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Journal articles on the topic 'ARMA-GARCH Model'

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Published: 4 June 2021
Last updated: 1 February 2022

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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.

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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.
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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.

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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.
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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.

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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%.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.

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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.
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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.

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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.

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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
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11

Lachout, Petr. "Weak consistency of estimators in linear regression model." Tatra Mountains Mathematical Publications 51, no. 1 (November 1, 2012): 91–100. http://dx.doi.org/10.2478/v10127-012-0010-3.

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ABSTRACT A linear regression model and M-estimator of its regression coefficients are considered. We present a derivation of a weak consistency of the M-estimator together with a rate. Derivation is made under general conditions set on the error term, say “asymptotic stationarity” property. The results are proved by means of L2-convergence and cover the cases as the error term is ARMA, ARCH, GARCH process or it is attracted by an ARMA, ARCH, GARCH process. We do not separate random and deterministic covariates. Both cases are treated in one general setting.
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12

Wang, W., P. H. A. J. M. Van Gelder, J. K. Vrijling, and J. Ma. "Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes." Nonlinear Processes in Geophysics 12, no. 1 (January 21, 2005): 55–66. http://dx.doi.org/10.5194/npg-12-55-2005.

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Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.
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Ling, Shiqing. "On the probabilistic properties of a double threshold ARMA conditional heteroskedastic model." Journal of Applied Probability 36, no. 03 (September 1999): 688–705. http://dx.doi.org/10.1017/s0021900200017502.

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Following Tweedie (1988), this paper constructs a special test function which leads to sufficient conditions for the stationarity and finiteness of the moments of a general non-linear time series model, the double threshold ARMA conditional heteroskedastic (DTARMACH) model. The results are applied to two well-known special cases, the GARCH and threshold ARMA (TARMA) models. The condition for the finiteness of the moments of the GARCH model is simple and easier to check than the condition given by Milhøj (1985) for the ARCH model. The condition for the stationarity of the TARMA model is identical to the condition given by Brockwell et al. (1992) for a special case, and verifies their conjecture that the moving average component does not affect the stationarity of the model. Under an additional irreducibility assumption, the geometric ergodicity of the GARCH and TARMA models is also established.
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Ling, Shiqing. "On the probabilistic properties of a double threshold ARMA conditional heteroskedastic model." Journal of Applied Probability 36, no. 3 (September 1999): 688–705. http://dx.doi.org/10.1239/jap/1032374627.

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Following Tweedie (1988), this paper constructs a special test function which leads to sufficient conditions for the stationarity and finiteness of the moments of a general non-linear time series model, the double threshold ARMA conditional heteroskedastic (DTARMACH) model. The results are applied to two well-known special cases, the GARCH and threshold ARMA (TARMA) models. The condition for the finiteness of the moments of the GARCH model is simple and easier to check than the condition given by Milhøj (1985) for the ARCH model. The condition for the stationarity of the TARMA model is identical to the condition given by Brockwell et al. (1992) for a special case, and verifies their conjecture that the moving average component does not affect the stationarity of the model. Under an additional irreducibility assumption, the geometric ergodicity of the GARCH and TARMA models is also established.
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Aminuddin Jafry, Nurul Hanis, Ruzanna Ab Razak, and Noriszura Ismail. "Dependence Modelling using GARCH, EGARCH, and Copula Models:." Asia Proceedings of Social Sciences 2, no. 2 (December 3, 2018): 55–59. http://dx.doi.org/10.31580/apss.v2i2.317.

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Copula become a popular tool to measure the dependency between financial data due to its ability to capture the non-normal distributions. Hence, this paper will inspect the impact of input models towards the parameter estimation of marginal and copula models for KLCI and FBMHS returns series by considering the ARMA-GARCH model and the ARMA-EGARCH model. This study also investigates the dependency of Islamic-conventional pair for Malaysia indices by using static copula and time-varying copula approach. The closing prices of Malaysia indices represented by KLCI (conventional) index and FBMHS (Islamic) index for the period of 21 May 2007 until 28 September 2018 are used as a sample data. The results show that KLCI-FBMHS pair is strongly correlated, different input models (ARMA-GARCH and ARMA-EGARCH) have identical dependence structure but slightly different value of parameter estimated, and the time-varying Gaussian copula is chosen as the best dependence model. Finding suggest that the diversification between Islamic-conventional pair is worthwhile during stable period.
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16

R, Adellara Mutya, Maiyastri Maiyastri, and Yudiantri Asdi. "PENENTUAN PORTOFOLIO DAN VALUE AT RISK MENGGUNAKAN MODEL ARMA-GARCH." Jurnal Matematika UNAND 8, no. 1 (July 5, 2019): 1. http://dx.doi.org/10.25077/jmu.8.1.1-8.2019.

