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Journal articles on the topic 'FIGARCH'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Musunuru, Naveen. "Modeling Long Range Dependence in Wheat Food Price Returns." International Journal of Economics and Finance 11, no. 9 (August 18, 2019): 46. http://dx.doi.org/10.5539/ijef.v11n9p46.

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The present paper focuses on analyzing the volatility dynamics of wheat commodity based on the presence of long memory. The paper utilizes several econometric tests to identify the presence and magnitude of the fractional difference parameter. Fractional GARCH models, namely FIGARCH and FIEGARCH, are employed to examine the long memory property. Twenty years of wheat daily price data were used to study the long-range dependence. The results reveal that fractional integration is found in the daily wheat price return series. Overall, the FIGARCH model seems a better fit, in describing the time-varying volatility of the commodity adequately, compared to the FIEGARCH model. Food price shocks are likely to persist for a long time for wheat, resulting in higher market risk for producers and increased purchasing costs for consumers.

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12

Cochran, Steven J., Iqbal Mansur, and Babatunde Odusami. "Volatility persistence in metal returns: A FIGARCH approach." Journal of Economics and Business 64, no. 4 (July 2012): 287–305. http://dx.doi.org/10.1016/j.jeconbus.2012.03.001.

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13

Andrysiak, Tomasz, Łukasz Saganowski, Mirosław Maszewski, and Piotr Grad. "Long-Memory Dependence Statistical Models for DDoS Attacks Detection." Image Processing & Communications 20, no. 4 (December 1, 2015): 31–40. http://dx.doi.org/10.1515/ipc-2015-0042.

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Abstract DDoS attacks detection method based on modelling the variability with the use of conditional average and variance in examined time series is proposed in this article. Variability predictions of the analyzed network traffic are realized by estimated statistical models with long-memory dependence ARFIMA, Adaptive ARFIMA, FIGARCH and Adaptive FIGARCH. We propose simple parameter estimation models with the use of maximum likelihood function. Selection of sparingly parameterized form of the models is realized by means of information criteria representing a compromise between brevity of representation and the extent of the prediction error. In the described method we propose using statistical relations between the forecasted and analyzed network traffic in order to detect abnormal behavior possibly being a result of a network attack. Performed experiments confirmed effectiveness of the analyzed method and cogency of the statistical models.

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14

CHANG, CHIA-LIN, MICHAEL McALEER, and ROENGCHAI TANSUCHAT. "MODELLING LONG MEMORY VOLATILITY IN AGRICULTURAL COMMODITY FUTURES RETURNS." Annals of Financial Economics 07, no. 02 (December 2012): 1250010. http://dx.doi.org/10.1142/s2010495212500108.

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This paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGARCH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1, d, 1) and FIEGARCH (1, d, 1) models are found to outperform their GARCH (1, 1) and EGARCH (1, 1) counterparts.

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15

Simões, Mario Domingues, Marcelo Cabus Klotzle, Antonio Carlos Figueiredo Pinto, and Gabriel Levrini. "Uma avaliação da volatilidade dos preços da soja no mercado internacional com dados de alta frequência." Gestão & Produção 19, no. 1 (2012): 219–31. http://dx.doi.org/10.1590/s0104-530x2012000100015.

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Neste trabalho foram avaliados os ajustes de cinco modelos para previsão da variância, utilizando-se uma série de preços de soja, uma commodity negociada na bolsa de mercadorias de Chicago (CBOT), com dados de alta frequência. Os modelos utilizados foram do tipo GARCH, FIGARCH e ARFIMA. Foi possível observar características desta série de preços de uma commodity negociada globalmente que se apresentaram inteiramente diferentes daquelas de ativos financeiros anteriormente estudados, possivelmente em virtude da característica contínua dos preços observados, induzida pela sua negociação global independente de pregões com início e fim. Foi possível concluir que a série de dados de alta frequência encerra informações adicionais às séries de dados diários, também no caso estudado de preços da soja, e que o tradicional modelo GARCH(1,1) tem um bom desempenho também no caso dos dados de alta frequência, assim como aqueles da família ARFIMA. Recomenda-se mais investigação para o caso dos modelos FIGARCH, procurando um melhor ajuste.

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16

Kumar, Anoop. "Testing for long memory in volatility in the Indian Forex market." Ekonomski anali 59, no. 203 (2014): 75–90. http://dx.doi.org/10.2298/eka1403075k.

