseguir propriedades de grandes amostras a partir de um número pequeno de observações. O procedimento do bootsptrap consiste de reamostrar, com repos
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Stocker, Toni Clemens. "On the asymptotic properties of the OLS estimator in regression models with fractionally integrated regressors and errors." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-57370.

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Van, Heerden Petrus Marthinus Stephanus. "The relationship between the forward– and the realized spot exchange rate in South Africa / Petrus Marthinus Stephanus van Heerden." Thesis, North-West University, 2010. http://hdl.handle.net/10394/4511.

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The inability to effectively hedge against unfavourable exchange rate movements, using the current forward exchange rate as the only guideline, is a key inhibiting factor of international trade. Market participants use the current forward exchange rate quoted in the market to make decisions regarding future exchange rate changes. However, the current forward exchange rate is not solely determined by the interaction of demand and supply, but is also a mechanistic estimation, which is based on the current spot exchange rate and the carry cost of the transaction. Results of various studies, inclu
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Ko, Kyungduk. "Bayesian wavelet approaches for parameter estimation and change point detection in long memory processes." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/2804.

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The main goal of this research is to estimate the model parameters and to detect multiple change points in the long memory parameter of Gaussian ARFIMA(p, d, q) processes. Our approach is Bayesian and inference is done on wavelet domain. Long memory processes have been widely used in many scientific fields such as economics, finance and computer science. Wavelets have a strong connection with these processes. The ability of wavelets to simultaneously localize a process in time and scale domain results in representing many dense variance-covariance matrices of the process in a sparse form. A wavel
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Delgado, Júlio António Rocha. "Aplicação empírica da realized volatility ao índice PSI20." Master's thesis, Instituto Superior de Economia e Gestão, 2005. http://hdl.handle.net/10400.5/17746.

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Mestrado em Economia Monetária e Financeira<br>Nesta dissertação é feito um estudo sobre o novo método ou procedimento (não paramétrico) de estimação da volatilidade recentemente proposto na literatura, a Realized Volatility (RV), obtido pela soma dos produtos cruzados dos retornos de alta- frequência intra-diários. O objectivo principal do estudo consiste em fazer uma aplicação empírica da RV ao índice PSI20, focando sobretudo nos estudos das propriedades das distribuições condicionais e não condicionais, confrontando com os resultados já obtidos na literatura. Considerand
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Vdovičenko, Martin. "ARFIMA modely časových řad." Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-323046.

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The thesis deal with long-memory processes which are defined by several ways. The main concern is dedicated to ARFIMA model, to its basic properties and its application. Next, graphical, semiparametric and parametric estimation methods of ARFIMA parameters are described in detail. Five selected R packages are introduced that are suitable for modeling long-memory processes. We discuss their basic functions with description of input arguments and output. Finally, the application of the packages on real data is discussed according to results of~each function. Data sample comes from the Nile River
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"An analysis of the Hong Kong stock market by the ARFIMA-GARCH model." 2001. http://library.cuhk.edu.hk/record=b5890703.

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Cheung Hiu-Yan.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.<br>Includes bibliographical references (leaves 83-87).<br>Abstracts in English and Chinese.<br>ACKNOWLEGMENTS --- p.iii<br>LIST OF TABLES --- p.iv<br>LIST OF ILLUSTRATIONS --- p.vi<br>CHAPTER<br>Chapter ONE --- INTRODUCTION --- p.1<br>Chapter TWO --- THE LITERATURE REVIEW --- p.6<br>The Family of the ARFIMA Process<br>Parameter Estimation of the ARFIMA Process<br>Applications in Economic and Financial Time Series<br>Chapter THREE --- THEORETICAL MODELS AND METHODOLOGY --- p.16<br>Theoretical Models of Long-memory
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Books on the topic "ARFIMA model"

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Smith, Jeremy. Comparing the bias and misspecification in Arfima models. Warwick University, Department of Economics, 1995.

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Book chapters on the topic "ARFIMA model"

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Andrysiak, Tomasz, and Łukasz Saganowski. "Network Anomaly Detection Based on ARFIMA Model." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10662-5_31.

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Andrysiak, Tomasz, Łukasz Saganowski, Michał Choraś, and Rafał Kozik. "Network Traffic Prediction and Anomaly Detection Based on ARFIMA Model." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07995-0_54.

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Ma, Yulin, Xia Li, Jing Zhao, and Dengyue Luo. "Using ARFIMA Model to Calculate and Forecast Realized Volatility of High Frequency Stock Market Index Data." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27711-5_57.

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Corduas, Marcella. "Preliminary estimation of ARFIMA models." In COMPSTAT. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57678-2_28.

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Ercan, Ali, M. Levent Kavvas, and Rovshan K. Abbasov. "Long-Range Dependence and ARFIMA Models." In Long-Range Dependence and Sea Level Forecasting. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01505-7_2.

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LIN, YONG, and KAI WANG. "ARFIMA MODEL AND THE NONLINEAR ANALYSIS OF THE CHINESE SECURITIES MARKETS." In Wavelet Analysis and Active Media Technology. World Scientific Publishing Company, 2005. http://dx.doi.org/10.1142/9789812701695_0224.

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"ARFIMA Models and the Fractional Brownian Motion." In Time Series Analysis. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119132165.ch12.