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Dalam dunia investasi saham merupakan bentuk yang paling populer di kalangan masyarakat. Pada saham terdapat nilai risiko dan nilai ekspektasi return yang perlu dipertimbangkan oleh investor. Nilai Ekspektasi return dapat dihitung menggunakan model analisis deret waktu yaitu ARMA, sedangkan nilai risiko dapat diukur menggunakan beberapa metode salah satunya adalah metode Value at Risk (VaR). Untuk menghitung VaR diperlukan komponen volatilitas. Volatilitas dapat diestimasi menggunakan analisis deret waktu yaitu GARCH. Pada penelitian ini, peramalan dilakukan menggunakan data harga penutupan saham PT Astra Internasional Tbk, PT Bank Central Asia Tbk, PT Bank Negara Indonesia Tbk, PT Bank Rakyat Indonesia Tbk, dan PT Telekomunikasi Indonesia Tbk. Model terbaik yang didapatkan untuk mengestimasi nilai ekspektasi retrun diantaranya MA(1) untuk PT Astra Internasional Tbk, AR(1) untuk PT Bank Central Asia Tbk, ARMA(1,1) untuk PT Bank Rakyat Indonesia Tbk, MA(1) untuk PT Bank Rakyat Indonesia Tbk, dan MA(1) untuk PT Telekomunikasi Indonesia. Sedangkan model terbaik untuk mengestimasi nilai volatilitas adalah GARCH(1,1) untuk masing-masing perusahaan. Dengan menggunakan model ARMA-GARCH yang telah diestimasi diperoleh nilai VaR terbesar sampai terkecil secara berturut-turut terjadi pada saham PT Bank Negara Indonesia Tbk, PT Astra Internasional Tbk, PT Bank Rakyat Indonesia Tbk, PT Telekomunikasi Indonesia Tbk, dan PT Bank Central Asia Tbk. Bobot portofolio yang diperoleh adalah 5.47% untuk saham PT Astra Internasional Tbk, 44.52% untuk saham PT Bank Central Asia Tbk, 1.49% untuk saham PT Bank Negara Indonesia Tbk, 6.48% untuk saham PT Bank Rakyat Indonsia Tbk, dan 42.02% untuk saham PT Telekomunikasi Indonesia Tbk.Kata Kunci: VaR, Portofolio, ARMA, GARCH
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Saadah, Nurul, Maiyastri ., and Hazmira Yozza. "PERBANDINGAN RESIKO INVESTASI BANK CENTRAL ASIA DAN BANK MANDIRI MENGGUNAKAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH)." Jurnal Matematika UNAND 5, no. 4 (November 29, 2016): 80. http://dx.doi.org/10.25077/jmu.5.4.80-88.2016.

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Abstrak. Data return saham adalah salah satu data deret waktu. Jika ingin melakukanpemodelan return, maka dapat dilakukan pemodelan deret waktu. Model rataan returnmenggunakan model Autoregressive Moving Average (ARMA). Sedangkan untuk memodelkanragam digunakan model Generalized Autoregressive Conditional Heteroscedasticity(GARCH). Setelah melakukan beberapa tahapan diperoleh model ARMA(1,0) danGARCH(1,1) sebagai model terbaik untuk data return saham Bank Central Asia. Sedangkanmodel terbaik untuk data return saham Bank Mandiri adalah model ARMA(0,1)dan GARCH(1,1). Model yang diperoleh digunakan untuk melakukan peramalan returndan volatilitas dalam pengukuran resiko. Salah satu alat ukur yang digunakan untukmengukur resiko adalah Value at Risk. Dari perhitungan resiko untuk kedua bank diperolehbahwa resiko maksimum Bank Mandiri lebih besar dari resiko maksimum BankCentral Asia.
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Chae, Wha-Yeon, Bo-Seung Choi, Kee-Whan Kim, and You-Sung Park. "A Study for Forecasting Methods of ARMA-GARCH Model Using MCMC Approach." Korean Journal of Applied Statistics 24, no. 2 (April 30, 2011): 293–305. http://dx.doi.org/10.5351/kjas.2011.24.2.293.

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Huang, Zifeng, and Ming Gu. "Characterizing Nonstationary Wind Speed Using the ARMA-GARCH Model." Journal of Structural Engineering 145, no. 1 (January 2019): 04018226. http://dx.doi.org/10.1061/(asce)st.1943-541x.0002211.

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20

Huang, Xiaowei, Mei Yu, and Chengwei Ban. "Nonlinear Dynamics of International Gold Prices: Conditional Heteroskedasticity or Chaos?" Journal of Systems Science and Information 2, no. 5 (October 25, 2014): 411–27. http://dx.doi.org/10.1515/jssi-2014-0411.