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This article attempts to verify the presence of long memory in volatility in the Indian foreign exchange market using daily bilateral returns of the Indian Rupee against the US dollar from 17/02/1994 to 08/11/2013. In the first part of the analysis the presence of long-term dependence is confirmed in the return series as well as in two measures of unconditional volatility (absolute returns and squared returns) by employing three measures of long memory. Next, the presence of long memory in conditional volatility is tested using ARMA-FIGARCH and ARMA-FIAPARCH models under various distributional assumptions. The results confirm the presence of long memory in conditional variance for two models. In the last part, the presence of long memory in conditional mean and conditional variance is verified using ARFIMA-FIGARCH and ARFIMA-FIAPARCH models. It is also found that long-memory models fare well compared to short-memory models in sample forecast performance.

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17

Shi, Yanlin, and Kin-Yip Ho. "Modeling high-frequency volatility with three-state FIGARCH models." Economic Modelling 51 (December 2015): 473–83. http://dx.doi.org/10.1016/j.econmod.2015.09.008.

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18

Lombardi, Marco J., and Giampiero M. Gallo. "Analytic Hessian matrices and the computation of FIGARCH estimates." Statistical Methods & Applications 11, no. 2 (June 2002): 247–64. http://dx.doi.org/10.1007/bf02511490.

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19

Štolc, Zdeněk. "Application of FIGARCH and EWMA Models on Stock Indices PX and BUX." Acta Oeconomica Pragensia 19, no. 4 (August 1, 2011): 25–38. http://dx.doi.org/10.18267/j.aop.338.

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20

Salgado, Roberto J. Santillán, Marissa Martínez Preece, and Francisco López Herrera. "Modeling the risk-return characteristics of the SB1 Mexican private pension fund index." Global Journal of Business, Economics and Management: Current Issues 5, no. 2 (March 4, 2016): 70. http://dx.doi.org/10.18844/gjbem.v5i2.370.

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This paper analyzes the returns and variance behavior of the largest specialized private pension investment funds index in Mexico, the SIEFORE Básica 1 (or, SB1). The analysis was carried out with time series techniques to model the returns and volatility of the SB1, using publicly available historical data for SB1. Like many standard financial time series, the SB1 returns show non-normality, volatility clusters and excess kurtosis. The econometric characteristics of the series were initially modeled using three GARCH family models: GARCH (1,1), TGARCH and IGARCH. However, due to the presence of highly persistent volatility, the series modeling was extended using Fractionally Integrated GARCH (FIGARCH) methods. To that end, an extended specification: an ARFIMA (p,d,q) and a FIGARCH model were incorporated. The evidence obtained suggests the presence of long memory effects both in the returns and the volatility of the SB1. Our analysis’ results have important implications for the risk management of the SB1. Keywords: Private Pension Funds, Time Series modelling, GARCH models, Long Term memory series

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21

Kaya Soylu, Pınar, Mustafa Okur, Özgür Çatıkkaş, and Z. Ayca Altintig. "Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple." Journal of Risk and Financial Management 13, no. 6 (May 29, 2020): 107. http://dx.doi.org/10.3390/jrfm13060107.

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This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck’s tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns.

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22

Baillie, Richard T., Aydin A. Cecen, and Young-Wook Han. "High Frequency Deutsche Mark-US Dollar Returns: FIGARCH Representations and Non Linearities." Multinational Finance Journal 4, no. 3/4 (December 1, 2000): 247–67. http://dx.doi.org/10.17578/4-3/4-6.

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23

Giraitis, Liudas, Donatas Surgailis, and Andrius Škarnulis. "STATIONARY INTEGRATED ARCH(∞) AND AR(∞) PROCESSES WITH FINITE VARIANCE." Econometric Theory 34, no. 6 (October 17, 2017): 1159–79. http://dx.doi.org/10.1017/s0266466617000391.

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We prove the long standing conjecture of Ding and Granger (1996) about the existence of a stationary Long Memory ARCH model with finite fourth moment. This result follows from the necessary and sufficient conditions for the existence of covariance stationary integrated AR(∞), ARCH(∞), and FIGARCH models obtained in the present article. We also prove that such processes always have long memory.

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24

Pelinescu, Elena, and Marius Acatrinei. "Modelling the High Frequency Exchange Rate in Romania with FIGARCH." Procedia Economics and Finance 15 (2014): 1724–31. http://dx.doi.org/10.1016/s2212-5671(14)00647-9.

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Bentes, Sónia R. "Measuring persistence in stock market volatility using the FIGARCH approach." Physica A: Statistical Mechanics and its Applications 408 (August 2014): 190–97. http://dx.doi.org/10.1016/j.physa.2014.04.032.

