Relevant bibliographies by topics / ARMA-GARCH Model

Contents

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

Academic literature on the topic 'ARMA-GARCH Model'

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

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'ARMA-GARCH Model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "ARMA-GARCH Model"

1

Bildirici, Melike, and Özgür Ersin. "Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns." Scientific World Journal 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/497941.

Full text
Abstract:
The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.
APA, Harvard, Vancouver, ISO, and other styles
2

Zhao, Xin, Hong Lei Qin, and Li Cong. "A Novel Adaptive Integrated Navigation Filtering Method Based on ARMA/GARCH Model." Applied Mechanics and Materials 462-463 (November 2013): 259–66. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.259.

Full text
Abstract:
This paper proposes a novel adaptive integrated navigation filtering method based on autoregressive moving average (ARMA) model and generalized autoregressive conditional heteroscedasticity (GARCH) model. The main idea in this study is to employ ARMA/GARCH model to estimate statistical characteristics of filtering residual series online, namely, the conditional mean and conditional standard deviation, and then the filter parameters are adaptively adjusted based on forecasted results of ARMA/GARCH model in order to improve the reliability of the system when there are abnormal disturbance and other uncertain factors in real condition. On this basis, experiment is used to verify the validity of the method. The simulation results demonstrate that the ARMA/GARCH model can well capture the unusual condition of GPS receiver output, and this adaptive filtering method can effectively improve the reliability of the system.
APA, Harvard, Vancouver, ISO, and other styles
3

Kuswanto, Heri, and Endy Norma Chyntia Damayanti. "Analisis Risiko Pada Return Saham Perusahaan Asuransi Menggunakan Metode VaR dengan Pendekatan ARMA-GARCH." Jurnal Matematika, Statistika dan Komputasi 16, no. 1 (June 27, 2019): 40. http://dx.doi.org/10.20956/jmsk.v16i1.6197.

Full text
Abstract:
Pasar modal Indonesia merupakan salah satu negara tujuan investasi bagi investor di negara-negara maju (developed markets) yang dikenal sebagai emerging market. Perkembangan kondisi perekonomian di Indonesia sendiri dianggap baik bagi para investor untuk menanamkan dana. Saham sektor keuangan menjadi salah satu sektor yang ikut berkembang di sepanjang tahun ini. Tiga dari tujuh saham yang menunjukkan bertumbuh dengan baik adalah PT Asuransi Multi Artha Guna Tbk (AMAG), PT Paninvest Tbk (PNIN), dan PT Lippo General Insurance Tbk (LPGI). Terdapat dua hal penting yaitu tingkat pengembalian atau imbal hasil (return) dan risiko. Komponen lain yang tidak kalah penting adalah volatilitas return saham. Berdasarkan penjelasan diatas, maka dilakukan penelitian untuk menganalisis return saham dan volatilitas ketiga saham. Salah satu metode yang digunakan dalam mengestimasi risiko saham adalah metode VaR (Value at Risk). Untuk mengatasi volatilitas dapat menggunakan ARMA dan GARCH. Dihasilkan bahwa tiga saham perusahaan memberikan nilai rata-rata return yang positif sehingga memberikan keuntungan bagi investor. Saham perusahaan LPGI memiliki potensi risiko yang paling tinggi karena nilai standar deviasi yang tinggi. Model terbaik untuk return saham AMAG adalah ARMA ([7],[7]) dan model GARCH (1,2). Pada return saham LPGI model terbaik adalah ARMA ([2],[2]) dan GARCH (1,1). Return saham PNIN diperoleh model terbaik ARMA (0,[3]) dan GARCH (1,2). Pada pemodelan Parsimony didapatkan model ARMA (1,0) GARCH (1,1) untuk return saham perusahaan AMAG, ARMA (0,1) GARCH (1,1) untuk return saham perusahaan LPGI dan ARMA (1,1) GARCH (1,1) untuk return saham perusahaan PNIN. Pada perhitungan VaR didapatkan investor akan mengalami kerugian maksimum sebesar Rp 47.089.529,- bila menanamkan modal sebesar Rp 1.000.000.000,- di perusahaan AMAG, berlaku pula pada perusahaan LPGI, investor akan mengalami kerugian sebesar Rp 60.018.734,- dan Rp 39.196.540,- di perusahaan PNIN dengan tingkat keyakinan 95%.
APA, Harvard, Vancouver, ISO, and other styles
4

