Relevant bibliographies by topics / FIGARCH

Contents

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

Academic literature on the topic 'FIGARCH'

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

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Journal articles on the topic "FIGARCH"

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Al-Hajieh, Heitham. "Evaluated the Success of Fractionally Integrated-GARCH Models on Prediction Stock Market Return Volatility in Gulf Arab Stock Markets." International Journal of Economics and Finance 9, no. 7 (June 22, 2017): 200. http://dx.doi.org/10.5539/ijef.v9n7p200.

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This paper evaluated the different Fractionally Integrated-GARCH Models (FIGARCH BBM's, FIGARCH Chung, FIEGARCH, FIAPARCH BBM's, FIAPARCH Chung, and HYGARCH). This is the first research to use six different Fractionally Integrated-GARCH Models. Most research compares one of Fractionally Integrated-GARCH Models with the traditional GARCH, EGARCH, GJG-GARCH, IGARCH, and APGARCH. To do so, daily returns of Gulf Cooperation Council (GCC) Stock Markets analyzed, covering the period 1995 to 2015. Both the Superior Predictive Ability and the Model Confidence Set tests were used to identify the best fitting models of each country. The results reveal that FIGARCH BBM is the best fitting model for UAE, KSA, and Bahrain. FIEGARCH is the best fitting model for Kuwait. FIGARCH Chung is the best fitting model for Qatar. Only the results for Oman were mixed between FIGARCH BBM and FIAPARCH BBM models.
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De Moraes, Alex Sandro Monteiro, Antonio Carlos Figueiredo Pinto, and Marcelo Cabus Klotzle. "Previsão de value-at-risk e expected shortfall para mercados emergentes usando modelos FIGARCH." Brazilian Review of Finance 13, no. 3 (November 16, 2015): 394. http://dx.doi.org/10.12660/rbfin.v13n3.2015.53080.

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This paper compares the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting volatility for calculating value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock indices. We used daily data from 1999 to 2014 and an adaptation of the Monte Carlo simulation to estimate VaR and ES forecasts for multiple steps ahead (1, 10 and 20 days ), using FIGARCH and GARCH models for four errors distributions. The results suggest that, in general, the FIGARCH models improve the accuracy of forecasts for longer horizons; that the error distribution used may influence the decision about the best model; and that only for FIGARCH models the occurrence of underestimation of the true VaR is less frequent with increasing time horizon. However, the results suggest that rolling sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models.
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Yilmaz, Adil, and Gazanfer Unal. "Chaoticity Properties of Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes." Bulletin of Mathematical Sciences and Applications 15 (May 2016): 69–82. http://dx.doi.org/10.18052/www.scipress.com/bmsa.15.69.

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Fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) arises in modeling of financial time series. FIGARCH is essentially governed by a system of nonlinear stochastic difference equations.In this work, we have studied the chaoticity properties of FIGARCH (p,d,q) processes by computing mutual information, correlation dimensions, FNNs (False Nearest Neighbour), the largest Lyapunov exponents (LLE) for both the stochastic difference equation and for the financial time series by applying Wolf’s algorithm, Kant’z algorithm and Jacobian algorithm. Although Wolf’s algorithm produced positive LLE’s, Kantz’s algorithm and Jacobian algorithm which are subsequently developed methods due to insufficiency of Wolf’s algorithm generated negative LLE’s constantly.So, as well as experimenting Wolf’s methods’ inefficiency formerly pointed out by Rosenstein (1993) and later Dechert and Gencay (2000), based on Kantz’s and Jacobian algorithm’s negative LLE outcomes, we concluded that it can be suggested that FIGARCH (p,d,q) is not deterministic chaotic process.
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Brunetti, Celso, and Christopher L. Gilbert. "Bivariate FIGARCH and fractional cointegration." Journal of Empirical Finance 7, no. 5 (December 2000): 509–30. http://dx.doi.org/10.1016/s0927-5398(00)00021-9.

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Gabe, João, and Marcelo Savino Portugal. "Volatilidade implícita versus volatilidade estatística: um exercício utilizando opções e ações da Telemar S.A." Brazilian Review of Finance 2, no. 1 (January 1, 2004): 47. http://dx.doi.org/10.12660/rbfin.v2n1.2004.1135.