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26

CHEN, SHU-LING, and YU-LIEH HUANG. "ACTUARIAL IMPLICATIONS OF STRUCTURAL CHANGES IN EL NIÑO-SOUTHERN OSCILLATION INDEX DYNAMICS." Annals of Financial Economics 09, no. 02 (September 2014): 1440007. http://dx.doi.org/10.1142/s2010495214400077.

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The influence of climate variability on agricultural production and financial risks faced by an individual or an institution has been the center of the public discussion in the recent years. The changing weather patterns and environmental conditions could cause substantial unpredicted economic losses. Failure to capture such changes would underestimate the insurance contract's expected indemnity and further create a major obstacle for insurance sectors. In this paper, we undertake a case study of El Niño-Southern Oscillation (ENSO) Index insurance for coastal Peru proposed by Skees. We examined the behavior of El Niño index and uncovered the evidence that the conditional volatility of El Niño index has changed over time. A fractionally integrated GARCH (FIGARCH) process that captures long memory behavior for conditional variance and allows the disturbance variance to vary over time is used to design and rate the ENSO Index insurance contract. Our results show that, with the time-invariant AR(2) model serving as a benchmark, the AR(2)-FIGARCH(1, d, 1) model outperforms the AR(2) model in both in-sample fit and out-of-sample forecast for El Niño index. Moreover, the time-invariant model could underestimate the premium rates, exposing the insurer to undesired underwriting risk and ultimately causing the index insurance market to collapse.

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Chan, Ngai Hang, and Chi Tim Ng. "A note on asymptotic inference for FIGARCH($p, d, q$) models." Statistics and Its Interface 4, no. 2 (2011): 227–33. http://dx.doi.org/10.4310/sii.2011.v4.n2.a16.

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Jaehwan Park and 김현숙. "Long Memory in LME Volatility through the ARFIMA and FIGARCH Model." Korean Journal of Financial Engineering 15, no. 4 (December 2016): 29–52. http://dx.doi.org/10.35527/kfedoi.2016.15.4.002.

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29

TU, Teng-Tsai, and Chih-Wei LIAO. "Block Trading Based Volatility Forecasting: An Application of VACD-FIGARCH Model." Journal of Asian Finance, Economics and Business 7, no. 4 (April 30, 2020): 59–70. http://dx.doi.org/10.13106/jafeb.2020.vol7.no4.59.

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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 (February 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 and hyperbolic generalised autoregressive conditional heteroskedasticity (ARFIMA-HYGARCH), and three forecasting horizons, that is, 1-, 5- and 20-step-ahead horizons, to model ETNs returns and volatilities. The article finds long-memory processes in ETNs; however, dual long-memory process in returns and volatilities is not verified. The research also poses a challenge to the weak-form efficiency hypothesis of Fama (1970) because lagged changes determine future values, especially in volatility. The findings also show that differences in the characteristics of commodity, currency and equity ETNs are not concluded because of similarities in ETN traits and several insignificant results. However, the presence of intermediate memory was identified, and should serve as a warning sign for investors not to keep these investments in the long run. Lastly, the ARFIMA-FIGARCH model has a slight edge over the ARFIMA-GARCH and ARFIMA-HYGARCH specifications using 1-, 5- and 20-forecast horizons. JEL Classification: G11, G17

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31

Onour, Ibrahim A. "Herd Behavior and Volatility Persistence in Bombay (Mumbai) Stock Exchange." Management and Economics Research Journal 6 (2020): 1. http://dx.doi.org/10.18639/merj.2020.958657.

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This paper employs a combination of unit root tests and fractional integration techniques using the ARFIMA(p,d,q) model to test rational bubbles, which implies herd behavior, in Bombay Stock Exchange (BSE). The results in the paper strongly support the evidence of herd behavior in the daily, weekly, and monthly price aggregates. Moreover, the paper also investigates the degree of conditional volatility persistence using FIGARCH(p,d,q) specification to show that the persistence of shocks to stock price and dividend yield volatilities is short-termed.

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R. Bentes, Sónia, and Nuno B. Ferreira. "A FIGARCH approach to stock market volatility: evidence from Portugal, Ireland, Italy, Greece and Spain." International Journal of Academic Research 5, no. 6 (December 10, 2013): 107–11. http://dx.doi.org/10.7813/2075-4124.2013/5-6/a.14.