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AbstractTaking the special nonlinear characteristics of the domestic and international gold price into account, this paper systematically analyzed its nonlinearity by the methods of BDS test, R/S analysis and improved largest Lyapunov exponent. We find three main results: (1) ARMA-GARCH model could adequately explain the linear and nonlinear dependence of gold price series; (2) long-memory does not exist anymore in price series explained by ARMA-GARCH model; (3) chaos phenomenon which is sensitive to the initial value does not exist either in the residuals of regression model. Therefore, we believe that the nonlinearity of gold price is mainly characterized in conditional heteroscedasticity rather than chaos.
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Jati (Kementerian Perdagangan), Kumara. "ANALISIS EFEK MUSIM HUJAN DAN KEMARAU TERHADAP HARGA BERAS." JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK 2, no. 1 (May 31, 2018): 37. http://dx.doi.org/10.30988/jmil.v2i1.68.

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<p><em>Penelitian ini menganalisis efek dari musim hujan dan kemarau terhadap harga beras. Metode yang digunakan yaitu Autoregressive and Moving Average (ARMA) dan Autoregressive Conditional Heteroskedasticity/Generalized Autoregressive Conditional Heteroskedasticity (ARCH/GARCH) dengan variabel dummy. Data yang digunakan yaitu stok dan harga beras harian dari 29 Januari 2014 sampai dengan 29 Januari 2018. Penggunaan model ARMA-ARCH/GARCH dapat menunjukkan bahwa model ini bisa untuk membantu melihat pola pergerakan harga beras. Model ARMA (0,1)-ARCH (1) dengan variabel dummy yaitu musim kemarau secara signifikan ternyata lebih mempengaruhi kondisional varians harga beras dibandingkan dengan variabel dummy musim hujan. Pemangku kepentingan perlu lebih memperhatikan fluktuasi harga beras terutama apabila pada musim kemarau karena pasokan beras relatif lebih sedikit dan hanya terjadi panen puso.</em><em></em></p>
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Zhang, Yang, Yidong Peng, Xiuli Qu, Jing Shi, and Ergin Erdem. "A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications." Energies 14, no. 9 (April 21, 2021): 2352. http://dx.doi.org/10.3390/en14092352.

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Enhancing forecasting performance in terms of both the expected mean value and variance has been a critical challenging issue for energy industry. In this paper, the novel methodology of finite mixture Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) approach with Expectation–Maximization (EM) algorithm is introduced. The applicability of this methodology is comprehensively evaluated for the forecasting of energy related time series including wind speed, wind power generation, and electricity price. Its forecasting performances are evaluated by various criteria, and also compared with those of the conventional AutoRegressive Moving-Average (ARMA) model and the less conventional ARMA-GARCH model. It is found that the proposed mixture GARCH model outperforms the other two models in terms of volatility modeling for all the energy related time series considered. This is proven to be statistically significant because the p-values of likelihood ratio test are less than 0.0001. On the other hand, in terms of estimations of mean wind speed, mean wind power output, and mean electricity price, no significant improvement from the proposed model is obtained. The results indicate that the proposed finite mixture GARCH model is a viable approach for mitigating the associated risk in energy related predictions thanks to the reduced errors on volatility modeling.
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Yi, Jun, Hong Ming Yang, Ming Yong Lai, and Shu Kui Li. "The Insulator’s Pollution Raining Flashover Forecast under GARCH-Based Forecast of Rainstorm Disasters1." Advanced Materials Research 143-144 (October 2010): 566–70. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.566.

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In this paper,it proposes insulator flashover probability prediction model based on Markov chain and ARMA-GARCH. It provides a foundation for risk evaluation of rainstorm of power system. First,this model daily precipitation forecasting model which combined Markov chain with ARMA-GARCH based on self-dependency and time-varying of atmosphere factor; and then according to rainfall’s impact on insulator’s pollution raining flashover,the paper raises the probability prediction model of insulator flashover;Ultimately, we can predict the insulator flashover’s probability. Through Historical data of Hunan Grid to calculate the probability of flashover, and compares with the actual situation it shows that the method can achieve rapid, accurate prediction of power insulator flashover probability.
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Niedzielski, Tomasz, and Wieslaw Kosek. "An Application of Low-Order Arma and Garch Models for Sea Level Fluctuations." Artificial Satellites 45, no. 1 (January 1, 2010): 27–39. http://dx.doi.org/10.2478/v10018-010-0003-x.

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An Application of Low-Order Arma and Garch Models for Sea Level FluctuationsThe paper presents the analysis of geographically-dependent irregular sea level fluctuations, often referred to as residual terms around deterministic signals, carried out by means of stochastic low-order autoregressive moving average (ARMA) and generalised autoregressive conditional heteroscedastic (GARCH) models. The gridded sea level anomaly (SLA) time series from TOPEX/Poseidon (T/P) and Jason-1 (J-1) satellite altimetry, commencing on 10th January 1993 and finishing on 14th July 2003, has been examined. The aforementioned models, limited to low-orders being combinations of 0,1 and 2, have been fitted to the SLA data. The root mean square and the Shapiro-Wilk test for the normal distribution have been used to calculate statistics of the residuals from these models. It has been found that autoregressive (AR) models as well as ARMA ones serve well the purpose of adequate modelling irregular sea level fluctuations, with a successful fit in some patchy bits of the equatorial Pacific. In contrast, GARCH models have been shown to be rather inaccurate, specifically in the vicinity of the tropical Pacific, in the North Pacific and in the equatorial Indian Ocean. The pattern of the Tropical Instability Waves (TIWs) has been noticed in the statistics of AR and ARMA model residuals indicating that the dynamics of these waves cannot be captured by the aforementioned linear stochastic processes.
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Arslan, Muhammad, Wajid Shakeel Ahmed, and Mansoor Akhter. "Volatility, Global Proxy Index, V-A-R: Empirical Study on Pakistan And China Stock Exchanges." International Journal of Advances in Data and Information Systems 1, no. 2 (May 15, 2020): 103–15. http://dx.doi.org/10.25008/ijadis.v1i2.183.