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The main goal this article was to find the best way of making forecast about future volatility using implicit or statistic forecast. The work is based on Telemar S.A. shares data from 21/09/1998 to 21/10/2002 and Telemar S.A. shares data from 2/10/2000 to 21/10/2002. The implicit volatility was obtained using back-out procedure from the Black-Scholes model. The statistics forecasts were obtained using weighted moving average models, GARCH, EGARCH and FIGARCH models. The Wald statistic shows that EGARCH and FIGARCH models are efficient and are not biased forecasts for Telemar S.A. absolute variation between t and t + 1. The volatility evaluation during the maturity time of an option, rejects the hypothesis that implicit volatility is the best forecast to future volatility and the Wald statistic show that FIGARCH model is an efficient and not biased forecast.
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Lan, Feng, and Bao Hua Chen. "Research on the Long-Term Memory of Commodity Housing Price Volatility Based on the FIGARCH Model." Advanced Materials Research 1079-1080 (December 2014): 1194–98. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.1194.

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The purpose of this paper is to test whether there exists a long-term memory volatility characteristics of housing price. The paper based on the data ranging of Zhengzhou from January 2004 to May 2014, by adopting the FIGARCH model, empirically studies and analysis this characteristics. The research results indicate that the price fluctuation of Zhengzhou commodity homes exist effect of cluster and long-term memory characteristic. FIGARCH model can capture the long memory well, and can predict the future price of commodity residential house for a period of time .Therefore, FIGARCH model can well catch long-term memory and forecast the commodity housing price in the future period of time, which illustrates that external shocks have long-standing impact on the volatility of commodity housing price as well, reaching the conclusion that long-effect Mechanism of regulation and control should be set and developed during the macro-control of the government.
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Belkhouja, Mustapha, and Mohamed Boutahary. "Modeling volatility with time-varying FIGARCH models." Economic Modelling 28, no. 3 (May 2011): 1106–16. http://dx.doi.org/10.1016/j.econmod.2010.11.017.

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Briones Zúñiga, José Luis. "Evaluación de modelos de volatilidad con memoria larga." Pesquimat 23, no. 2 (December 28, 2020): 1–8. http://dx.doi.org/10.15381/pesquimat.v23i2.19342.

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El objetivo del estudio es comparar los modelos de memoria larga para modelar la volatilidad del tipo de cambio. Para dicho objetivo se utiliza el tipo de cambio nominal sol/dolar cubriendo los periodos desde el 19 de julio de 1999 hasta el 19 de noviembre del 2013. Escencialmente se busca examinar la capacidad de predicción entre los modelos de memoria larga y comportamiento hiperbólico de las autocorrelaciones dadas por FIGARCH, HYGARCH e IGARCH y concluyendo que el modelo FIGARCH(1,0.637,1) utilizando una distribución t-Student posee una mejor capacidad de predicción. La predicción de la volatilidad del tipo de cambio en el caso de Perú, es estructuralmente importante en el cálculo del Valor en riesgo (VaR) y en la administración de riesgos.
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Lee, Ji Hyeon, Dong Seog Kim, and Hoe Gyeong Lee. "Long memory in the volatility of Korean stock returns." Journal of Derivatives and Quantitative Studies 10, no. 2 (November 30, 2002): 95–114. http://dx.doi.org/10.1108/jdqs-02-2002-b0004.

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In this paper, we empirically examine the volatility process of Korean stock market returns using the KOSPI200. To investigate the property of the process, we use the FIGARCH (Fractionally Integrated GARCH) model that includes GARCH and 1GARCH processes as special cases. Since the FIGARCH model allows fractional integration order, it can detect hyperbolically decaying volatility processes with cannot be explained by existing models with integer integration order. The result shows that the KOSPI200 exhibits long-term dependencies. To investigate the robustness of the obtained result, we analyze the time and cross-sectional aggregation effect using weekly data and individual stock returns that the KOSPI200 is comprised of. The long memory property of the KOSPI200 does not seem to be spuriously induced by aggregation.
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YAO, JING, ZHONG-FEI LI, and KAI W. NG. "MODEL RISK IN VaR ESTIMATION: AN EMPIRICAL STUDY." International Journal of Information Technology & Decision Making 05, no. 03 (September 2006): 503–12. http://dx.doi.org/10.1142/s021962200600209x.