Klepáč, Václav, and David Hampel. "Assessing Efficiency of D-Vine Copula ARMA-GARCH Method in Value at Risk Forecasting: Evidence from PSE Listed Companies." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 4 (2015): 1287–95. http://dx.doi.org/10.11118/actaun201563041287.

Full text
Abstract:
The article points out the possibilities of using static D-Vine copula ARMA-GARCH model for estimation of 1 day ahead market Value at Risk. For the illustration we use data of the four companies listed on Prague Stock Exchange in range from 2010 to 2014. Vine copula approach allows us to construct high-dimensional copula from both elliptical and Archimedean bivariate copulas, i.e. multivariate probability distribution, created from process innovations. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we backtested D-Vine copula ARMA-GARCH model against the VaR rolling out of sample forecast from October 2012 to April 2014 of chosen benchmark models, e.g. multivariate VAR-GO-GARCH, VAR-DCC-GARCH and univariate ARMA-GARCH type models. Common backtesting via Kupiec and Christoffersen procedures offer generalization that technological superiority of model supports accuracy only in case of an univariate modeling – working with non-basic GARCH models and innovations with leptokurtic distributions. Multivariate VAR governed type models and static Copula Vines performed in stated backtesting comparison worse than selected univariate ARMA-GARCH, i.e. it have overestimated the level of actual market risk, probably due to hardly tractable time-varying dependence structure.
APA, Harvard, Vancouver, ISO, and other styles
5

Cheng, Cong, Ling Yu, and Liu Jie Chen. "Structural Nonlinear Damage Detection Based on ARMA-GARCH Model." Applied Mechanics and Materials 204-208 (October 2012): 2891–96. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.2891.

Full text
Abstract:
Two economic models, i.e. auto-regressive and moving average model (ARMA) and generalized auto-regressive conditional heteroscedasticity model (GARCH), are adopted to assess the conditions of structures and to detect structural nonlinear damage based on time series analysis in this study. To improve the reliability of the method for nonlinear damage detection, a new damage sensitive feature (DSF) for the ARMA-GARCH model is defined as a ratio of the standard deviation of the variance time series of ARMA-GARCH model residual errors in test condition to ones in reference condition. Compared to the traditional DSF defined as the ratio between the deviations of ARMA-GARCH model residual error in two conditions, the successful outcomes of the new DSF can give obvious explanation for the current states of structures and can detect the nonlinear damage exactly, which enhance the worth of structural health monitoring as well as condition-based maintenance in practical applications. This method is finally verified by a series of experimental data of three-story building structure made in Los Alamos National Laboratory USA.
APA, Harvard, Vancouver, ISO, and other styles
6

Yang, Wenqi, and Jingkun Ma. "Implied Volatility Prediction Based on Different Term Structures: An Empirical Study of the SSE 50 ETF Options Market from High-Frequency Data." E3S Web of Conferences 235 (2021): 02043. http://dx.doi.org/10.1051/e3sconf/202123502043.

