Academic literature on the topic 'Arfima-figarch'
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Journal articles on the topic "Arfima-figarch"
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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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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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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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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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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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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 may possibly conclude the resilience of both precious metals against increased volatility. However, further tests proved that only platinum has a symmetric volatility response to shocks with the presence of negative gamma parameter, which proves that only platinum can be considered a safe harbor investment, because negative and positive shocks have equal effects on their returns and volatilities. Comparing the four models utilized in this study, the combined ARFIMA-FIAPARCH models are the best fitting model to characterize both precious metals’ spot prices.
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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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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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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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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.
Dissertations / Theses on the topic "Arfima-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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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 amélioréeThe 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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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.
Book chapters on the topic "Arfima-figarch"
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Ben Sassi, Salim, and Azza Bejaoui. "On the Impact of Long Memory on Market Risk." In Advances in Finance, Accounting, and Economics, 42–62. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3767-0.ch003.
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This chapter investigates the influence of the long memory behavior in returns and volatility on the market risk for four emerging stock markets during the pre- and post-crisis periods. In this respect, the authors consider four major political events (Tunisian revolution, Egyptian revolution, assassination of Prime Minister Rafik El Hariri, and a series of suicide bombings in Morocco). Using the modified R/S test and GPH test, they show the long memory property in returns and volatility over the two sub-periods. To explore the dual long memory property, the authors apply the joint ARFIMA–FIGARCH specification on the returns and volatility of the four emerging stock markets. The dual long memory property is prevalent in the returns and volatility of the emerging stock markets over the pre-crisis period. During the post-crisis period, the dual long memory process is only detected in the Moroccan market. The authors also display the dynamic behavior of VaR during the two sub-periods. In addition, based on the backtesting test, VaR performed better during the two sub-periods for all countries.
Conference papers on the topic "Arfima-figarch"
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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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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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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.