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This paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk.
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Dissertations / Theses on the topic "FIGARCH"

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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, we find positive (negative) bidirectional linkages between these two aforementioned variables in various cases of the estimation, while a mixed one is observed in the remainder of these cases. Chapter 3 examines the issue of temporal ordering of the range-based stock market return (S&P 500 index) and aggregate mutual fund flow in the U.S. market for the period 1998-2012. We construct nine sub-samples represented by three fundamental cases of the whole data set. In addition, we take into consideration three essential indicators when splitting the whole data set, which are the 2000 Dot-Com Bubble, the 2007 Financial Crisis as well as the 2009 European Sovereign Debt Crisis. We examine the dynamics of the return-flow interaction by employing bivariate VAR model with various specifications of GARCH approach. Our principal findings display a bidirectional mixed feedback between stock market return and aggregate mutual fund flow for the majority of the sub-samples obtained. Nevertheless, we provide limited evidence of a positive bi-directional causality between return and flow. Chapter 4 investigates the dynamic relation between S&P 500 return volatility and U.S. aggregate mutual fund flow for the period spanning between 1998 and 2012. We assess the dynamics of the volatility-flow linkage by employing a bivariate VAR model with the GARCH approach which allows for long memory in the mean and the variance equations. In addition to the sub-samples obtained in chapter 3, we generate two measurements of volatility. Our baseline results indicate a variety of bidirectional mixed causalities between market return volatility and aggregate mutual fund flow in several sub-samples. In addition, we observe a negative/positive bi-directional relationship between volatility and flow in the rest of the sub-periods. Summarizing, a range of our findings are in line with the empirical underpinnings that most likely predict a significant linkage between the aforementioned variables. Finally, most of the bidirectional effects are found to be quite robust to the dynamics of the various GARCH processes employed in this thesis.
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Marcinkevičius, Matas. "Finansų rinkų statistinis tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2008. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20080619_130404-39498.

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Keliami uždaviniai: GARCH modelių klasės taikymas ilgo periodo finansiniams duomenims: modelių parametrų paieška, jų vertinimas, testavimas ir taikymas. Ilga atmintis sąlyginiame variantiškume yra viena iš empirinių savybių, kurią turi daugelis finansinių laiko eilučių. Viena modelių klasė, kuri atvaizduoja šį elgesį yra vadinama Dalinai Integruotu GARCH (Baillie, Bollerslev ir Mikkelsen 1996). Dalinės integracijos idėją pateikė ir ją pritaikė GARCH struktūrai Granger (1980) ir Hosking (1981). Šiame darbe bus surastos analitinės FIGARCH proceso antros eilės logaritminės tikėtinumo funkcijos išvestinės. Ilgo diapazono priklausomybė bus apskaičiuota parametriniu dalinai integruotu GARCH modeliu. Finansinių laiko eilučių duomenys bus įvertinti GARCH (CGARCH(1), CGARCH(2)) ir FIGARCH(1,d,1)) modeliais maksimalaus tikėtinumo metodu. Taip pat bus sukurtas NASDAQ- NYSE santykinio stiprumo indikatorius bei patikrintos jo panaudojimo sąlygos. Iiustracija yra pateikta 5 akcijų indeksais, 2 valiutų santykiais, aukso bei NNSS duomenims.
The paper deals with the problems of applying GARCH model/framework to a long term financial data, the search of the models, their evaluation, testing/validation and application. Long memory in conditional variance is one of the empirical features exhibited by many financial time series. One class of models that was suggested to capture this behavior is the so-called Fractionally Integrated GARCH (Baillie, Bollerslev and Mikkelsen 1996) in which the ideas of fractional integration originally introduced by Granger (1980) and Hosking (1981) for processes of the mean are applied to GARCH framework. In this paper we derive analytic expressions for the second-order derivatives of the log-likelihood function of FIGARCH processes. Long-range dependence is assessed through the parametric fractionally integrated GARCH model. Financial time series data will be estimated Component GARCH (CGARCH(1), CGARCH(2)) and FIGARCH models maximum likelihood method. Also we built NASDAQ- NYSE relative strength indicator and tested its usage conditions. An illustration is provided on 2 exchange rate, 5 stock index, gold and NNSS data.
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Solda, Grazielle Yumi. "Modelos de memória longa, GARCH e GARCH com memória longa para séries financeiras." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-03052008-170204/.