Full text
Abstract:
This article focuses on the implied volatility forecast of the SSE 50 ETF options market from June 1, 2017, to August 30, 2019, and constructs AR (1) model and ARMA-GARCH model based on liquidity characteristics to compare and analyze the prediction effect of implied volatility on different option types and term structures. The results show that, during the sample period of the SSE 50 ETF options market, the effect of model fitting of the ARMA-GARCH model is significantly better than the AR (1) model; the fitting sequences predicted by the two models have typical time-varying and synchronization characteristics, and the prediction effect of the ARMA-GARCH model in the whole period is significantly better than the AR (1) model.
APA, Harvard, Vancouver, ISO, and other styles
7

Garcia Angelico, Diego, and Sandra Cristina de Oliveira. "ARMA-GARCH Model and temporal precedence between stock indices." Revista Gestão da Produção Operações e Sistemas 11, no. 1 (March 1, 2016): 97–112. http://dx.doi.org/10.15675/gepros.v11i1.1306.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Dritsakis, Nikolaos, and Georgios Savvas. "Forecasting Volatility Stock Return: Evidence from the Nordic Stock Exchanges." International Journal of Economics and Finance 9, no. 2 (January 11, 2017): 15. http://dx.doi.org/10.5539/ijef.v9n2p15.

Full text
Abstract:
The purpose of this study is to explore the volatility and secondary effects in the four Nordic stock exchanges of Norway: Oslo Bors Linked all-share index AXLT Denmark: OMX Copenhagen 20, Sweden: OMX Stockholm 30 and Finland: OMX Helsinki 25. Keeping in mind that there is an ARCH effect in the returns of the four stock exchanges, we move on to the evaluation to the evaluation of models ARCH (q), GARCH (p, q) GARCH-M (p, q). Evaluating the parameters became possible through the use of the maximum likelihood method using the BHHH algorithm of (Berndt et al., 1974) and the three distributions (normal, t-Student, and the Generalized normal distribution GED). The results of this study indicate model ARMA(0,1)-GARCH-Μ(1,1) with t-student distribution as the appropriate one to describe the returns of the all Nordic stock exchanges except that of Sweden, where model ARMA(0,3)-GARCH-Μ(1,1) describes it best. Lastly, for forecasting the models ARMA(0,1)-GARCH-Μ(1,1) and ARMA(0,3)-GARCH-Μ(1,1) of the current stock exchanges we use both the dynamic and static process. The results of this study indicate that the static process forecasts better than the corresponding dynamic.
APA, Harvard, Vancouver, ISO, and other styles
9

Jezernik Širca, Špela, and Matjaž Omladič. "The JLS model with ARMA/GARCH errors." Ars Mathematica Contemporanea 13, no. 1 (October 21, 2016): 63–79. http://dx.doi.org/10.26493/1855-3974.746.dab.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Mahlindiani, Lara, Maiyastri ., and Hazmira Yozza. "PENENTUAN RESIKO INVESTASI DENGAN MODEL GARCH PADA INDEKS HARGA SAHAM PT. INDOFOOD SUKSES MAKMUR TBK." Jurnal Matematika UNAND 6, no. 1 (February 1, 2017): 25. http://dx.doi.org/10.25077/jmu.6.1.25-32.2017.

Full text
Abstract:
Abstrak. Ketika melakukan investasi saham, investor menginginkan return yang tingginamun dengan resiko yang rendah. Untuk mencapai tujuan investasi tersebut, dilakukanpemodelan terhadap harga saham dengan beberapa model seperti Autoregressive (AR),Moving Average (MA) dan Autoregressive Moving Average (ARMA). Aspek pentinglain yang berkaitan dengan investasi adalah pengukuran resiko dengan Value at Risk(VaR) yang merupakan pengukuran kemungkinan kerugian terburuk dalam kondisi pasaryang normal pada kurun waktu t dengan taraf kepercayaan tertentu. Salah satu modelyang dapat mengestimasi resiko adalah model Generalized Autoregressive ConditionalHeteroscedastic (GARCH). Oleh karena itu dalam penelitian ini digunakan model ARMAdan GARCH pada indeks harga saham PT Indofood Sukses Makmur Tbk. Dari analisisyang dilakukan didapatkan model terbaik adalah ARMA(3,1) dan GARCH(1,1).Berdasarkan estimasi VaR diperoleh bahwa dengan taraf kepercayaan 95% kerugianmaksimum yang mungkin dialami investor setelah berinvestasi Rp. 50:000:000; 00 adalahsebesar Rp. 1:219:588; 00.Kata Kunci: Model AR, Model MA, Model ARMA, Value at Risk (VaR), Model GARCH
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "ARMA-GARCH Model"

1

Huang, Xiaoyan. "Predicting Short-Term Exchange Rates with a Hybrid PPP/UIP Model." Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/236.