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Nguyen, Quynh-Trang, John Francis Diaz, Jo-Hui Chen, and Ming-Yen Lee. "Fractional Integration in Corporate Social Responsibility Indices: A FIGARCH and HYGARCH Approach." Asian Economic and Financial Review 9, no. 7 (2019): 836–50. http://dx.doi.org/10.18488/journal.aefr.2019.97.836.850.

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34

Beine, Michel, Agnès Bénassy-Quéré, and Christelle Lecourt. "Central bank intervention and foreign exchange rates: new evidence from FIGARCH estimations." Journal of International Money and Finance 21, no. 1 (February 2002): 115–44. http://dx.doi.org/10.1016/s0261-5606(01)00040-7.

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35

Lee, O. "Functional central limit theorems for augmented GARCH(p,q) and FIGARCH processes." Journal of the Korean Statistical Society 43, no. 3 (September 2014): 393–401. http://dx.doi.org/10.1016/j.jkss.2013.12.001.

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36

Kılıç, Rehim. "Long memory and nonlinearity in conditional variances: A smooth transition FIGARCH model." Journal of Empirical Finance 18, no. 2 (March 2011): 368–78. http://dx.doi.org/10.1016/j.jempfin.2010.11.007.

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Hongngoc, Truong. "Arfima-Figarch vs. Arfima-Hygarch: Case Study ETF Returns of Emerging Asian Countries." Asian Journal of Finance & Accounting 6, no. 2 (October 2, 2014): 171. http://dx.doi.org/10.5296/ajfa.v6i2.5896.

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38

Baillie, Richard T., and Claudio Morana. "Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach." Journal of Economic Dynamics and Control 33, no. 8 (August 2009): 1577–92. http://dx.doi.org/10.1016/j.jedc.2009.02.009.

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Elyasiani, Elyas, Iqbal Mansur, and Babatunde Odusami. "Sectoral stock return sensitivity to oil price changes: a double-threshold FIGARCH model." Quantitative Finance 13, no. 4 (April 2013): 593–612. http://dx.doi.org/10.1080/14697688.2012.721562.

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Figuerola-Ferretti, Isabel, and Christopher L. Gilbert. "Commonality in the LME aluminum and copper volatility processes through a FIGARCH lens." Journal of Futures Markets 28, no. 10 (October 2008): 935–62. http://dx.doi.org/10.1002/fut.20338.

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Jach, Agnieszka, and Piotr Kokoszka. "Empirical wavelet analysis of tail and memory properties of LARCH and FIGARCH models." Computational Statistics 25, no. 1 (August 29, 2009): 163–82. http://dx.doi.org/10.1007/s00180-009-0168-6.

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Gaio, Luiz Eduardo, and Tabajara Pimenta Júnior. "Value-at-Risk da Carteira do Ibovespa: uma análise com o uso de modelos de memória longa." Gestão & Produção 19, no. 4 (December 2012): 779–92. http://dx.doi.org/10.1590/s0104-530x2012000400009.

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O presente estudo propõe uma análise comparativa de dez modelos de volatilidade para o cálculo do Value-at-Risk (VaR) para carteira teórica do Ibovespa, considerando a presença de memória longa na série temporal dos seus retornos diários. Para isso, foram utilizados dados do período de 4 de janeiro de 2000 a 28 de dezembro de 2007. Os resultados mostraram que os modelos que captam o efeito de memória longa na volatilidade condicional dos retornos do Ibovespa, em especial o medido pelo modelo FIGARCH (1,d,1), são os que apresentam melhor desempenho para o cálculo do Value-at-Risk, comparado com alguns modelos tradicionais, como é o caso do Riskmetrics.

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Young Wook, Han. "Structural Breaks and Long Memory Property in Korean Won Exchange Rates: Adaptive FIGARCH Model." East Asian Economic Review 15, no. 2 (June 30, 2011): 33–59. http://dx.doi.org/10.11644/kiep.jeai.2011.15.2.229.

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Bentes, Sonia R. "Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: New evidence." Physica A: Statistical Mechanics and its Applications 438 (November 2015): 355–64. http://dx.doi.org/10.1016/j.physa.2015.07.011.

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Figueiredo, Erik Alencar de, and André M. Marques. "Inflação inercial como um processo de longa memória: análise a partir de um modelo Arfima-Figarch." Estudos Econômicos (São Paulo) 39, no. 2 (June 2009): 437–58. http://dx.doi.org/10.1590/s0101-41612009000200008.