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O objetivo deste trabalho é apresentar e comparar diferentes métodos de modelagem da volatilidade (variância condicional) de séries temporais financeiras. O modelo ARFIMA é empregado para capturar o comportamento de memória longa observado na volatilidade de séries financeiras. Por sua vez, o modelo GARCH é utilizado para modelar a volatilidade variando no tempo destas séries. Finalmente, o modelo FIGARCH é utilizado para modelar a dinâmica dos retornos de séries temporais financeiras juntamente com sua volatilidade. Serão apresentados alguns estimadores para os parâmetros dos modelos estudados. Foram realizadas simulações dos três tipos de modelos com o objetivo de comparar o comportamento dos estimadores para diferentes valores dos parâmetros. Por fim, serão apresentadas aplicações em séries reais.
The goal of this project is to present and compare differents methods of modeling volatility (conditional variance) in financial time series. ARFIMA model is applied to capture long memory behavior of volatility in financial time series. GARCH model is used to model the temporal variation in financial volatility. Finally, FIGARCH model is used to model dynamic of financial time series returns as well as its volatility behavior. We present some estimators for the studied models. Estimators behavior of the three types of models for different parameters is assessed through a simulation study. At last, applications to real data are presented.
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Cai, Xinhua. "Froecast the USA Stock Indices with GARCH-type Models." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175432.

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Hu, Yingyi. "Gestion du risque de change : modélisation de la couverture et études économétriques." Cergy-Pontoise, 2009. http://www.theses.fr/2009CERG0443.

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L’objet de cette thèse est de donner une analyse approfondie du système de la gestion du risque de change mis en place dans les entreprises internationales, et surtout, d’améliorer la performance de ce système en introduisant des outils économétriques. Pour ce faire, le modèle ARFIMA-FIGARCH a été proposé afin de modéliser les cours historiques des taux de change « les Grands Six » et d’en donner ensuite les prévisions. Les résultats trouvent que la capacité prévisionnelle du modèle ARFIMA-FIGARCH surpasse systématiquement à différents horizons le modèle « Marche Aléatoire » qui ne donne simplement que des prévisions naïves. L’utilisation du modèle ARFIMA-FIGARCH peut potentiellement faciliter le travail de la gestion du risque de change. La décision de couverture contre ce risque s’en trouvera nettement améliorée
The purpose of this work is to give a thorough analysis on the exchange risk management system in the international enterprises, and, the most important, to improve the performance of this system by introducing the econometrical tools. For this purpose, the ARFIMA-FIGARCH econometric model has been introduced and applied to the exchange rate series of the “Great Six” with the presence of the long memory phenomenon. A comparison of the predictive ability has been made between ARFIMA-FIGARCH model and random walk. Our results suggest that the efficiency hypothesis of the exchange market would be strongly questioned. Application of such result to exchange rate would improve significantly hedging methods
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Chiriacescu-Lüling, Sanda. "Herrschaft und Revolte in Figaros Hochzeit : Untersuchung zu szenischen Realisationsmöglichkeiten des sozialkritischen Aspekts in W.A. Mozarts "die Hochzeit des Figaro"... /." Erlangen : Verlagsbuchhandlung H. Lüling, 1991. http://catalogue.bnf.fr/ark:/12148/cb35533365p.

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Schiebler, James Karl. "The marriage of Figaro : role of figaro." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/27383.

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Furlong, Alison Marie. "Georg Wildhagen's Figaros Hochzeit: How an Italian Opera Based on a French Play Became a German Socialist Film." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1269535387.

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Waldura, Markus. "Goethe und Figaro." Bärenreiter Verlag, 2012. https://slub.qucosa.de/id/qucosa%3A71950.

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Santos, Alessandra Gazzoli. "Estrutura fractal em séries temporais: uma investigação quanto à hipótese de passeio aleatório no mercado à vista de commodities agrícolas brasileiro." reponame:Repositório Institucional do FGV, 2013. http://hdl.handle.net/10438/11121.