Full text
Abstract:
This study creates a model to predict short-term exchange rates as a combination of the relative purchasing power parity model (Grossman and Simpson 2011) and the interest power parity model. I then use the statistical techniques ARMA and GARCH to account for the variance of the terms. Previous works considered the effects of these models individually, but mine consider them in unison. I consider both in-sample and out-of-sample tests. I use data on five major exchange rates (JPY/USD, CAD/USD, CHF/USD, GBP/USD, and AUD/USD) sampled at a monthly frequency from 1989-2013. My model statistically significantly predicts these exchange rates over the January 2012 to January 2013 period.
APA, Harvard, Vancouver, ISO, and other styles
2

Qu, Jing. "Market and Credit Risk Models and Management Report." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/649.

Full text
Abstract:
This report is for MA575: Market and Credit Risk Models and Management, given by Professor Marcel Blais. In this project, three different methods for estimating Value at Risk (VaR) and Expected Shortfall (ES) are used, examined, and compared to gain insightful information about the strength and weakness of each method. In the first part of this project, a portfolio of underlying assets and vanilla options were formed in an Interactive Broker paper trading account. Value at Risk was calculated and updated weekly to measure the risk of the entire portfolio. In the second part of this project, Value at Risk was calculated using semi-parametric model. Then the weekly losses of the stock portfolio and the daily losses of the entire portfolio were both fitted into ARMA(1,1)-GARCH(1,1), and the estimated parameters were used to find their conditional value at risks (CVaR) and the conditional expected shortfalls (CES).
APA, Harvard, Vancouver, ISO, and other styles
3

Shimizu, Kenichi. "Bootstrapping stationary ARMA-GARCH models." Wiesbaden Vieweg + Teubner, 2009. http://d-nb.info/996781153/04.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wallin, Edvin, and Timothy Chapman. "A heteroscedastic volatility model with Fama and French risk factors for portfolio returns in Japan." Thesis, Stockholms universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-194779.

Full text
Abstract:
This thesis has used the Fama and French five-factor model (FF5M) and proposed an alternative model. The proposed model is named the Fama and French five-factor heteroscedastic student's model (FF5HSM). The model utilises an ARMA model for the returns with the FF5M factors incorporated and a GARCH(1,1) model for the volatility. The FF5HSM uses returns data from the FF5M's portfolio construction for the Japanese stock market and the five risk factors. The portfolio's capture different levels of market capitalisation, and the factors capture market risk. The ARMA modelling is used to address the autocorrelation present in the data. To deal with the heteroscedasticity in daily returns of stocks, a GARCH(1,1) model has been used. The order of the GARCH-model has been concluded to be reasonable in academic literature for this type of data. Another finding in earlier research is that asset returns do not follow the assumption of normality that a regular regression model assumes. Therefore, the skewed student's t-distribution has been assumed for the error terms. The result of the data indicates that the FF5HSM has a better in-sample fit than the FF5M. The FF5HSM addresses heteroscedasticity and autocorrelation in the data and minimises them depending on the portfolio. Regardingforecasting, both the FF5HSM and the FF5M are accurate models depending on what portfolio the model is applied on.
APA, Harvard, Vancouver, ISO, and other styles
5

Sze, Mei Ki. "Mixed portmanteau test for ARMA-GARCH models /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?MATH%202009%20SZE.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Mori, Renato Seiti. "Mensuração de risco de mercado com modelo Arma-Garch e distribuição T assimétrica." reponame:Repositório Institucional do FGV, 2017. http://hdl.handle.net/10438/18818.