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Lowe, Kristina Duncombe, and Sarah Eckhardt. "Avoidant/Restrictive Food Intake Disorder." In Applications of the Unified Protocols for Transdiagnostic Treatment of Emotional Disorders in Children and Adolescents, edited by Jill Ehrenreich-May and Sarah M. Kennedy. Oxford University Press, 2021. http://dx.doi.org/10.1093/med-psych/9780197527931.003.0008.

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The Unified Protocol for Transdiagnostic Treatment of Emotional Disorders in Children and Adolescents (UP-C/A) is an innovative intervention that has been adapted for the treatment of youth with avoidant/restrictive food intake disorder (ARFID). When combined with family-based treatment (FBT) for eating disorders, the UP-C/A is a highly customizable treatment model that addresses the multitude of clinical presentations seen in ARFID patients while also flexibly addressing the high rates of comorbid mental health concerns common in these youth. This chapter discusses important considerations when deciding when and how to add the UP-C/A for ARFID patients, as well as how to troubleshoot common barriers in treatment. Important adaptations to the UP-C/A are also presented, including ways to personalize Top Problems and goals, psychoeducation, exposures, and parental involvement for this unique patient population.
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Conference papers on the topic "ARFIMA model"

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Liu, Kai, Xi Zhang, and YangQuan Chen. "An Evaluation of ARFIMA Programs." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67483.

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Strong coupling between values at different time that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The ARFIMA model, which employs the fractional order signal processing techniques, is the generalization of the conventional integer order models — ARIMA and ARMA model. Therefore, it has much wider applications since it could capture both short-range dependence and long range dependence. For now, several software have developed functions dealing with ARFIMA processes. However, it could be a big difference
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Li, Qianru, Christophe Tricaud, Rongtao Sun, and YangQuan Chen. "Great Salt Lake Surface Level Forecasting Using FIGARCH Model." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34909.

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In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we divide the time series in two parts (first 2000 measurement points in Part-1 and the rest is Part-2), we found that for Part-2 data, FIGARCH offers best performance indicating that conditional heteroscedast
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Sheng, Hu, and YangQuan Chen. "The Modeling of Great Salt Lake Elevation Time Series Based on ARFIMA With Stable Innovations." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86864.

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Great Salt Lake (GSL) is the largest salt lake in the western hemisphere, the fourth-largest terminal lake in the world. The elevation of Great Salt Lake has critical effect on the people who live nearby and their properties. It is crucial to build an exact model of GSL elevation time series in order to predict the GSL elevation precisely. Although some models, such as FARIMA or ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedast
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Sun, Rongtao, YangQuan Chen, and Qianru Li. "Modeling and Prediction of Great Salt Lake Elevation Time Series Based on ARFIMA." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34905.

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The elevation of Great Salt Lake (GSL) has a great impact on the people of Utah. The flood of GSL in 1982 has caused a loss of millions of dollars. Therefore, it is very important to predict the GSL levels as precisely as possible. This paper points out the reason why conventional methods failed to describe adequately the rise and fall of the GSL levels — the long-range dependence (LRD) property. The LRD of GSL elevation time series is characterized by some most commonly used Hurst parameter estimation methods in this paper. Then, according to the revealed LRD, the autoregressive fractional in
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Kurul, Zühal, and Pınar Sezer. "The Long Memory Characteristics of Inflation in Turkey and Analysis of Inflation Persistence." In International Conference on Eurasian Economies. Eurasian Economists Association, 2013. http://dx.doi.org/10.36880/c04.00696.

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The aim of this paper is to illustrate the long memory characteristics of the Turkish inflation rates and to analyze the potential inflation persistence. Our empirical analysis is carried out for inflation series of Turkey during the period of 1980-2013. We used the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model and find that inflation in Turkey has long memory properties when structural breaks are not taken into account. When structural changes are considered, the long memory properties show different and ambiguous results. The exogenously identified structural changes h
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Yusof, Fadhilah, Ibrahim Lawal Kane, and Zulkifli Yusop. "Measuring volatility persistence on rainfall records with the hybrid of autoregressive fractional integrated moving average (ARFIMA) - hidden Markov model (HMM)." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907479.

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Jibrin, Sanusi Alhaji, and Rosmanjawati Abdul Rahman. "R package arfurima for fractional unit root integral (FURI) time series, ARFIMA and ARFURIMA models." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136403.

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Goyal, Vipul, Mengyu Xu, Jayanta Kapat, and Ladislav Vesely. "Prediction Enhancement of Machine Learning Using Time Series Modeling in Gas Turbines." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59082.

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Abstract Blade-path temperature can serve as a precursor of anomalies in combustion system and/or cooling system. Given observations from blade-path temperature sensors of a power plant, we consider prediction of the temperature for each sensor. The only extraneous predictor is the combustion turbine fuel flow, while measurements of other potential predictors are unavailable. Long-memory behavior and heterogeneous variance are observed from the residuals of the generalized additive model. Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Generalized Autoregressive Conditional
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PALMA, W. "MISSING VALUES IN ARFIMA MODELS." In Proceedings of the Hong Kong International Workshop on Statistics in Finance. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2000. http://dx.doi.org/10.1142/9781848160156_0008.

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Shalalfeh, Laith, Paul Bogdan, and Edmond Jonckheere. "Modeling of PMU Data Using ARFIMA Models." In 2018 Clemson University Power Systems Conference (PSC). IEEE, 2018. http://dx.doi.org/10.1109/psc.2018.8664019.

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