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This study postulates that propose global proxy index is a significant conduit to evaluate the shocks in volatile stock markets i.e. PSX and SSE, alike. The two separate models i.e. Log-GARCH (1, 1) and ARMA-GARCH (1, 1) have been used along with the value at risk (V-a-R) @ 5% criteria for choosing best-fitted model. The study results showed Log-GARCH (1, 1) model proves to the best. This study results are not driven by political-level risks and thus independent study can be conducted to evaluate the detrimental consequences on investment opportunities under volatile environments.
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Makoni, Tendai, and Delson Chikobvu. "Modelling International Tourist Arrivals Volatility in Zimbabwe Using a GARCH Process." April 2021, Volume 10(2) (April 30, 2021): 639–53. http://dx.doi.org/10.46222/ajhtl.19770720-123.

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The aim of the paper was to develop bootstrap prediction intervals for international tourism demand and volatility in Zimbabwe after modelling with an ARMA-GARCH process. ARMA-GARCH models have better forecasting power and are capable of capturing and quantifying volatility. Bootstrap prediction intervals can account for future uncertainty that arises through parameter estimation. The monthly international tourism data obtained from the Zimbabwe Tourism Authority (ZTA) (January 2000 to June 2017) is neither seasonal nor stationary and is made stationery by taking a logarithm transformation. An ARMA(1,1) model fits well to the data; with forecasts indicating a slow increase in international tourist arrivals (outside of the Covid-19 period). The GARCH(1,1) process indicated that unexpected tourism shocks will significantly impact the Zimbabwe international tourist arrivals for longer durations. Volatility bootstrap prediction intervals indicated minimal future uncertainty in international tourist arrivals. For the Zimbabwe tourism industry to remain relevant, new tourism products and attraction centres need to be developed, as well as embarking on effective marketing strategies to lure even more tourists from abroad. This will go a long way in increasing the much-needed foreign currency earnings needed to revive the Zimbabwean economy.
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Wang, HongRui, Xiong Gao, LongXia Qian, and Song Yu. "Uncertainty analysis of hydrological processes based on ARMA-GARCH model." Science China Technological Sciences 55, no. 8 (June 29, 2012): 2321–31. http://dx.doi.org/10.1007/s11431-012-4909-3.

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liu, Qi, Guanlan Zhang, Shahzad Ali, Xiaopeng Wang, Guodong Wang, Zhenkuan Pan, and Jiahua Zhang. "SPI-based drought simulation and prediction using ARMA-GARCH model." Applied Mathematics and Computation 355 (August 2019): 96–107. http://dx.doi.org/10.1016/j.amc.2019.02.058.

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Esenyel, Nimet Melis, and Melda Akın. "Comparing Accuracy Performance of ELM, ARMA and ARMA-GARCH Model In Predicting Exchange Rate Return." Alphanumeric Journal 5, no. 1 (June 30, 2017): 1. http://dx.doi.org/10.17093/alphanumeric.298658.

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Layla, Nur Najmi, Eti Kurniati, and Didi Suhaedi. "Peramalan Indeks Harga Saham dengan Autoregressive Moving Average Generelized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH)." Jurnal Riset Matematika 1, no. 1 (July 6, 2021): 7–12. http://dx.doi.org/10.29313/jrm.v1i1.103.