Full text
Abstract:
Submitted by RENATO MORI (rmori3@hotmail.com) on 2017-09-20T05:58:01Z No. of bitstreams: 1 dissertacao_VaRArmaGarchSkewt.pdf: 3267680 bytes, checksum: 6a8a935c128bb04a8a4f91fb592de3a8 (MD5)
Approved for entry into archive by Thais Oliveira (thais.oliveira@fgv.br) on 2017-09-20T17:58:58Z (GMT) No. of bitstreams: 1 dissertacao_VaRArmaGarchSkewt.pdf: 3267680 bytes, checksum: 6a8a935c128bb04a8a4f91fb592de3a8 (MD5)
Made available in DSpace on 2017-09-21T13:36:32Z (GMT). No. of bitstreams: 1 dissertacao_VaRArmaGarchSkewt.pdf: 3267680 bytes, checksum: 6a8a935c128bb04a8a4f91fb592de3a8 (MD5) Previous issue date: 2017-08-22
A proposta do estudo é aplicar ao Ibovespa, modelo paramétrico de VaR de 1 dia, com distribuição dos retornos dinâmica, que procura apreciar características empíricas comumente apresentadas por séries financeiras, como clusters de volatilidade e leptocurtose. O processo de retornos é modelado como um ARMA com erros GARCH que seguem distribuição t assimétrica. A metodologia foi comparada com o RiskMetrics e com modelos ARMA-GARCH com distribuição dos erros normal e t. Os modelos foram estimados diariamente usando uma janela móvel de 1008 dias. Foi verificado pelos backtests de Christoffersen e de Diebold, Gunther e Tay que dentre os modelos testados, o ARMA(2,2)- GARCH(2,1) com distribuição t assimétrica apresentou os melhores resultados.
The proposal of the study is to apply to Ibovespa a 1 day VaR parametric model, with dynamic distribution of returns, that aims to address empirical features usually seen in financial series, such as volatility clustering and leptocurtosis. The returns process is modeled as an ARMA with GARCH residuals that follow a skewed t distribution. The methodology was compared to RiskMetrics and to ARMA-GARCH with normal and t distributed residuals. The models were estimated every daily period using a window of 1008 days. By the backtests of Christoffersen and Diebold, Gunther and Tay, among the tested models, the ARMA(2,2)-GARCH(2,1) with skewed t distribution has given the best results.
APA, Harvard, Vancouver, ISO, and other styles
7

Ebert, Michael. "Preisprognosen an europäischen Spotmärkten für Elektrizität." [S.l. : s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB12103664.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Oliver, Muncharaz Javier. "MODELIZACIÓN DE LA VOLATILIDAD CONDICIONAL EN ÍNDICES BURSÁTILES : COMPARATIVA MODELO EGARCH VERSUS RED NEURONAL BACKPROPAGATION." Doctoral thesis, Editorial Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/35803.

Full text
Abstract:
El siguiente proyecto de tesis pretende mostrar y verificar cómo las redes neuronales, en concreto, la red backpropagation son una alternativa para la predicción de la volatilidad condicional frente a los modelos econométricos clásicos de la familia GARCH. El estudio se realiza para diferentes índices bursátilies de diferentes tamaños y zonas geográficas, así como para datos tanto diarios como de alta frecuencia utilizando para la comparativa uno de los modelos más extendidos para el estudio de la volatildiad condicional en índices bursátiles como el EGARCH, dada la existencia comprobada de asimetrías en la volatildiad de dichos índices. La elección de la red neuronal backpropagation viene motivada por ser una de las redes neuronales más extendidas en su uso en finanzas por su capacidad de generalización método de aprendizaje basada en la relga delta generalizada.
Oliver Muncharaz, J. (2014). MODELIZACIÓN DE LA VOLATILIDAD CONDICIONAL EN ÍNDICES BURSÁTILES : COMPARATIVA MODELO EGARCH VERSUS RED NEURONAL BACKPROPAGATION [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/35803
Alfresco
APA, Harvard, Vancouver, ISO, and other styles
9

Strohe, Hans Gerhard. "Time series analysis : textbook for students of economics and business administration ; [part 2]." Universität Potsdam, 2004. http://stat.wiso.uni-potsdam.de/documents/zeitr/Time_Series_Analysis_Script2.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

hua, wu ching, and 吳晴華. "Analysis of RMB’s Exchange Rate Floating：Application of ARMA-GARCH Model." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/02867589526989931301.