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O objetivo principal deste estudo é investigar a dependência de longo prazo da inflação brasileira, descrevendo-a como um processo fracionariamente integrado tanto na média quanto na variância. A metodologia empregada baseia-se na estimação de um modelo ARFIMA-FIGARCH, capaz de detectar a presença de memória longa em altas defasagens de um processo autorregressivo. Os principais resultados alcançados indicam que, para o período pós-Plano Real, a inflação brasileira exibe um comportamento estacionário em seus dois primeiros momentos com lento decaimento hiperbólico. Há indícios de longa memória na média e na variância do processo. Além disso, constatou-se, para esse período, uma recíproca influência entre a volatilidade e a taxa média de inflação.

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Angelidis,, Dimitrios, Athanasios Koulakiotis, and Apostolos Kiohos. "Feedback Trading Strategies: The Case of Greece and Cyprus." South East European Journal of Economics and Business 13, no. 1 (June 1, 2018): 93–99. http://dx.doi.org/10.2478/jeb-2018-0006.

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Abstract This paper examines whether or not feedback trading strategies are present in the Athens (ASE) and Cyprus Stock Exchanges (CSE). The analysis employs two econometric models: the feedback trading strategy model, introduced by Sentana and Wadhwani (1992), and the exponential autoregressive model, proposed by LeBaron (1992). These two theoretical frameworks, separately, were joined with the FIGARCH (1, d, 1) approach. Both models assume two different groups of traders - the “rational” investors that build their portfolio by following the firms’ fundamentals and the “noise” speculators that ignore stock fundamentals and focus on a positive (negative) feedback trading strategy. The empirical results revealed that negative feedback trading strategies exist in the two underlying stock markets

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Briones Zúñiga, José Luis, and Antonio Bravo Quiroz. "Procesos FIGARCH: Caso Estimación de la volatilidad del tipo de cambio nominal del Per´ú." Pesquimat 22, no. 2 (December 20, 2019): 35–50. http://dx.doi.org/10.15381/pesquimat.v22i2.17230.

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En esta investigación se presenta una revisión teórica de la estructura y aplicación de modelos de naturaleza de memoria larga que combinan características de los procesos fraccionalmente integrados con los clásicos modelos GARCH obteniéndose de esta manera los modelos Autorregresivos con Heterocedasticidad Condicionada Fraccionalmente Integrado (FIGARCH) los cuales a través de la función impulso respuesta acumulativa permitió cuantiﬁcar el grado de persistencia del impacto de la innovación sobre la función de la varianza condicionada, es decir el elemento de persistencia en una serie caótica muy sensible a cambios en la condicio-nes iniciales asociadas al movimiento browniano fraccional. Para dicha aplicación se utilizó la variable tipo de cambio y mediante modelos de series de tiempo de memoria larga poder analizar la persistencia del efecto existente en la volatilidad de dicha serie.

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Pavlova, Ivelina, Jang Hyung Cho, A. M. Parhizgari, and William G. Hardin. "Long memory in REIT volatility and changes in the unconditional mean: a modified FIGARCH approach." Journal of Property Research 31, no. 4 (February 17, 2014): 315–32. http://dx.doi.org/10.1080/09599916.2013.877063.

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Beine, Michel, Sébastien Laurent, and Christelle Lecourt. "Accounting for conditional leptokurtosis and closing days effects in FIGARCH models of daily exchange rates." Applied Financial Economics 12, no. 8 (August 2002): 589–600. http://dx.doi.org/10.1080/09603100010014041.

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Abed, Riadh El, Sahar Boukadida, and Warda Jaidane. "Financial Stress Transmission from Sovereign Credit Market to Financial Market: A Multivariate FIGARCH-DCC Approach." Global Business Review 20, no. 5 (August 28, 2019): 1122–40. http://dx.doi.org/10.1177/0972150919846994.

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This study examines the interdependence between the daily eurozone sovereign credit default swaps (CDS) index and four financial market sectors such as banking CDS market (CDSb), underlying sovereign market (BONDs), stock market (BMI) and future interest rate benchmark of the bunds obligation (EUROBOBL). Focusing on different phases of the sovereign debt crises, the aim of this article is to examine how the dynamics of correlations between the CDSs and financial market indicators evolved from 20 September 2011 to 12 February 2016. To this end, we adopt a dynamic conditional correlation (DCC) model into a multivariate fractionally integrated generalized ARCH (FIGARCH) framework, which accounts for long memory and time-varying correlations. The empirical findings indicate a general pattern of decrease and increase in correlations during the phases of the sovereign debt crisis, suggesting the spillover effect and different vulnerability of the CDS index and financial market indicators.

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