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Economic variables are often governed by dynamic and non-linear processes that can originate long-term relationship and non-periodic and non-cyclical patterns with abrupt trend changes. Commodity prices exhibit this type of behavior and the peculiarities of those markets could generate fractionally integrated time series, whose singularities could not be properly captured by the traditional analytic models based on the efficient market hypothesis and random walk processes. Therefore, this study has investigated the presence of fractal structures on some very important Brazilian commodity spot markets such as coffee, cattle, sugar, soybean and calf. Some traditional techniques were used as well as other specific for fractal time series analysis, such as rescaled range (R/S) analysis, different fractal hypothesis tests and ARFIMA and FIGARCH models. The results showed that the drift component has not shown fractal behavior, except for the calf series, however, volatility has demonstrated fractal behavior for all the commodities that were analyzed.
As variáveis econômicas são frequentemente governadas por processos dinâmicos e não-lineares que podem gerar relações de dependência de longo prazo e padrões cíclicos não-periódicos com mudanças abruptas de tendências. Para o caso dos preços agrícolas este comportamento não é diferente e as peculiaridades destes mercados podem gerar séries temporais fracionalmente integradas, cujas singularidades não seriam adequadamente capturadas pelos tradicionais modelos analíticos fundamentados na hipótese dos mercados eficientes e de passeio aleatório. Sendo assim, o presente estudo buscou investigar a presença de estruturas fractais no mercado à vista de algumas das principais commodities agrícolas brasileiras: café, boi gordo, açúcar, milho, soja e bezerro. Foram empregadas técnicas tradicionais e específicas para a análise de séries temporais fractais como a análise de R/S e a aplicação de modelos das famílias ARFIMA e FIGARCH. Os resultados indicaram que, com exceção do bezerro, o componente de drift destas séries não apresentou comportamento fractal, ao contrário do observado para o componente da volatilidade, que apresentou aspecto de estrutura fractal para todas as commodities analisadas.
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Books on the topic "FIGARCH"

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Barnes, Frances. Figaro. Santa Rosa, CA: SRA School Group, 1994.

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Overmyer, Eric. Figaro/Figaro: Beauchmarchais' The marriage of Figaro and Ödön von Horváth's Figaro gets a divorce. N[ew] Y[ork], NY: Broadway Play Pub., 1996.

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Figari, Pedro. Pedro Figari. [New York, N.Y.]: W. Beadleston, 1987.

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Pedro, Figari. Pedro Figari. Punta del Este, Uruguay: Galería Sur, 1992.

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Murtas, Gianni. Filippo Figari. Nuoro: Ilisso, 2004.

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1861-1938, Figari Pedro, and Testoni Studios, eds. Pedro Figari. Montevideo, Uruguay]: El País, 2011.

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Murtas, Gianni. Filippo Figari. Nuoro: Ilisso, 1996.

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Pereda, Raquel. Pedro Figari. Montevideo, Uruguay: Edición Fundación Banco de Boston, 1995.

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Figari, Pedro. Pedro Figari. Punta del Este, Uruguay: Galería Sur, 1996.

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Shin Kokuritsu Gekijō. Un'ei Zaidan. Figaro no kekkon: Le nozze di Figaro. Tōkyō-to Shibuya-ku: Shin Kokuritsu Gekijō Un'ei Zaidan, 2013.

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Book chapters on the topic "FIGARCH"

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de Beaumarchais, Pierre Augustin Caron. "The Marriage of Figaro." In Four Georgian and Pre-Revolutionary Plays, 151–231. London: Macmillan Education UK, 1998. http://dx.doi.org/10.1007/978-1-349-26947-1_3.

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Willaschek, Wolfgang. "Le nozze di Figaro." In Mozart Theater, 119–84. Stuttgart: J.B. Metzler, 1996. http://dx.doi.org/10.1007/978-3-476-03426-7_3.

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Tobias, Michael Charles. "The Marriage of Figaro Factor." In Codex Orféo, 243–47. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30622-3_79.