Full text
Abstract:
碩士
清雲科技大學
經營管理研究所
95
Mainland China keep reducing the currency under the standard value since its economical development intermediate stage. Because China is the export country under the weak monetary policy, the exporting product price is more competitive which is similar to the export oriented policy. Due to the advantage of Mainland China export trade continues to grow, Driving Taiwan’s the hot money goes to China .The favorable balance of trade keep increasing, however Taiwan and the mainland mutually dependent highly. No matter Taiwanese businessman, who is trading with mainland China in Taiwan, or directly trading in the mainland, the Renminbi exchange rate will impact on their business. Therefore grasping the change of the Renminbi exchange rate becomes urgent. This paper discusses exchange rate statistical characteristics and its econometrics by reading the Renminbi exchange rate path and using the ARMA-GARCH to establish exchange rate model. We discovered the Renminbi exchange rate presents continues small revaluation. Further we can forecast the trend of the Renminbi exchange rate and the undulation in the short term. Renminbi exchange rate by using ones differencing estimated parameters is significant。Using estimated models to simulate the tendency of the characteristics of the Renminbi sequence, and all there models present good export forecast performance.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "ARMA-GARCH Model"

1

Shimizu, Kenichi. Bootstrapping Stationary ARMA-GARCH Models. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

service), SpringerLink (Online, ed. Bootstrapping Stationary ARMA-GARCH Models. Wiesbaden: Vieweg+Teubner Verlag / Springer Fachmedien Wiesbaden GmbH, Wiesbaden, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Paolella, Marc S. Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH. Wiley & Sons, Incorporated, John, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "ARMA-GARCH Model"

1

Sucarrat, Genaro. "The log-GARCH model via ARMA representations." In Financial Mathematics, Volatility and Covariance Modelling, 336–59. Abingdon, Oxon ; New York, NY : Routledge, 2019. | Series: Routledge advances in applied financial econometrics ; Volume 2: Routledge, 2019. http://dx.doi.org/10.4324/9781315162737-14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Shimizu, Kenichi. "Introduction." In Bootstrapping Stationary ARMA-GARCH Models, 1–7. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Shimizu, Kenichi. "Bootstrap Does not Always Work." In Bootstrapping Stationary ARMA-GARCH Models, 9–17. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Shimizu, Kenichi. "Parametric AR(p)-ARCH(q) Models." In Bootstrapping Stationary ARMA-GARCH Models, 19–64. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Shimizu, Kenichi. "Parametric ARMA(p, q)- GARCH(r, s) Models." In Bootstrapping Stationary ARMA-GARCH Models, 65–83. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Shimizu, Kenichi. "Semiparametric AR(p)-ARCH(1) Models." In Bootstrapping Stationary ARMA-GARCH Models, 85–126. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Çevik, Emre, Suzan Kantarcı Savaş, and Esin Cumhur Yalçın. "Comparative Analysis of Value at Risk(VaR) of MSCI-EMI With Traditional Time Series Methods and ANN." In Financial Management and Risk Analysis Strategies for Business Sustainability, 34–57. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7634-2.ch003.