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Abstract. The stock price index is the information the public needs to know the development of stock price movements. Stock price forecasting will provide a better basis for planning and decision making. The forecasting model that is often used to model financial and economic data is the Autoregressive Moving Average (ARMA). However, this model can only be used for data with the assumption of stationarity to variance (homoscedasticity), therefore an additional model is needed that can model data with heteroscedasticity conditions, namely the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. This study uses data partitioning in pre-pandemic conditions and during the pandemic, Insample data with pre-pandemic conditions and insample data during pandemic conditions. Based on the research results, the GARCH model (1,1) was obtained with the conditions before the pandemic and GARCH (1,2) during the pandemic condition. The forecasting model obtained has met the eligibility requirements of the GARCH model. If the forecasting model fulfills the eligibility requirements, then MAPE calculations are performed to see the accuracy of the forecasting model. And obtained MAPE in the conditions before the pandemic and during the pandemic in the very good category. Abstrak. Indeks harga saham merupakan informasi yang diperlukan masyarakat untuk mengetahui perkembangan pergerakan harga saham. Peramalan harga saham akan memberikan dasar yang lebih baik bagi perencanaan dan pengambilan keputusan. Model peramalan yang sering digunakan untuk memodelkan data keuangan dan ekonomi adalah Autoregrresive Moving Average (ARMA). Namun model tersebut hanya dapat digunakan untuk data dengan asumsi stasioneritas terhadap varian (homoskedastisitas), oleh karena itu diperlukan suatu model tambahan yang bisa memodelkan data dengan kondisi heteroskedastisitas, yaitu model Generalized Autoregressive Conditional Heteroscedastisity (GARCH). Penelitian ini menggunakan partisi data pada kondisi sebelum pandemi dan saat pandemi berlangsung data Insample dengan kondisi sebelum pandemi dan insample pada kondisi pandemi. Berdasarkan hasil penelitian, maka didapat model GARCH (1,1) dengan kondisi sebelum pandemi dan GARCH (1,2) saat kondisi pandemi. Model peramalan yang didapat sudah memenuhi syarat kelayakan model GARCH. Apabila model peramalan terpenuhi syarat kelayakannya maka dilakukan perhitungan MAPE untuk melihat keakuratan model peramalannya. Dan diperoleh MAPE pada kondisi sebelum pandemi dan saat pandemi dengan kategori sangat baik.
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Guo, Zhenhai, Yao Dong, Jianzhou Wang, and Haiyan Lu. "The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area." Mathematical Problems in Engineering 2010 (2010): 1–17. http://dx.doi.org/10.1155/2010/684742.

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Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the first definite season index method and the Autoregressive Moving Average (ARMA) models or the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) forecasting models. The forecasting errors are analyzed and compared with the ones obtained from the ARMA, GARCH model, and Support Vector Machine (SVM); the simulation process and results show that the developed method is simple and quite efficient for daily average wind speed forecasting of Hexi Corridor in China.
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Fink, Holger, Andreas Fuest, and Henry Port. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates." Risks 6, no. 3 (August 20, 2018): 84. http://dx.doi.org/10.3390/risks6030084.

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A functional ARMA-GARCH model for predicting the value-at-risk of the EURUSD exchange rate is introduced. The model implements the yield curve differentials between EUR and the US as exogenous factors. Functional principal component analysis allows us to use the information of basically the whole yield curve in a parsimonious way for exchange rate risk prediction. The data analyzed in our empirical study consist of the EURUSD exchange rate and the EUR- and US-yield curves from 15 August 2005–30 September 2016. As a benchmark, we take an ARMA-GARCH and an ARMAX-GARCHX with the 2y-yield difference as the exogenous variable and compare the forecasting performance via likelihood ratio tests. However, while our model performs better in one situation, it does not seem to improve the performance in other setups compared to its competitors.
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Pham, Hong Thom, and Bo-Suk Yang. "Estimation and forecasting of machine health condition using ARMA/GARCH model." Mechanical Systems and Signal Processing 24, no. 2 (February 2010): 546–58. http://dx.doi.org/10.1016/j.ymssp.2009.08.004.

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Lee, Sangyeol, and Taewook Lee. "Value-at-risk forecasting based on Gaussian mixture ARMA–GARCH model." Journal of Statistical Computation and Simulation 81, no. 9 (September 2011): 1131–44. http://dx.doi.org/10.1080/00949651003752320.

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Liko, Rozana. "Modeling the Behavior of Inflation Rate in Albania Using Time Series." JOURNAL OF ADVANCES IN MATHEMATICS 13, no. 3 (July 30, 2017): 7257–63. http://dx.doi.org/10.24297/jam.v13i3.6196.

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In this paper, time series theory is used to modelling monthly inflation data in Albania during the period from January 2000 to December 2016. The autoregressive conditional heteroscedastic (ARCH) and their extensions, generalized autoregressive conditional heteroscedasticity (GARCH)) models are used to better fit the data. The study reveals that the inflation series is stationary, non-normality and has serial correlation. Based on minimum AIC and SIC values the best model turn to be GARCH (1, 1) model with mean equation ARMA (2, 1)x(2, 0)12. Based on the selected model one year of inflation is forecasted (from January 2016 to December 2016).
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Gaiduchevici, Gabriel. "A Method for Systemic Risk Estimation Based on CDS Indices." Review of Economic and Business Studies 8, no. 1 (June 1, 2015): 103–24. http://dx.doi.org/10.1515/rebs-2016-0018.

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AbstractThe copula-GARCH approach provides a flexible and versatile method for modeling multivariate time series. In this study we focus on describing the credit risk dependence pattern between real and financial sectors as it is described by two representative iTraxx indices. Multi-stage estimation is used for parametric ARMA-GARCH-copula models. We derive critical values for the parameter estimates using asymptotic, bootstrap and copula sampling methods. The results obtained indicate a positive symmetric dependence structure with statistically significant tail dependence coefficients. Goodness-of-Fit tests indicate which model provides the best fit to data.
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Lee, Yi-Hsi, Ming-Hua Hsieh, Weiyu Kuo, and Chenghsien Jason Tsai. "How can an economic scenario generation model cope with abrupt changes in financial markets?" China Finance Review International 11, no. 3 (May 31, 2021): 372–405. http://dx.doi.org/10.1108/cfri-03-2021-0056.

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PurposeIt is quite possible that financial institutions including life insurance companies would encounter turbulent situations such as the COVID-19 pandemic before policies mature. Constructing models that can generate scenarios for major assets to cover abrupt changes in financial markets is thus essential for the financial institution's risk management.Design/methodology/approachThe key issues in such modeling include how to manage the large number of risk factors involved, how to model the dynamics of chosen or derived factors and how to incorporate relations among these factors. The authors propose the orthogonal ARMA–GARCH (autoregressive moving-average–generalized autoregressive conditional heteroskedasticity) approach to tackle these issues. The constructed economic scenario generation (ESG) models pass the backtests covering the period from the beginning of 2018 to the end of May 2020, which includes the turbulent situations caused by COVID-19.FindingsThe backtesting covering the turbulent period of COVID-19, along with fan charts and comparisons on simulated and historical statistics, validates our approach.Originality/valueThis paper is the first one that attempts to generate complex long-term economic scenarios for a large-scale portfolio from its large dimensional covariance matrix estimated by the orthogonal ARMA–GARCH model.
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Lin, Jeng Hsiang. "Time Series Modeling of Earthquake Ground Motions Using ARMA-GARCH Models." Applied Mechanics and Materials 470 (December 2013): 240–43. http://dx.doi.org/10.4028/www.scientific.net/amm.470.240.

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Engineers are well aware that, due to the stochastic nature of earthquake ground motion, the information obtained from structural response analysis using scant records is quite unreliable. Thus, providing earthquake models for specific sites or areas of research and practical implementation is essential. This paper presents a procedure for the modeling strong earthquake ground motion based on autoregressive moving average (ARMA) models. The Generalized autoregressive conditional heteroskedasticity (GARCH) model is used to simulate the time-varying characteristics of earthquakes.
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Widodo, Dea Manuella, Sudarno Sudarno, and Abdul Hoyyi. "PEMODELAN RETURN HARGA SAHAM MENGGUNAKAN MODEL INTERVENSI–ARCH/GARCH (Studi Kasus : Return Harga Saham PT Bayan Resources Tbk)." Jurnal Gaussian 7, no. 2 (May 30, 2018): 110–18. http://dx.doi.org/10.14710/j.gauss.v7i2.26642.

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The intervention method is a time series model which could be used to model data with extreme fluctuation whether up or down. Stock price return tend to have extreme fluctuation which is caused by internal or external factors. There are two kinds of intervention function; a step function and a pulse function. A step function is used for a long-term intervention, while a pulse function is used for a short-term intervention. Modelling a time series data needs to satisfy the homoscedasticity assumptions (variance of residual is homogeneous). In reality, stock price return has a high volatility, in other words it has a non-constant variance of residuals (heteroscedasticity). ARCH (Autoregressive Conditional Heteroscedasticity) or GARCH (Generalized Autoregressive Conditional Heteroscedasticity) can be used to model data with heteroscedasticity. The data used is stock price return from August 2008 until September 2018. From the stock price return data plot is found an extreme fluctuation in September 2017 (T=110) that is suspected as a pulse function. The best model uses the intervention pulse function is ARMA([1,4],0) (b=0, s=1, r=1). The intervention model has a non-constant variance or there is an ARCH effect. The best variance model obtained is ARMA([1,4],0)(b=0, s=1, r=1)–GARCH(1,1) with the AIC value is -205,75088. Keywords: Stock Return, Intervention, Heteroscedasticity, ARCH/GARCH
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Chandra Pati, Pratap, and Prabina Rajib. "Volatility persistence and trading volume in an emerging futures market." Journal of Risk Finance 11, no. 3 (May 25, 2010): 296–309. http://dx.doi.org/10.1108/15265941011043666.

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PurposeThe purpose of this paper is to estimate time‐varying conditional volatility, and examine the extent to which trading volume, as a proxy for information arrival, explain the persistence of futures market volatility using National Stock Exchange S&P CRISIL NSE Index Nifty index futures.Design/methodology/approachTo estimate the volatility and capture the stylized facts of fat‐tail distribution, volatility clustering, leverage effect, and mean‐reversion in futures returns, appropriate ARMA‐generalized autoregressive conditional heteroscedastic (GARCH) and ARMA‐EGARCH models with generalized error distribution have been used. The ARMA‐EGARCH model is augmented by including contemporaneous and lagged trading volume to determine their contribution to time‐varying conditional volatility.FindingsThe paper finds evidence of leverage effect, which indicates that negative shocks increase the futures market volatility more than positive shocks of the same magnitude. In addition, the results indicate that inclusion of both contemporaneous and lagged trading volume in the GARCH model reduces the persistence in volatility, but contemporaneous volume provides a greater reduction than lagged volume. Nevertheless, the GARCH effect does not completely vanish.Practical implicationsResearch findings have important implications for the traders, regulatory bodies, and practitioners. A positive volume‐price volatility relationship implies that a new futures contract will be successful only to the extent that there is enough price uncertainty associated with the underlying asset. Higher trading volume causes higher volatility; so, it suggests the need for greater regulatory restrictions.Originality/valueEquity derivatives are relatively new phenomena in Indian capital market. This paper extends and updates the existing empirical research on the relationship between futures price volatility and volume in the emerging Indian capital market using improved methodology and recent data set.
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Chen, Liu-Jie, and Ling Yu. "Structural Nonlinear Damage Identification Algorithm Based on Time Series ARMA/GARCH Model." Advances in Structural Engineering 16, no. 9 (September 2013): 1597–609. http://dx.doi.org/10.1260/1369-4332.16.9.1597.

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Lee, Oesook, and Jungwha Lee. "The functional central limit theorem for the multivariate MS–ARMA–GARCH model." Economics Letters 125, no. 3 (December 2014): 331–35. http://dx.doi.org/10.1016/j.econlet.2014.10.002.

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Lee, JeongHo, and YongWoong Lee. "Empirical Analysis on Growth Optimal Portfolio (GOP) Using ARMA-GARCH-DCC Model." Korean Data Analysis Society 23, no. 1 (February 28, 2021): 471–89. http://dx.doi.org/10.37727/jkdas.2021.23.1.471.

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Merabet, F., H. Zeghdoudi, R. H. Yahia, and I. Saba. "MODELLING OF OIL PRICE VOLATILITY USING ARIMA-GARCH MODELS F. Merabet, H. Zeghdoudi, R. H Yahia, and I. Saba." Advances in Mathematics: Scientific Journal 10, no. 5 (May 4, 2021): 2361–80. http://dx.doi.org/10.37418/amsj.10.5.6.

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In this paper, the behavior of the oil price series named OIL is examined. The non-stationarity on average and variance, with the non-normality of the OIL series distribution, indicate the volatility of the series. The study is based on a combination of the Box-Jenkins methodology with the GARCH processes (Engle and Bollerslev). The first part models the lnOIL series in which, by applying the first difference the series becomes DlnOIL. Then the Box-Jenkins methodology is applied. The choice of the model was made on basis of minimization of criterion -Akaike (AIC), Shwarz (SIC)- and maximization of log likelihood (LL). Of the four models identified, ARMA (3.1) is retained. According to the statistical indicators of the ARMA model (3,1), the nature of the residuals and other tests, it is shown that the series of squares of the residuals follows a conditionally heteroscedastic ARCH model. The second part is devoted to a symmetrical and asymmetrical GARCH modelling. The model used for predicting volatility is the EGARCH model (1,2). The data available relates to 3652 daily values of the change in OIL, from 01/01/2019 to 12/31/2019. The forecast is made for the first three months of 2020; the result concludes that the predicted values and the current values are very close, and that the model ARIMA (3,1,1) + EGARCH (1,2) is the best forecast model.
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Nadhirin, Nur Fathin Shaida Muhammad, Norizarina Ishak, and Siti Masitah Elias. "Performance of Shariah-Compliant Equity Portfolio Using Model-Based Return and Risk Estimation." Journal of Economic Info 7, no. 2 (August 1, 2020): 104–19. http://dx.doi.org/10.31580/jei.v7i2.1439.

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Establishing optimal allocation for different stocks in a portfolio via modern portfolio theory is highly depended on the accuracy of the return and risk estimation. For retail investors, technological advancement has made it possible for them to apply the complex estimation procedure for decision making. Therefore, this study aims to assess the mean-variance Shariah-compliant portfolio performance with model-based return and risk estimation. The methodology adopted is based on the implementation of ARMA and GARCH model, focused on the daily stock prices from year 2011 until 2018. Further, we used one-step ahead forecast for the best ARMA-GARCH model as well as an arithmetic mean and variance estimation to prepare the composition of diversified portfolio weights for top 10 constituent companies listed in FBM Hijrah Shariah (FBMHS) Index. We also measure out of sample performance in a constructed portfolio using Sharpe, Treynor and Jensen’s measures. The result shows that the stock allocation for the model-based portfolio is less diversified as compared to non-model-based portfolio. The composition of the model-based portfolio weight is capable of achieving high annual returns which can compensate for high risk. The out of sample portfolio performance of both techniques is capable to outperform the FBMHS Index.
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Szolgayová, Elena Peksová, Michaela Danačová, Magda Komorniková, and Ján Szolgay. "Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity." Slovak Journal of Civil Engineering 25, no. 2 (June 27, 2017): 39–48. http://dx.doi.org/10.1515/sjce-2017-0011.

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AbstractIt is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-type nonlinear time series models are considered here. The fitting, forecasting and simulation performance of such models have to be explored on a case-by-case basis. The goal of this paper is to test and develop an appropriate methodology for model fitting and forecasting applicable for daily river discharge forecast error data from the GARCH family of time series models. We concentrated on verifying whether the use of a GARCH-type model is suitable for modelling and forecasting a hydrological model error time series on the Hron and Morava Rivers in Slovakia. For this purpose we verified the presence of heteroscedasticity in the simulation error series of the KLN multilinear flow routing model; then we fitted the GARCH-type models to the data and compared their fit with that of an ARMA - type model. We produced one-stepahead forecasts from the fitted models and again provided comparisons of the model’s performance.
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Arisena, Adri, Lienda Noviyanti, and S. Achmad Zanbar. "Portfolio return using Black-litterman single view model with ARMA-GARCH and Treynor Black model." Journal of Physics: Conference Series 974 (March 2018): 012023. http://dx.doi.org/10.1088/1742-6596/974/1/012023.

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Ayele, Amare Wubishet, Emmanuel Gabreyohannes, and Hayimro Edmealem. "Generalized Autoregressive Conditional Heteroskedastic Model to Examine Silver Price Volatility and Its Macroeconomic Determinant in Ethiopia Market." Journal of Probability and Statistics 2020 (May 25, 2020): 1–10. http://dx.doi.org/10.1155/2020/5095181.

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Like most commodities, the price of silver is driven by supply and demand speculation, which makes the price of silver notoriously volatile due to the smaller market, lower market liquidity, and fluctuations in demand between industrial and store value use. The concern of this article was to model and forecast the silver price volatility dynamics on the Ethiopian market using GARCH family models using data from January 1998 to January 2014. The price return series of silver shows the characteristics of financial time series such as leptokurtic distributions and thus can suitably be modeled using GARCH family models. An empirical investigation was conducted to model price volatility using GARCH family models. Among the GARCH family models considered in this study, ARMA (1, 3)-EGARCH (3, 2) model with the normal distributional assumption of residuals was found to be a better fit for price volatility of silver. Among the exogenous variables considered in this study, saving interest rate and general inflation rate have a statistically significant effect on monthly silver price volatility. In the EGARCH (3, 2) volatility model, the asymmetric term was found to be positive and significant. This is an indication that the unanticipated price increase had a greater impact on price volatility than the unanticipated price decrease in silver. Then, concerned stockholders such as portfolio managers, planners, bankers, and investors should intervene and pay due attention to these factors in the formulation of financial and related market policy.
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Jati, Kumara. "ANALISIS EFEK MUSIM HUJAN DAN KEMARAU TERHADAP HARGA BERAS." Jurnal Manajemen Industri dan Logistik 2, no. 1 (December 4, 2018): 40–51. http://dx.doi.org/10.30988/jmil.v2i1.24.

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This study analyzes the effects of the rainy and dry seasons on rice prices. Autoregressive and Moving Average (ARMA) and Autoregressive Conditional Heteroskedasticity / Generalized Autoregressive Conditional Heteroskedasticity (ARCH / GARCH) with a dummy variable. We used daily data of the stock and the price of rice from January 29, 2014 until January 29, 2018. ARMA (0,1)-ARCH (1) model with dummy variable that is dry season is more influence conditional variance of rice price compared with rainy season dummy variable. Stakeholders need to pay more attention to fluctuations in rice prices, especially in the dry season because rice supply is relatively less and there is only puso harvest.
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Tang, Jiechen, Chao Zhou, Xinyu Yuan, and Songsak Sriboonchitta. "Estimating Risk of Natural Gas Portfolios by Using GARCH-EVT-Copula Model." Scientific World Journal 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/125958.

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This paper concentrates on estimating the risk of Title Transfer Facility (TTF) Hub natural gas portfolios by using the GARCH-EVT-copula model. We first use the univariate ARMA-GARCH model to model each natural gas return series. Second, the extreme value distribution (EVT) is fitted to the tails of the residuals to model marginal residual distributions. Third, multivariate Gaussian copula and Studentt-copula are employed to describe the natural gas portfolio risk dependence structure. Finally, we simulate N portfolios and estimate value at risk (VaR) and conditional value at risk (CVaR). Our empirical results show that, for an equally weighted portfolio of five natural gases, the VaR and CVaR values obtained from the Studentt-copula are larger than those obtained from the Gaussian copula. Moreover, when minimizing the portfolio risk, the optimal natural gas portfolio weights are found to be similar across the multivariate Gaussian copula and Studentt-copula and different confidence levels.
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