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Unseld, Melanie, and Carola Bebermeier. "Figaro als Ereignis. Zur Einleitung." In La cosa è scabrosa, 7–20. Köln: Böhlau Verlag, 2018. http://dx.doi.org/10.7788/9783412512262.7.

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Mehring, Martina. "Horváth, Ödön von: Figaro läßt sich scheiden." In Kindlers Literatur Lexikon (KLL), 1–2. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_6916-1.

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Tyson, Alan. "Die Prager Version von Mozarts »Figaro« 1786." In Musik in Baden-Württemberg, 143–58. Stuttgart: J.B. Metzler, 1995. http://dx.doi.org/10.1007/978-3-476-03625-4_9.

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Cohen, Judith G. "The FIGARO Package for Astronomical Data Analysis." In Instrumentation for Ground-Based Optical Astronomy, 448–57. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3880-5_43.

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Gielen, Michael. "Zwei meiner Hauptaufgaben als Dirigent bei Figaros Hochzeit (1976)." In Michael Gielen, 38–42. Stuttgart: J.B. Metzler, 1997. http://dx.doi.org/10.1007/978-3-476-03695-7_7.

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Bermbach, Udo. "Revolution als Gesellschaftsspiel Mozarts Figaro als politisches Stück." In »Der moderne Komponist baut auf der Wahrheit«, 220–27. Stuttgart: J.B. Metzler, 2003. http://dx.doi.org/10.1007/978-3-476-02925-6_28.

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Schmidt, Dörte. "»Figaro diesseits des Rheins«. Michèle Reverdys Oper Le Précepteur." In Lenz im zeitgenössischen Musiktheater, 215–73. Stuttgart: J.B. Metzler, 1993. http://dx.doi.org/10.1007/978-3-476-03483-0_6.

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Conference papers on the topic "FIGARCH"

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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 heteroscedasticity should be included in time series with high volatility.
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"Modelling high-frequency volatility with three-state FIGARCH models." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand, 2013. http://dx.doi.org/10.36334/modsim.2013.f8.shi2.

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Sivakumar, P. Bagavathi, and V. P. Mohandas. "Modeling and predicting stock returns using the ARFIMA-FIGARCH." In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393807.

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Lin, Yong, and Lei Wu. "FIGARCH model on Chinese securities market based on the genetic algorithms." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5648036.

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Zhang, Lei. "Testing for long memory volatility of Chinese stock markets with FIGARCH model." In 2014 International Conference on Management Science and Engineering (ICMSE). IEEE, 2014. http://dx.doi.org/10.1109/icmse.2014.6930402.

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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 Heteroskedasticity), have been built to characterize the variation of Great Salt Lake elevation, these models can not characterize it perfectly. Therefore, it became a key point to build a more appropriate model of GSL elevation time series. In this paper a new model based on fractional autoregressive integrated moving average (ARFIMA) with Stable innovations is applied to analyze the data and predict the future levels. From the analysis we can see that the new model can characterize GSL elevation time series more accurately. The new model will be beneficial to predict GSL elevation more precisely.
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Nasi, Kelly, Martin Daněk, Theodoros Karoubalis, and Zdeněk Pohl. "Figaro." In the 2005 ACM/SIGDA 13th international symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1046192.1046227.

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Khan, Shahid, Matthias Volk, Joost-Pieter Katoen, Alexis Braibant, and Marc Bouissou. "Model Checking the Multi-Formalism Language FIGARO." In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 2021. http://dx.doi.org/10.1109/dsn48987.2021.00056.

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Svanera, Michele, Umar Riaz Muhammad, Riccardo Leonardi, and Sergio Benini. "Figaro, hair detection and segmentation in the wild." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532494.

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Porfirio, David J., Laura Stegner, Maya Cakmak, Allison Sauppé, Aws Albarghouthi, and Bilge Mutlu. "Figaro: A Tabletop Authoring Environment for Human-Robot Interaction." In CHI '21: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3411764.3446864.

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Reports on the topic "FIGARCH"

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Polumysna, Olga. ОБРАЗ УКРАЇНИ У ФРАНЦУЗЬКИХ МЕДІА (НА ПРИКЛАДІ «LE FIGARO»). Ivan Franko National University of Lviv, January 2020. http://dx.doi.org/10.30970/vjo.2020.48.10564.

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