Full text
Abstract:
In this chapter, the VaR of the MSCI emerging market index (MSCI-EMI) developed by Morgan Stanley Capital International (MSCI) is estimated using linear, nonlinear time series and ANN. In this context, the aim of the study is to estimate the VaR exceedance of the MSCI-EMI as a global financial risk indicator compared with traditional time series methods and ANN. In addition, the most effective method on this index is determined by statistical information criteria, and the comparative evaluation of the model selection criteria is carried out. The period of analysis is between December 1987-April 2020 with monthly frequency and VaR exceedance obtained with ARMA-GARCH, TGARCH, EGARCH, GJR, and ANN models. Confidence levels of models, VaR exceedance, and Kupeic statistics are obtained. VaR exceedances are examined through the superior model.
APA, Harvard, Vancouver, ISO, and other styles
8

"Other Financial Models: From ARMA to the GARCH Family." In Mathematics of Financial Markets, 165–74. Chichester, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118818510.ch9.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "ARMA-GARCH Model"

1

Wang, Weiqiang, Ying Guo, Zhendong Niu, and Yujuan Cao. "Stock indices analysis based on ARMA-GARCH model." In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2009. http://dx.doi.org/10.1109/ieem.2009.5373131.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

Nguyen-Hong, Nhung, and Nakanishi Yosuke. "Stochastic dynamic power flow analysis based on stochastic response surfarce method and ARMA-GARCH model." In 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2017. http://dx.doi.org/10.1109/isgt.2017.8086059.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Caesarendra, Wahyu, Achmad Widodo, Hong Thom Pham, and Bo-Suk Yang. "Machine degradation prognostic based on RVM and ARMA/GARCH model for bearing fault simulated data." In 2010 Prognostics and System Health Management Conference (PHM). IEEE, 2010. http://dx.doi.org/10.1109/phm.2010.5414586.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Ding, Yang. "Empirical Analysis of Logarithmic Return Rate of China’s Financial Stocks—based on the ARMA-GARCH Model." In Proceedings of the 2018 International Symposium on Social Science and Management Innovation (SSMI 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/ssmi-18.2019.51.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Jonas, M. "The Application of the Time Series Theory to Processing Data From the SBAS Receiver in Safety Mode." In 2012 Joint Rail Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/jrc2012-74033.

Full text
Abstract:
Before satellite-based augmentation systems (SBAS) such as the Wide Area Augmentation System (WAAS) in the USA, and the European Geostationary Navigation Overlay Service (EGNOS), will be used in railway safety-related applications, it is necessary to determine reliability attributes of these systems as quality measures from the user’s point of view. It is necessary to find new methods of processing data from the SBAS system in accordance with strict railway standards. For this purposes data from the SBAS receiver with the Safety of Life Service was processed by means of the time series theory. At first, a basic statistic exploration analysis by means of histograms and boxplot graphs was done. Then correlation analysis by autocorrelation (ACF), and partial autocorrelation functions (PACF), was done. Statistical tests for the confirmation of non-stationarity, and conditional heteroscedasticity of time series were done. Engle’s ARCH test confirmed that conditional heteroscedasticity is contained. ARMA/GARCH models were constructed, and their residuals were analyzed. Autocorrelation functions and statistical tests of models residuals were done. The analysis implies that the models well cover the variance volatility of investigated time series and so it is possible to use the ARMA/GARCH models for the modeling of SBAS receiver outputs.
APA, Harvard, Vancouver, ISO, and other styles
7

LING, SHIQING. "MLE FOR CHANGE-POINT IN ARMA-GARCH MODELS WITH A CHANGING DRIFT." In Proceedings of a Workshop. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702715_0011.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sedláková, Markéta. "APPLICATION OF ARMA AND GARCH MODELS ON TIME SERIES OF KOMERČNÍ BANKA STOCKS." In 16th International Bata Conference for Ph.D. Students and Young Researchers. Tomas Bata University in Zlín, 2020. http://dx.doi.org/10.7441/dokbat.2020.40.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Al-Sharoot, Muhammad H., and Omar M. Alramadhan. "Forecasting the gas prices in Investing.com’s weekly economic data table using linear and non-linear ARMA-GARCH models for period 2016-2018." In SECOND INTERNATIONAL CONFERENCE OF MATHEMATICS (SICME2019). Author(s), 2019. http://dx.doi.org/10.1063/1.5097818.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'ARMA-GARCH Model' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography